A Molecular Phylogenetic Study and the Use of Dna Barcoding

A MOLECULAR PHYLOGENETIC STUDY AND THE USE OF DNA BARCODING

TO DETERMINE ITS EFFICACY FOR IDENTIFICATION OF ECONOMICALLY

IMPORTANT SCALE INSECTS (HEMIPTERA: COCCOIDEA) OF SOUTH

AFRICA

MAMADI THERESA SETHUSA

THESIS

submitted in fulfillment of the requirements for the degree

PHILOSOPHIAE DOCTOR

ZOOLOGY

in the

Faculty of Science

at the

University of Johannesburg

Supervisor: Prof Herman van der Bank Co-supervisor: Prof Michelle van der Bank Co-supervisor: Mr. Ian Millar

February 2014

I declare that this work hereby submitted to the University of Johannesburg for the degree

Philosophiae Doctor in Zoology has not been previously submitted by me for a degree either at this or any other university, and that all materials contained therein have been duly acknowledged.

------

M. T. Sethusa (February 2014)

TABLE OF CONTENTS

INDEX OF TABLES v

INDEX OF FIGURES vi

INDEX OF APPENDICES x

ABSTRACT xi

FORWARD xiv

ACKNOWLEDGEMENT xv

LIST OF ABBREVIATIONS xvii

CHAPTER1: 1 GENERAL INTRODUCTION AND OBJECTIVES

1.1 GENERAL INTRODUCTION 1

1.2 DIVERSITY AND RECOGNITION OF GROUPINGS OF SCALE 5 INSECT

1.3 RELATIONSHIPS AMONG THE COCCOIDEA FAMILLIES 6

1.4 DISPERSAL AND ADAPTATION TO HOST PLANTS 7

1.5 DNA BARCORDING AS A TOOL IN SPECIES IDENTIFICATION 13 AND PHYLOGENY RECONSTRUCTION

1.6 OBJECTIVES OF THE STUDY 18

1.7 HYPOTHESES OF THE STUDY 18

CHAPTER 2: 20 DNA BARCODING EFFICACY FOR THE IDENTIFICATION OF ECONOMICALLY IMPORTANT SCALE INSECTS (HEMIPTERA: COCCOIDEA) IN SOUTH AFRICA

2.1 INTRODUCTION 20

2.2 MATERIALS AND METHODS 22

2.2.1 Sample collection 22

2.2.2 DNA extraction and slide preparation 22

2.2.3 Amplification, sequencing and alignment of DNA barcodes 23

2.2.4 Data analysis 25

2.3 RESULTS 34

2.4 DISCUSSION 62

CHAPTER 3: 65 A MOLECULAR PHYLOGENETIC STUDY OF SOUTH AFRICAN SCALE INSECTS (HEMIPTERA: COCCOIDEA)

3.1 INTRODUCTION 65

3.2 MATERIALS AND METHODS 68

3.2.1 Taxon sampling 68

3.2.2 Molecular data 71

3.2.3 Phylogenetic analysis 71

3.3 RESULTS 73

3.3.1 Molecular evolution 73

3.4 DISCUSSION 81

CHAPTER 4: 87 THE EFFECT OF CLIMATE CHANGE ON SCALE INSECT DISTRIBUTION PATTERNS IN SOUTH AFRICA

4.1 INTRODUCTION 87

4.2 MATERIALS AND METHODS 94

4.2.1 Study area and occurrence data 94

4.2.2 Climate data 99

4.2.3 Determination of suitable habitat 100

iii

4.3 RESULTS 102

4.3.1 Model test 102

4.3.2 Potential distribution as computed under current and future 107 climate conditions

4.4 DISCUSSION 116

CHAPTER 5: 124 SUMMARY AND FUTURE RESEARCH

CHAPTER 6: 130 REFERENCES

APPENDICES 161

INDEX OF TABLES

CHAPTER 1

Table 1.1 Economically important scale insect families in South Africa. 10

CHAPTER 2

Table 2.1 List of Coccoidea species studied, host plants, collecting localities 28 and number of DNA sequences generated per gene region. All samples were collected in South Africa, except where indicated with an asterisk (*).

Table 2.2 Summary of Meier’s close match test, BOLD threshold ID test and 37 the near neighbour method for all genetic markers tested. “No ID” indicates the percentages of individuals that could not be identified.

CHAPTER 3

Table 3.1 Collected species, host plants, collection localities and GenBank 69 accession numbers, where n/a is sequences not available and (-) are those whose GenBank number are still being generated.

Table 3.2 Statistics from MP analyses for the individual partitions and the 75 combined three-region data set.

CHAPTER 4

Table 4.1 Species used for Species Distibution Modelling and their family 95 classification. (Appendix 4.1 gives more details of the collection).

Table 4.2 Bioclimatic variables used as predictors in Maximum Entropy 100 modeling of species geographic distribution.

Table 4.3 The percentage contribution of environmental variables in 105 predicting geographic distribution models. Each variable was tested independently on individual species at a time.

INDEX OF FIGURES

CHAPTER 1

Figure 1.1 Asterolecanium quercicola on Quercus robus as typical plant acne 2 (Photo by I Millar).

Figure 1.2 Aonidiella aurantii colony on Rosa species showing different life 2 stages (Photo by I Millar).

Figure 1.3 Schematic representation of the life cycle of scale insects e.g. gum 4 tree scale (information from Philips 1992).

Figure 1.4 Coccus ehretiae on Gymnosporia species, with ants attracted by 9 secreted honeydew (Photo by I Millar).

Figure 1.5 Slide mounted Ceroplastes species collected from Citrus sinensis, 14 with a close up on the taxonomically important features (spiracles, stigmatic farrow and stigmatic setae; Photo by M T Sethusa).

Figure 1.6 Slide mounted Tachadiana species, collected from Acacia tortilis 14 (Photo by M T Sethusa).

Figure 1.7 Major components of the Barcode of Life projects and their 16 contribution to taxonomy, reconstruction of molecular phylogenies and population genetics investigations (Hajibabaei et al. 2007).

CHAPTER 2

Figure 2.1 Summary of PCR success rate (%) of 18S, 28S and CO1 across 10 35 scale insect families, with no bar areas represent no/unsuccessful PCR results. The femilies Lecanodiaspidae and Eriococcidae were not tested for CO1.

Figure 2.2 Interspecific vs. intraspecific distances showing a barcode gap for 40 individual markers 18S, 28S and CO1.

Figure 2.3 Interspecific vs. intraspecific distances showing a barcode gap for 40 combined markers 18S&28S, 18S&CO1 and 28S&CO1.

Figure 2.4 28S neighbour-joining tree of K2P distances with 1 000 replicates 45 bootstrap support.

Figure 2.5 CO1 neighbour-joining tree of K2P distances with 1 000 replicates 52 bootstrap support.

Figure 2.6 18S neighbour-joining tree of K2P distances with 1 000 replicates 58 bootstrap support.

Figure 2.7 18S&28S neighbour-joining tree of K2P distances with 1 000 59 replicates bootstrap support.

Figure 2.8 18S&CO1 neighbour-joining tree of K2P distances with 1 000 60 replicates bootstrap support.

Figure 2.9 28S&CO1 neighbour-joining tree of K2P distances with 1 000 61 replicates bootstrap support.

CHAPTER 3

Figure 3.1 Nuclear analysis showing relationships between and within scale 76 insect families with MP bootstrap and BI posterior probability support (BS/PP) . Figure 3.2 Mitochondrial analysis showing relationships between and within 77 scale insect families with MP bootstrap and BI posterior probability support (BS/PP).

Figure 3.3 Combined analysis showing relationships between and within scale 78 insect families with MP bootstrap and BI posterior probability support (BS/PP), and congruency indicated by green circles while red circles below the branches indicate conflict.

Figure 3.4 Combined analysis showing the represented sub-families and tribes, 80 with species and tribal splits, and unexpected groupings highlighted in red, ungrouped African taxa indicated by a pink circle and MP bootstrap and BI probability support (BS/PP) on the branches.

CHAPTER 4

Figure 4.1A Map of the study area showing localities where scale insects of the 96 family Coccidae were collected in the different agricultural regions in South Africa (Layer source: SANBI - Biodiversity and monitoring division).

Figure 4.1B Map of the study area showing localities where scale insects of the 96 family Diaspididae were collected in the different agricultural regions in South Africa (Layer source: SANBI - Biodiversity and monitoring division).

Figure 4.1C Map of the study area showing localities where scale insects of the 97 family Margarodidae were collected in the different agricultural

vii

regions in South Africa (Layer source: SANBI - Biodiversity and monitoring division).

Figure 4.1D Map of the study area showing localities where scale insects of the 97 family Ortheziidae were collected in the different agricultural regions in South Africa (Layer source: SANBI - Biodiversity and monitoring division).

Figure 4.1E Map of the study area showing localities where scale insects of the 98 family Pseudococcidae were collected in the different agricultural regions in South Africa (Layer source: SANBI - Biodiversity and monitoring division).

Figure 4.1F Map of the study area showing localities where all scale insects 98 were collected (Layer source: SANBI - Biodiversity and monitoring division).

Figure 4.2 Model performance results showing examples of “very good” (A), 104 “good” (B), “useful” (C) and “unuseful” (D) performance. Complete results are in Appendix 4.2.

Figure 4.3 Combined map of scale insects of the family Coccidae in South 108 Africa showing (A): current projected distribution and (B): future- current (2080) estimated change in distribution of scale insects of the family Coccidae with red showing areas more vulnerable, baby blue to yellow moderately vulnerable and dark blue areas less vulnerable to Coccidae infestation using the raw probability values from MaxEnt.

Figure 4.4 Combined map of scale insects of the family Diaspididae in South 109 Africa showing (A): current projected distribution and (B): future- current (2080) estimated change in distribution of scale insects of the family Diaspididae with red showing areas more vulnerable, baby blue to yellow moderately vulnerable and dark blue areas less vulnerable to Diaspididae infestation using the raw probability values from MaxEnt.

Figure 4.5 Map of scale insects of the family Margarodidae (represented by 111 Icerya seycherlarum) in South Africa showing (A): current projected distribution and (B): future-current (2080) estimated change in distribution of scale insects of the family Margarodidae with red showing areas more vulnerable, baby blue to yellow moderately vulnerable and dark blue areas less vulnerable to Margarodidae infestation using the raw probability values from MaxEnt.

Figure 4.6 A Map of scale insects of the family Ortheziidae (represented by 112 Orthezia insignis) in South Africa showing (A): current projected distribution and (B): future-current (2080) estimated change in distribution of scale insects of the family Ortheziidae with red

viii

showing areas more vulnerable, baby blue to yellow moderately vulnerable and dark blue areas less vulnerable to Ortheziidae infestation using the raw probability values from MaxEnt.

Figure 4.7 Combined map of scale insects of the family Pseudococcidae in 114 South Africa showing (A): current projected distribution and (B): future-current (2080) estimated change in distribution of scale insects of the family Pseudococcidae with red showing areas more vulnerable, baby blue to yellow moderately vulnerable and dark blue areas less vulnerable to scale insect infestation using the raw probability values from MaxEnt.

Figure 4.8 Combined map of scale insects in South Africa showing (A): 115 current projected distribution and (B): future-current (2080) estimated change in distribution of scale insects (combined) with red showing areas more vulnerable, baby blue to yellow moderately vulnerable and dark blue areas less vulnerable to scale insect infestation using the raw probability values from MaxEnt.

INDEX OF APPENDICES

CHAPTER 2

Appendix 2.1 Voucher information and GenBank accession numbers of 18S, 28S 161 and CO1 sequences generated in this study. The voucher specimens are deposited in the SANC of insects hosted at the ARC-PPRI Bioystematics division.

CHAPTER 4

Appendix 4.1 List of Coccoidea species studied, collecting localities and voucher 172 numbers.

Appendix 4.2 Model performance results. 207

ABSTRACT

Scale insects, plant pests of quarantine importance, with specialised anatomy and unresolved phylogenetic relationships, are responsible for major economic losses to South Africa and its trading partners. These losses may reach critical levels if the pests are not timely identified and controlled. They are currently identified based on published keys of adult females, a process that takes three days to two weeks depending on the family and the life stage of interception. In addition, agricultural commodities are often contaminated with different life stages, males or damaged specimen of these pests, making identification difficult or impossible. As a result, shipments of agricultural produce are often rejected and trade disrupted. Furthermore, pest invasions do not only occur by importation via formal channels.

At times pests cross boarders as contaminants of undeclared material and may again spread on their own as they naturally expand their range. This expansion may be negatively or positively influenced by other factors such as climate change. Resolving the challenges associated with identification, phylogenetic relationships and the limited knowledge of the effects of climate change on distribution range of scale insects are the main goals of this study. Specifically (i) the development of a rapid method of species identification, (ii) the relationship between and within three major scale insect families the Coccidae, Diaspididae and Pseudococcidae and (iii) the effect of climate change on the future distribution range of scale insects in South Africa were explored.

For species identification, DNA barcoding, a molecular identification methods based on a standardised region CO1, has proved to give a more rapid and sensitive option. CO1 however, is not effective for all animal groups including scale insects. The potential of the additional markers 18S and 28S to improve on the sensitivity of CO1 were therefore

xi explored. Using the Near Neighbour Method, results showed sensitivity to improve from

72.7% using CO1 alone to 91.5% using a combination of CO1 and 28S. Based on these results, the addition of 28S as a supplementary marker in scale insect identification is therefore recommended.

Exploring the relationships between and within the three major scale insect families, a concatenated approach using the nuclear ribosomal genes 18S and 28S, and the mitochondrial cytochrome oxidase 1 (CO1) for phylogeny reconstruction was employed.

Firstly, a congruency test between the mitochondrial data (CO1) and the nuclear data (18S and 28S) was conducted, and based on the congruency results, phylogenetic analysis were carried out using Maximum Pasimony (MP) and Bayesian Inference (BI) methods. Results obtained from the current study were congruent with classical scale insect classifications, with the families predominantly exclusive of one another and Pseudococcidae sister to the

Coccidae and Diaspididae. The relationship within the families is however is unresolved.

Further molecular and morphological studies, with a more comprehensive sampling are thus recommended.

For current and future projections of scale insect geographic patterns, a species distribution model using MaxEnt was employed. The computed current climate is similar with scale insect occurrence records indicating the tested variables to be appropriate to predict scale insect biogeographic range. Potential distribution under future climate predicts a north-westerly shift, with the currently unsuitable Northern Cape and Free State provinces becoming more suitable and the Western Cape becoming less suitable for scale insects by

2080. It is therefore recommended that strong regulations be put in place for transportation of plants and plant produce into uninfested areas that are currently suitable or are projected to be

xii suitable under future climatic conditions to avoid management and eradication costs.

Furthermore, a study on interactions of scale insects with other species and the effect of transportation modes in addition to current and future bioclimatic envelope is recommended, as this might shed more light into future scale insect range.

xiii

FOREWORD

This is a comprehensive study of economically important scale insects focusing on their identification, relationships and distribution pattern in relation to climate change. It is the first study to evaluate scale insect identification efficacy in a broad scale (across 10 famillies) in terms of turnaround time, accuracy, and reliability and across family application without modifications. The study evaluated the use of a secondary DNA barcode 28S in addition to the universal CO1 and increased the efficacy to 91.5%. The DNA extraction method used was non-destructive, enabling retrieval of intact insects for vouchering. All voucher specimens are deposited in the scale insect collections of the South African National

Collection of Insects. The study is also the first to look into distribution of scale insects in

South Africa and climatic factors influencing the distribution pattern. The DNA sequence database produced from this study is the first essential step in implementing a quarantine inspection service required in international trade.

The proposal and results of this study were presented at one international conference

(International Conference on Insect Science, 2013 in India), three national conferences

(South African Society for Systematic Biology conference, 2011; Entomological Society of

South Africa conference, 2011 and Die Suid-Afrikanse Akademie vir Wetenskap en Kuns,

Afdeling Biologie, 2012) and several University of Johannesburg colloquiums. An article on barcoding has been accepted for publication and will appear in journal of African

Entomology Vol. 22(1), which is scheduled to be published in March 2014. A second article on the effect of climate change on scale insects distribution patterns is currently being prepared for publication.

xiv

ACKNOWLEDGMENTS

My heartfelt appreciation goes to Prof. Herman van der Bank and Prof. Michelle van der

Bank for their supervision. Special thanks go to Mr. Ian Millar for his mentorship, support and motivation, without him this work would not have been possible.

My sincere gratitude goes to Dr Kowiyou Yessoufou of the African Centre for DNA

Barcoding, University of Johannesburg, Dr Damaries Odeny of the Agricultural Research

Council, Bioplatform, and Dr Danni Guo of the South African National Biodiversity Institute,

Climate Change and Bioadaptation Division for their enormous support, advice and time throughout the study.

Thanks go to my colleagues Ledile Mankga, Barnabus Daru and Bezeng Simmy for their help, suggestions and correction in the different aspects of this study.

I gratefully acknowledge the discussions I had with Prof. Tshepho Matjila, Dr

Andrianna Jacobs-Venter, Dr Vangile Mkhatswa and Mr Bright Laaka.

I thank the following institutions for their financial and infrastructure support throughout the study: the Department of Agriculture, Forestry and Fisheries, Agricultural

Research Council-Plant Protection Research Institute, the Consortium for Barcoding of Life and the University of Johannesburg.

To my ever-supportive family, thank you for believing in me and always encouraging me in all my endeavors. Ndiyalithada bokxe, dilelebogha kxulukxulu Bathetheya, Somandla abe nale.

Lastly I thank God almighty, for giving me strength and shining light through all the dark patches I had to go through in the duration of this study. Ndiyalebogha Somandla.

xvi

LIST OF ABBREVIATIONS

AIC = Akaike Information Criteria

ARC-PPRI = Agricultural Research Council- Plant Protection Research Institude

BI = Bayesian Inference

BLAST = Basic local alignment search tool

BOLD = Barcoding of Life Database bp = base pair

BS = Bootstrap Support

CI = Consistency Index

CO1 = Cytochrome oxidase 1

CO2 = Carbon dioxide

CSIRO- MK3.0 = Commonwealth scientific and industrial research organization- Mark 3.0

DAFF = Department of Agriculture, Forestry and Fisheries

DNA = Deoxyribonucleic acid e.g. = Exempli gratia (for example)

GCM = General circulation model

GDP = Gross domestic product

GenBank = National Centre for Biotechnology Information

GHG = Green House Gases

GLRa V-3 = Grapevine leafroll associated virus 3

GTR+ I+ G = General Time Reversible + Gamma+ Propotion invariant

HI = Homoplasy Index i.e. = Id est (that is)

IPCC = Intergovernmental Panel on Climate Change

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IPM = Intergrated pest management

ISSR = Inter-simple sequence repeat

K2P = Kimura 2 parameter km = Kilo meter mm = Milli meter

MP = Maximum Parsimony

MtDNA = Mitochondrial Deoxyribonucleic acid

MulTrees = Multiple equally parsimonious trees

NJ = Neighbor-joining oC = Degree Celsius

PGDP = Provincial growth and development plan

PGE = Paternal Genome Elimination

PP = Posterior Probabilities

PRA = Pest Risk Assessment

RI = Retention Index

RNA = Ribonucleic acid

SANC = South African National Collection

SDM = Species Distribution Modelling

SIBI = Scale Insect Barcoding Initiative sp. = Species

SPIDER = Species Identity and Evolution in R

SRES A1B = Special report emissions scenarios balanced across energy sources

STATSSA = Statistics South Africa

TBR = Tree- Bisection- Reconstruction

xviii

CHAPTER 1

GENERAL INTRODUCTION AND OBJECTIVES

1.1 GENERAL INTRODUCTION

Scale insects (Hemiptera: Sternorrhyncha: Coccoidea) are sap sucking plant parasites, which are small (typically less than 5mm) and cryptic in habit, often resembling their host plant

(Gullan & Kosztarab 1997), and are sometimes referred to as plant acne, see Figure 1.1. Their common name derives from the frequent presence of a protective covering, the “scale”, or from the appearance of the insects themselves. The scale of the adult female of armoured scales (Diaspididae), e.g. Figure 1.2, is a unique entomological example of conservation of resources, since it is formed by secreted wax filaments, cemented by anal excretions and embedded with the exuviae of the two preceding nymphal stages (Stoetzel 1976, Foldi 1990).

Most Coccoidea produce waxy secretions, depending on the type and amount, the formed scale protects the insect and its eggs from water loss, wet conditions, attack from natural enemies including pathogens and contamination by honeydew (Gullan & Kosztarab 1997). It is believed that the scale also protects the insects against pesticides (Amitai 1992), therefore efficient chemical control requires the application of pesticides before the scale or test is formed.

Figure 1.1: Asterolecanium quercicola on Quercus robus as typical plant acne (Photo by I

Millar).

Figure 1.2: Aonidiella aurantii colony on Rosa species showing different life stages i.e. eggs, crawlers and adults (Photo by I Millar).

Scale insects are morphologically very distinct from other insect taxonomic groups.

The structural peculiarities can be explained by ancestral adaptations for reproduction and surviving adverse environmental conditions (Gullan & Kosztarab 1997). The group is also characterised by a sexual dimorphism. For instance, adult females covered by scales are paedomophic, perhaps due to neoteny, adults retaining immature external morphology at sexual maturity, whereas adult males display a complete metamorphosis as illustrated in

Figure 1.3, and are sometimes mistaken for small flies like gnats and midges. Adult males that need to copulate with concealed females have an elongated aedeagus, the reproductive male organ, which is often up to half their body length (Kosztarab 1982), and unusual bundles of 16-64 individual sperms surrounded by a sheath are released at a time (Jamieson

1987). The sperm bundles usually become motile after they have entered the female body and may be stored for up to three months before they rupture and release individual sperms

(Jamieson 1987). This pattern is necessary where males mate with teneral females, recently emerged adult females whose bodies have not fully matured, and fertilisation needs to be delayed for weeks or months until maturity (Gullan & Kosztarab 1997). Moreover, the adult males lack functional mouthparts, are non-feeding and thus short-lived. Females however, feed by inserting their piercing mouthpieces (stylets) into the host plant to suck sap from the phloem, the living tissue that carries organic nutrients (Gullan & Kosztarab 1997). The feeding stylets of scale insects vary in length, even among closely related taxa, and their size does not appear to correlate with feeding habits (Koteja 1990). Scale insect diet is high in carbohydrates but low in other nutrients, especially amino acids. Like other members of the

Hemiptera suborder Sternorrhyncha, scale insects harbor vertically transmitted endosymbionts that are believed to help synthesise amino acids missing from their diet

(Buchner 1965, Gruwell et al. 2005, 2007).

Figure 1.3: Schematic representation of the life cycle of scale insects e.g. gum tree scale

(Information from Philips 1992).

The Coccoidea (scale insects and mealybugs) is one of the four superfamilies of the monophyletic suborder Sternorrhyncha within the Hemiptera (Schaefer 1996, Bourgoin &

Campbell 2002, Gullan & Martin 2003). They are more diverse in terms of morphology, species richness and major evolutionary lineage than the other Sternorrhyncha groups, namely aphids (Aphidoidea), jumping plant lice (Psylloidea) and whiteflies (Aleyrodoidea)

(Schaefer 1996, Bourgoin & Campbell 2002, Gullan & Martin 2003). Coccoidea is the undisputed sister group of Aphidoidea (Sorenson et al. 1995, von Dohlen & Moran 1995,

Bourgoin & Campbell 2002). Aphidoidea fossils of several extinct families are well documented from as far back as the Jurassic and Triassic (Shcherbakov & Wegierek 1991,

Heie 1999, Grimaldi & Engel 2005) but in general only three extant families, the Aphididae, the Adelgidae and the Phylloxeridae are recognised (Remaudiére & Remaudiére 1997,

Blackman & Eastop 2000, von Dohlen & Moran 2000, Havill et al. 2007). In contrast, scale

4 insects are divided into either 22 (Ben-Dov et al. 2006) or approximately 33 extant families

(Hodgson & Hardy 2013). Even though the scale insects must be as old as their sister group, the Aphidoidea, they suddenly appear in the fossil records in the early Cretaceous as an abundant, diverse and specialised group without any “ancestral” forms (Koteja 2001). This auther attributed the dearth of pre-cretaceous fossils to either ecological or preservation conditions or technical failure to recognise scale insect fossil impressions during sorting.

Scale insects diversity is exhibited by a number of peculiar structural and behavioral traits subsequently used in high level groupings.

1.2 DIVERSITY AND RECOGNITION OF GROUPINGS OF SCALE INSECTS

Coccoidea are diverse in terms of chromosome number (Nur et al. 1987), sperm structure

(Robinson 1977, 1990), types of bacterial endosymbioses (Buchner 1965, Gruwell et al.

2005, 2007) and genetic systems, including hermaphroditisms, diplodiploidy (diploid males), thelytoky (unfertilised eggs developing into females) and haplodiploidy (unfertilised eggs developing into males) (Nur 1980, Normark 2003). The estimated 8 000 species of scale insects are traditionally divided into two informal groups, sometimes treated as superfamilies,

Archaecocciods and Neococciodes (Borchsenius 1950, Miller 1984, Danzig 1996, Williams

2013). The Archaecoccoids comprise the Margarodidae sensu lato (Morrison 1925), the

Carayonemidae (Kozar & Konczne-Benedicty 2000) and sometimes the Phaenacoleachiidae

(Danzig 1980, Koteja 1996). However, most of the morphological characters that define the

Archaecoccoids namely abdominal spiracles, compound eyes in males, and an XX-XO chromosome system are plesiomorphies that occur widely in the Hemiptera and thus paraphyly of Archacoccoids is likely (e.g. Foldi 1997). A study by Cook et al. (2002), based on nuclear small-subunit ribosomal DNA, indicated that the relationship between

Archaecoccoids is unresolved.

The Neococciods comprise all of the other families, it include most of the species of scale insects, and is treated currently as a monophyletic group. It has been defined by synapomorphies such as a chromosome system involving Paternal Genome Elimination

(PGE) (Danzig 1980, Nur 1980), needle like apical setae on the labrum (Koteja 1974C,

1996), shared structural and developmental features of the ovaries (Szklarzewicz 1998) and the absence of abdominal spiracles (Morrison 1928). Among the Neococcoids sensu lato, only Puto (XX-XO), which is sometimes considered a member of the Pseudococcidae (e.g.

Danzig 1980, Miller & Miller 1993), the Eriococcidae genus Lachnodius (2N-2N) and the

Stictococcidae (2N-2N) do not exhibit chromosome systems with PGE (Nur 1980). In the

PGE system, both male and female zygotes are diploid and there are no sex chromosomes, instead the paternally inherited haploid set of chromosomes becomes heterochromatic or is lost in males during early embryogenesis (Cook et al. 2002). Traits like those mentioned above are important in inferring relationships between species, genera, families etc.

1.3 RELATIONSHIPS AMONG THE COCCOIDEA FAMILIES

Phylogenetic relationships among scale insect families are largely unresolved or not well supported by morphological data (Foldi 1997, Gullan & Kosztarab 1997). Individual families are often well characterised by autapomorphies, but there is controversy concerning the monophyly of some of the traditionally recognised families such as the Margarodidae sensu lato (Miller 1984, Foldi 1997, Gullan & Sjaarda 2001) and the Eriococcdae (Cox & Williams

1987, Miller & Gimpel 2008), which are defined mostly by symplesiomorphic characters.

Cladistic studies either have had limited taxonomic scope (Miller 1984, Miller & Hodgson

1997) or have not examined node support in a critical manner (Foldi 1997). This lack of reliable phylogenetic estimate is due primarily to the much reduced and highly modified

6 morphologies of adult female scale insects, which lead to there being few phylogenetically informative features that can be scored for taxa across all recognised families. Moreover, environmental factors i.e. average daily temperature, influences morphological features used in identification (Cox 1983), thus warm temperature variants of a species may be assumed to be a different species (Cox & Wetton 1998). This is a major concern since scale insects have been collected in different regions with varying environmental conditions across the word.

Moreover, when introduced as plant contaminants, scale insects can expand their range at an alarming rate using different dispersal mechanisms, evolve and adapt to survive in the newly infested habitat (Cox & Wetton 1998, Walton & Pringle 2004).

1.4 DISPERSAL AND ADAPTATION TO HOST PLANTS

First-instar nymphs are the main dispersal agents in Coccoidea since adult females are usually stationary (sedentary) and spend their entire life on a single host plant and oviposit there (Gullan & Kosztarab 1997). Newly emerged first instar nymphs or crawlers, though highly motile, are often carried passively by the wind for a few meters to several kilometers and more rarely hundreds of kilometers from the natal trees (Hanks & Denno 1993). Their flattened bodies and two or more long filamentous caudal setae reduce their fall speed, thus enhancing their dispersal potential (Wainhouse 1980, Pedgley 1982). Wind dispersal and crawling are not the only dispersal options available to scale insects. The crawlers of some mealybugs are carried to new feeding sites by colony-founding queen ants attracted by honeydew excreted as a byproduct from the high sugar diet consumed, as illustrated in Figure

1.4 (Buschinger et al. 1987, Kosztarab 1987). Scale insects are more likely than many other phytophagous insects to show restricted geneflow between populations on different long- lived hosts, since the sedentary females usually spend their entire life on a single host species and the weak short-lived males mate with local females. This behavioral pattern may

7 promote the formation of genetically distinct demes i.e. each scale insect population adapting to the features of its host plant, as proposed by Edmunds & Alstad (1978). Hanks & Denno

(1993) however, suggested that demic adaptation has little effect on the colonisation and spread of scale insect populations within a habitat, because it has only been detected when individually infected hosts are far enough apart. They concluded that colonisation on a new host is more influenced by restricted capacity to disperse, combined with variation in host suitability, than by specialisation to host-plant genotype. Relative to most other insect groups, a high percentage of scale insects have been moved around the world by humans and many of these are important economically as pests of agriculture, horticulture and forestry (e.g. Miller

& Davidson 1990, Miller et al. 2005B), as listed in Table 1.1 below. Thus accurate and timeous identification is essential. Traditional identification of scale insects is based on the morphology of the adult female, a process that takes up to two weeks depending on the family (Personal communication, I Millar). Moreover, if only instars or males are collected, identification may be difficult. Because of challenges in identification, i.e. highly modified morphologies of adult females and thus few phylogenetically informative features that may be scored across families, scale insects relationships are unresolved. Therefore, it is necessary for alternative and/or supplementary identification method, e.g. DNA barcoding, to be investigated.

Figure 1.4: Coccus ehretiae on Gymnosporia species, with ants attracted by secreted honeydew (Photo by I Millar).

Table 1.1: Economically important scale insect families in South Africa

Family Common name Common Host Economic impact Constitution Reference

Asterolecaniidae Pit scales 1. Bamboo trees 1. Plant pests 229 species Russell 1941, Boisduval 1869, Hamon 1980,

-Pit formation 21 genera Ben-Dov et al. 2006

-Honeydew production

-Sap removal

Cerococcidae Superficial Pit scales 1. Woody shrubs and 1. Plant pests 72 species Miller et al. 2005B, Ben-Dov et al. 2006

trees -Honeydew production 3 genera

Coccidae Soft scales 1. Woody shrubs and 1. Quarantine plant pests 1133 species Miller et al. 2005A, Ben-Dov 1993, Ben-Dov et

trees -Honeydew production 163 genera al. 2006

-Sap removal

Conchaspididae False armoured scales 1. Vanilla frangrens 1. Plant pests 29 species Mamet 1954, Richard et al. 2003, Le Roux et al.

2. Phylica species -Sap removal 4 genera 2005, Ben-Dov et al. 2006

3. Metalasia muricata

Dactylopiidae Conchineal insects 1. Weeds 1. Biocontrol agent (eg. Opuntia 10 species Pérez Guera & Kosztarab 1992, Ben-Dov et al.

species) 1 genus 2006

Diaspididae Armoured scales 1. Polyphagous 1. Quarantine agricultural pests 2383 species McClure 1990, Ben-Dov et al. 2006

-Fruit discoloration 371 genera

-Plant distortion

Family Common name Common Host Economic impact Constitution Reference

-Premature leaf loss

Eriococcidae Felt scales 1. Woody and 1. Plant pests 5516 species Ben-Dov et al. 2006

herbaceous plants -Honeydew production 68 genera

(including grasses) -Sap removal

-Plant debilitation and death

Kerridae Lac scales 1. Crop and forest 1. Shellac production 98 species Kondo & Gullan 2005, Ben-Dov et al. 2006

plants 2. Plant pests 9 genera

-Sap removal

Lecanodiaspididae False pit scales Non recorded 1. Plant pests 82 species Ben-Dov et al. 2006

-Honeydew production 12 genera

Margarodidae Ground pearls 1. Grape vines 1. Production of Pearls 439 species de Klerk 1985, Ben-Dov 2005, Ben-Dov et al.

2. Sugarcain 2. Plant pest 70 genera 2006

3. Oil palms -Sap removal

4. Cotton

5.Lawn grasses

Ortheziidae Ensign coccids 1. Green house plants 1. Biocontrol agents for Lantana 196 species Williams & Watson 1990, Ben-Dov et al. 2006

camara 20 genera

2. Green house plants pests

-Leaf curl and premature leaf

drop

Family Common name Common Host Economic impact Constitution Reference

-Stunted growth of shoot apex

Pseudococcidae Mealybugs 1. Agricultural plants 1. Plant pests 2194 species Ben-Dov et al. 2006

-Sap removal 277 genera

-Honeydew production

-Toxin injection

-Plant viral vector

1.5 DNA BARCODING AS A TOOL IN SPECIES IDENTIFICATION AND PHYLOGENY

RECONSTRUCTION

Adult females generally have more species-specific features (Figures 1.5 and 1.6) than nymphs and are encountered more frequently than nymphs and adult males, as a result, the morphology of the adult female has been used in species identification, and subsequently genera and higher taxa classification (Morrison 1925, Koteja 1974C, Gullan & Kosztarab 1997). Identification of some female scale insects however, i.e. mealybugs, is notoriously difficult, time consuming and require a high level of taxonomic expertise, mainly because environmental factors affect morphological characters often used for identification (Cox 1983). For instance, in a study conducted by Cox & Wetton (1998), no single diagnostic feature could be used to distinguish between Planococcus ficus and P. halli, raising the necessity of a search for improved identification tools (e.g. DNA barcoding technique) in addition to morphology.

Figure 1.5: Slide mounted Ceroplastes species collected from Citrus sinensis, with a close up on the taxonomically important features (spiracles, stigmatic farrow and stigmatic setae; Photos by

M T Sethusa).

Figure 1.6: Slide mounted Tachadiana species, collected from Acacia tortilis (Photo by M T

Sethusa).

Species identification through barcoding is usually achieved by retrieving a short sequence, the “barcode”, from a standard region of the genome of the specimens under investigation (Hebert et al. 2003). The barcode sequence from each unknown specimen is then compared with a library of reference barcode sequences derived from individuals of known identity. A specimen is identified if the sequence matches one in the barcode library. Otherwise the new record can lead to a novel barcode sequence for a given species; it can suggest the existence of a newly encountered or overlooked species or indicate contamination. Because

DNA barcodes are used both to identify species and to draw attention to overlooked and new species (Figure 1.7), they can help identify candidates’ exemplar taxa for a comprehensive phylogenetic study. Consequently, phylogenies that are constructed using barcode libraries together with morphological knowledge of a given taxonomic group are less likely to be influenced by insufficient taxon sampling (Hajibabaei et al. 2007). Additionally barcoding aids in pinpointing and subsequent replacement of taxa with attributes, such as exceptionally elevated rates of evolution or nucleotide composition bias (Felsenstein 2004) that can mislead the recovery of phylogenetic trees. Barcode sequence data can also provide a shared genomic cornerstone for variable repertoire of genes that can be used to build the phylogenetic tree. It can be used as a link between the deeper branches of the tree to its shallow species level branches.

The use of DNA barcodes for accurate and timeous identification is even more important during international tradings for countries like South Africa where millions of Rands are generated annually in fresh produce trading and biosecurity is of utmost importance. Despite the above mentioned advantages, DNA barcoding has been virtually unused in scale insects identification, except those by Ball & Armstrong (2007) in sooty beach scales, Park et al. 2011 in mealybugs and armored scales, and Deng et al. 2012 in wax scales.

Figure 1.7: Major components of the Barcode of Life projects and their contribution to taxonomy, reconstruction of molecular phylogenies and population genetics investigations

(Hajibabaei et al. 2007).

The currently accepted DNA barcode for the animal kingdom Cytochrome Oxidase 1

(CO1), has proved reliable in distinguishing between species (Hebert et al. 2004), however its universality remains challenged as it has proved controversial for several groups (e.g.

Aphidiinae; Stephane et al. 2012), including scale insects (Park et al. 2010). Moreover, recent studies that focused on this group have revealed limitations in the currently developed primers.

For instance, a combination of the newly developed forward primer (PcoF1) with the standard reverse primer (LepR1) showed a success rate of only 58% for armored scales and 76% for mealybugs (Park et al. 2011). The pitfalls linked to traditional identification of scale insects, combined with current possible non-universality of CO1 point out the need to search for

16 additional markers. Deng et al. (2012) showed the nuclear 28S to have discriminative potential in scale insects. Their work however, only focused on the six wax scales Ceroplastes floridensis, C. japonicas, C. ceriferus, C. pseudoceriferus, C. rubens and C. kunmingensis. There is therefore a need to investigate the performance of 28S on a broader scale and the possibility of an increase in efficacy of DNA barcoding identification in scale insects with the addition of 28S as a secondary marker to CO1. Since the nuclear 18S is one of the most important molecular markers used in diverse applications like molecular phylogenetic studies and biodiversity screening

(Chenuil 2006), it could as well increase the efficacy of DNA barcoding in scale insects identification, and should therefore be tested.

Because of the pest status of scale insect (Table 1.1), in addition to accurate identification, it is crucial to contain, control and prevent infestations. It would therefore be beneficial for biosecurity officials to know the current distribution of scale insect and have a hypothetical or projected habitat range shift for future infestations. This will enable proactive action to avoid costs associated with control and eradication. Species range shift could be influenced by a number of factors including land use changes, tourism, introduction of scale insects as bio-control agents for invasive plants and climate change (United Nations 2009). Of the above mentioned, though highly influenced by human activity through Green House Gas

(GHG) emissions, climate change is the one not resultant from direct human activities and thus difficult to manage. It is therefore important to protected non-infested areas that are currently climatically suitable for scale insects and those that are projected to be suitable in the future.

Species Distribution Models (SDM’s) have been instrumental in predicting species range shifts by evaluating correlation between the known distribution and climate envelope of a species, and

17 projecting geographic regions that will fall within species climate in the future and vise-versa

(Peterson et al. 2002, Thomas et al. 2004, Elith & Leathwick 2009). Employing SDM’s to predict how scale insects will respond to climate change in South Africa will provide valuable information for containment and early response in eradication efforts. In light of the above mentioned, this project was aimed at resolving challenges associated with identification, phylogenetic relationships and control and prevention of scale insects infestations.

1.6 OBJECTIVES OF THE STUDY

The main objectives of the study were to:

• test the efficiency of nuclear Small Subunit Ribosomal 18S and the nuclear Large Subunit

Ribosomal 28S genes as potential additional barcodes to the universal animal DNA

barcode CO1 for scale insect across 10 families,

• reconstruct the phylogenetic relationships between and within the three major families

Coccidae, Diaspididae and Pseudococcidae based on size and economic importance,

using molecular data, and

• study climate envelope characterising current distribution of scale insects and evaluate

whether the geographic range is predicted to expand or contract, indicating area

vulnerable to infestation or invasion.

1.7 HYPOTHESES OF THE STUDY

The hypotheses of the study include:

 that amplification of all three genes have equal success rates,

 the addition of nuclear markers to the mitochondrial CO1 will equally increase the

efficacy of DNA Barcoding in scale insect identification,

 phylogenies based on molecular data will reveal artificial groupings in scale insects, and

 scale insect ranges will expand in the future due to climate change.

CHAPTER 2

DNA BARCODE EFFICACY FOR THE IDENTIFICATION OF ECONOMICALLY

IMPORTANT SCALE INSECTS (HEMIPTERA: COCCOIDEA) IN SOUTH AFRICA

2.1 INTRODUCTION

Scale insects are members of the monophyletic superfamily Coccoidea within the order

Hemiptera (Ben-Dov et al. 2006). They have paedomorphic adult females and males that display complete metamorphosis (Gullan & Kosztarab 1997, Gullan & Martin 2009). Adult females of many families spend their entire lives under a protective covering known as a “scale”, which enables them to survive adverse environmental conditions (Gullan & Kosztarab 1997). In some species, the scale is thick enough to effectively protect them from insecticides (Amitai 1992), making chemical control difficult.

Of the 8 000 described species of scale insects (Miller et al. 2002), over 570 occur in

South Africa with about 100 species introduced in the country (Hodgson et al. 2011). Most economically important species are polyphagous and feed on a widerange of plant species. They are not only important fresh food product contaminants, but also important pests in the agricultural, horticultural and forestry sectors (Miller & Davidson 1990, Miller et al. 2005A).

With the increase in international trade of fresh produce, export programs rely on pre- and post-shipping inspection by quarantine officials. Once scale insects are intercepted, there is a need for rapid and accurate identification to avoid unnecessary storage costs. In South Africa, the

20 identification service is provided by the Department of Agriculture, Forestry and Fisheries

(DAFF) and the Agricultural Research Council, Plant Protection Research Institute (ARC-PPRI), and by a very limited number of experienced entomologists, who provide identifications based on morphological keys. However, morphological keys for scale insects can be very problematic for two main reasons. First, there is a striking morphological disparity between male and female adult scale insects; females are wingless and paedomorphic, resembling the immatures, and males are winged and dipteri form, having a single pair of wings (Gullan & Cook 2002). Second, due to their very short life expectancy (about three days), adult male scale insects are generally rarely encountered, therefore identification keys are mainly based on the morphology of comparatively longer-living adult females, with a life span from a few weeks to several months

(Ben-Dov et al. 2006). Consequently the identification of males, eggs, and immature stages is mostly impossible, although efforts have been made to provide keys for immatures of a limited number of pest species (e.g. Gullan 2000, Wakgari & Giliomee 2005, for immature instars of mealybugs on citrus). Immature and damaged scale insects that are intercepted may often represent widespread species of low quarantine concern, and if identifiable by quarantine officials, shipments could proceed with less delay. On the other hand, some scale insects are assumed to be widespread species when they are in fact invasive species that do not occur in the importing country, which could result in serious problems if they become established in a new geographic area without their natural enemies (Miller et al. 2002). In addition, even the process of identification of adult females is time-consuming and requires extensive taxonomic experience. This is of great concern from an economical perspective; especially in South Africa where millions of Rands are generated annually from exporting fresh food produce (Walton et al.

2009).

Challenges involved in identification of scale insects make them ideal candidates for molecular-based identification techniques such as DNA barcoding (e.g. Hebert et al. 2003,

Barrett & Hebert 2005). DNA barcoding techniques are becoming increasingly acknowledged as key tools with which to discriminate species, especially when morphological identification techniques are problematic (Herbert et al. 2003, Van der Bank et al. 2012). The mitochondrial cytochrome coxidase1 (CO1) gene sequence has been widely used as a barcode for animals

(Hebert et al. 2004). However, its universality remains challenged, as it has proved controversial for several insect groups such as parasitoid wasps (Stephane et al. 2012), and the scale insect family Pseudococcidae (Park et al. 2010). Moreover, recent studies on these groups have revealed important limitations for the currently developed primers. For instance, a combination of the newly developed forward primer (PcoF1) with the standard reverse primer (LepR1) showed a PCR success rate of only 58% for armoured scales and 76% for mealybugs (see Park et al. 2011). Notwithstanding the important body of literature on barcoding of several scale families

(e.g. Diaspididae and Pseudococcidae, Park et al. 2010, 2011; Pseudococcidae, Malausa et al.

2010, Abd-Rabou et al. 2012, Beltrà et al. 2012, Correa et al. 2011 and Coccidae, Deng et al.

2012), the efficacy of DNA barcoding for other scale insect families remains comparatively unexplored. In this study, the suitability of nuclear regions 18S and 28S as complementary DNA barcodes to the CO1 gene for an improved species discrimination of scale insects of economic importance in South Africa was investigated.

2.2 MATERIALS AND METHODS

2.2.1 Sample collection

Fresh scale insect colonies were collected by searching on their host plants. Most were collected in the vicinity of the Tshwane area, in the Gauteng Province of South Africa. Some samples that had been intercepted by quarantine officials during importation of plants or fruits by the DAFF were included, as well as older collection samples stored at the South African National

Collection of Insects, ARC-PPRI (SANC). Freshly collected specimens were preserved in 95% ethyl alcohol and stored at room temperature (Bisanti et al. 2008).

In total, 480 samples representing at least an estimated 100 species that were identified morphologically (a number were identified to genus or family level only) were studied (Table 1).

These belonged to 10 families of Coccoidea: Asterolecaniidae, Coccidae, Dactylopiidae,

Diaspididae, Eriococcidae, Kerriidae, Lecanodiaspididae, Margarodidae, Ortheziidae and

Pseudococcidae. Although nearly all of the samples studied are economically important species, a few, mostly indigenous species of quarantine risk potential, were included.

2.2.2 DNA extraction and slide preparation

Total DNA was extracted from intact specimens using a QiagenQIAamp mini kit (Southern

Cross Biotechnology, South Africa) with the following modifications: (1) specimens were not ground up, but instead pierced through the abdomen using a fine sterile needle, and the intact cuticle was retrieved after the first lysis step for morphological study and vouchering; (2) after the addition of Proteinase K, specimens were incubated at 70°C for approximately 15 hours (the

23 success rate of the PCR reactions improved with this increased incubation period), and (3) extracted DNA was eluted in 50 µl of elution buffer to increase DNA concentration. The retrieved specimens were prepared for slide mounting and vouchering according to the method of Williams & Granara de Willink (1962), except that the dewaxing procedure in step 2 of this method was omitted for the family Diaspididae, and the staining fluid was prepared according to

Cilliers (1967). Morphological identification was done using relevant published keys for scale insects. All voucher specimens are housed in the scale insect collections of the SANC.

2.2.3 Amplification, sequencing and alignment of DNA barcodes

The 28S region was amplified using the primers s3660 (GAGAGTTMAASAGTACGTGAAAC)

(Dowton & Austin 1988) and 28b (TCGGAAGGAACCAG-CTACTA) (Whiting et al. 1997), and the primers 2660 (CTGGTTGATCCTGCCAGTAG) (as in Tautz et al. 1988) and 18s-B

(CCGCGGCTGCTGGCACCAGA) (as in Von Dohlen & Moran 1995) were used to amplify the

18S region. Amplification reactions contained 0.2 µM of each primer, 1 U Taq DNA polymerase, 0.15 mM of each nucleotide and 5 ng/µl of extracted DNA. For both fragments, a touch–down PCR was performed in which an initial annealing temperature of 56°C was decreased by 0.7°C every three cycles until a final temperature of 42°C was reached, and then held for 18 cycles. After the initial denaturation of 5 min at 94°C, further amplification cycles included 30 sec at 95°C (denaturation) and 120 sec at 72°C (extension), and the appropriate annealing temperature according to the touch-down procedure for 60 seconds. PCR products were visualised on a 1% agarose gel (Merch LTD, South Africa) stained with Gold view

(Sylvean Biotech, South Africa). Sequencing was outsourced to Inqaba Biotec LTD, Tshwane,

South Africa were the obtained amplicons were sequenced following standard sequencing protocols.

For the CO1 region, total extracted DNA was sent to the Canadian Centre for DNA

Barcoding of the University of Guelph, where the CO1 region was amplified and sequenced according to standard barcoding protocols (http://barcoding.si.edu/PDF/

Protocols_for_High_Volume_DNA_Barcode_Analysis.pdf).

Generated sequences were edited in BioEdit (Hall 1999), and then compared with sequences in the GenBank/NCBI database using the Basic Local Alignment Search Tool

(BLAST) system (www.ncbi.nlm.nih.gov/BLAST). Only specimens whose BLAST results corresponded to a scale insect as a first match were included in subsequent analysis. Matrices were then compiled for the three candidate barcode regions, aligned in MAFFT (Katoh et al.

2005), and the alignments were edited manually in BioEdit.

2.2.4 Data analysis

The optimum barcoding region for scale insects was identified according to four criteria. First, the PCR success rate for each DNA region was assessed. Due to unequal sampling size, the test of PCR efficiency for 18S and 28S was conducted on 480 specimens across 10 Coccoidea families, whereas the test for CO1 was based on 430 samples representing eight families. Only records that yielded at least one sequence that meet the accepted barcode standard are reported in

Table 2.1. The amplification success rates of each gene within each of the families were assessed, as well as across all families. PCR efficiency was assessed according to the following

25 scale: 0-49% = low, 50-69% = average and >70% = high PCR success rate, expecting the best candidate barcoding gene region to provide the highest PCR efficiency.

Secondly, for the existence of a ‘barcode gap’ (Meyer & Paulay 2005) in the entire dataset was tested, comparing interspecific vs. intraspecific genetic distances for all six datasets

(18S, 28S, CO1, 18S&28S, 18S&CO1 and 28S&CO1). The genetic distances were calculated using the Kimura-2-parameter (K2P) model (Kimura 1980) which treats transitions and transversions separately, and the significance of the difference between intra- and interspecific distances was assessed using the non-parametric Wilcoxon ranked sum test. The best barcode candidate is also expected to show a barcode gap, i.e. the interspecific distance should be greater than intraspecific distance, such that there is no overlap. For the all markers (18S, 28S, CO1,

18S&28S, 18S&CO1 and 28S&CO1), Neighbour-joining (NJ) trees (Saitou & Nei 1987) were also reconstructed using Paup*v.4.0b.10 (Swofford 2002) to provide a graphic representation of the patterning of the divergences among species. Bootstrap analyses (1 000 replicates) were used to estimate the robustness of internal nodes.

Thirdly, the identification accuracy of species was evaluated by combining three distance-based methods (BOLD criteria of 1% threshold, best close match and near neighbour methods). All three distance-based tests of identification accuracy were conducted using the R software package SPIDER 2.1 (Brown et al. 2012). For the best close match method, an optimised threshold distance for each data set was determined using the function ‘localMinima’.

Further, local sequence databases for each candidate barcode were generated in CLC Genomics

Workbench 4.7.2 (htpp://www.clcbio.com), against which local BLAST searches will be conducted routinely.

Finally, the number of species in the dataset was estimated based on the threshold genetic distance that was previously identified using the function “tclust”, also implemented in SPIDER

2.1. Species that were delimited on genetic threshold were referred to as ‘genetic species’, and their numbers compared to the number of species that had been morphologically identified.

Table 2.1: List of Coccoidea species studied, host plants, collecting localities and number of DNA sequences generated per gene region. All samples were collected in South Africa, except where indicated with an asterisk (*).

Family Species Host Plant Localities No. No. No. (18S) (28S) (CO1) Asterolecaniidae Asterolecanium pustulans Mangifera indica Hoedspruit, Mpumalanga 0 1 0 Asterolecaniidae Asterolecanium quercicola Quercus robur Tshwane, Gauteng 4 4 4 Asterolecaniidae Planchonia stentae Bryophyllum delagoense Rietondale, Tshwane, 0 0 1 Gauteng Asterolecaniidae Planchonia stentae Xysmalobium undulatum Roodeplaat, Gauteng 2 0 11 Coccidae Ceroplastes destructor Syzygium paniculatum Roodeplaat, Gauteng 2 2 4 Coccidae Ceroplastes ficus Tecoma stans Rietondale, Tshwane, 1 3 5 Gauteng Coccidae Ceroplastes rusci Ficus carica Kalamata, Greece * 10 9 9 Coccidae Ceroplastes sinoiae Unidentified plant Tshwane, Gauteng 5 2 2 Coccidae Ceroplastes sp. Citrus sinensis Roodeplaat, Gauteng 1 3 4 Coccidae Ceroplastes sp. Euclea sp. Faerie Glen, Tshwane, 2 2 2 Gauteng Coccidae Ceroplastes sp. Litchi chinensis Tzaneen, Limpopo 0 1 1 Coccidae Ceroplastes sp. Ochna pulchra Tshwane, Gauteng 4 0 4 Coccidae Coccus ehretiae Gymnosporia buxifolia Faerie Glen, Tshwane, 5 3 4 Gauteng Coccidae Coccus hesperidum Citrus sp. Tshwane, Gauteng 5 3 5 Coccidae Coccus hesperidum Ficus sp. Tshwane, Gauteng 5 2 10 Coccidae Coccus sp. Cycas sp. Roodeplaat, Gauteng 1 4 0 Coccidae Marsipococcus proteae Protea caffra Johannesburg, Gauteng 0 1 8

Family Species Host Plant Localities No. No. No. (18S) (28S) (CO1) Coccidae Parasaissetia nigra Ficus sp. Rietondale, Tshwane, 1 2 3 Gauteng Coccidae Parasaissetia nigra Vitis vinifera Roodeplaat, Gauteng 10 8 10 Coccidae Parasaissetia nigra Yucca guatamalensis Roodeplaat, Gauteng 0 9 10 Coccidae Parasaissetia nigra Anredera cordifolia Rietondale, Tshwane, 3 3 5 Gauteng Coccidae Parasaissetia nigra Musa sp. Rietondale, Tshwane, 0 0 2 Gauteng Coccidae Protopulvinaria pyriformis Hedera sp. Roodeplaat, Gauteng 6 3 0 Coccidae Pulvinaria iceryi Grass Roodeplaat, Gauteng 2 2 0 Coccidae Pulvinaria Carpobrotus edulis Roodeplaat, Gauteng 5 0 0 mesembryanthemi Coccidae Pulvinaria psidii Rhus leptodictya Tshwane, Gauteng 3 1 0 Coccidae Pulvinaria psidii Rhus lancea Roodeplaat, Gauteng 0 4 0 Coccidae Pulvinaria saccharia Saccharum officinarum Malelane, Mpumalanga 0 4 0 Coccidae Saissetia coffeae Cyrtanthus sanguineus Rietondale, Tshwane, 5 6 5 Gauteng Coccidae Saissetia oleae Bryophyllum delagoense Johannesburg, Gauteng 1 0 0 Coccidae Saissetia sp. Syzygium paniculatum Rietondale, Tshwane, 0 0 5 Gauteng Dactylopiidae Dactylopius confuses Opuntias ubulata Ayacucho, Peru * 4 0 0 Dactylopiidae Dactylopius opuntiae Opuntia sp. Rietondale, Tshwane, 6 5 0 Gauteng Dactylopiidae Dactylopius sp. Opuntia sp. Rietondale, Tshwane, 0 8 6 Gauteng Diaspididae Africaspis sp. Syzygium paniculata Roodeplaat, Gauteng 2 1 0 Diaspididae Abgrallaspis cyanophylli Anredera cordifolia Rietondale, Tshwane, 4 3 3 Gauteng

Family Species Host Plant Localities No. No. No. (18S) (28S) (CO1) Diaspididae Aonidia sp. Aloe sp. Vredehuis, Tshwane, 0 1 5 Gauteng Diaspididae Aonidiella aurantii Rosa sp. Rietondale, Tshwane, 0 2 5 Gauteng Diaspididae Aspidiotus hartii Unidentified plant Ghana* 1 0 0 Diaspididae Aspidiotus destructor Musa sp. Mozambique * 6 2 3 Diaspididae Aspidiotus nerii Nerium oleander Rietondale, Tshwane, 3 0 4 Gauteng Diaspididae Aspidiotus nerii Melia azedarach Vredehuis, Tshwane, 4 1 3 Gauteng Diaspididae Aspidiotus nerii Unidentified plant Rietondale, Tshwane, 3 0 5 Gauteng Diaspididae Aspidiotus nerii Rhus leptodictya Faerie Glen, Tshwane, 3 1 3 Gauteng Diaspididae Aulacaspis sp. Leucosidea sericea Bushmansnek, Drakensberg, 6 10 0 KZN Diaspididae Aspidoproctus sp. Acacia caffra Tshwane, Gauteng 1 0 0 Diaspididae Aulacaspis tubercularis Litchi chinensis Hazyview, Mpumalanga 3 3 4 Diaspididae Diaspis echinocacti Cereus jamacaru Castle Gorge, North West 4 2 3 Diaspididae Duplachionaspis sp. Aloe sp. Vredehuis, Tshwane, 1 2 5 Gauteng Diaspididae Entaspidiotus lounsburyi Carpobrotus edulis Johannesburg, Gauteng 13 8 0 Diaspididae Neoselenaspidus kenyae Euphorbia ingens Rustenburg area, North West 2 0 0 Diaspididae Pseudaulacaspis Crassula sp. Rietondale, Tshwane, 3 2 0 pentagona Gauteng Diaspididae Pseudaulacaspis Prunus persica Vredehuis, Tshwane, 3 0 0 pentagona Gauteng Diaspididae Pseudaulacaspis Morus alba Roodeplaat, Gauteng 0 0 5

Family Species Host Plant Localities No. No. No. (18S) (28S) (CO1) pentagona Diaspididae Pseudaulacaspis sp. Erythrina sp. Roodeplaat, Gauteng 1 0 0 Diaspididae Selenaspidus sp. Aloe sp. Vredehuis, Tshwane, 3 5 0 Gauteng Diaspididae Separaspis capensis Olea capensis Magaliesberg, North West 4 0 4 Diaspididae Separaspis proteae Protea sp. Faerie Glen, Tshwane, 3 0 1 Gauteng Diaspididae Unidentified species Mimuso pszeyheri Magaliesberg, North West 10 4 0 Eriococcidae Eriococcus araucariae Araucaria sp. Tshwane, Gauteng 4 0 0 Kerriidae Tachardina affluens Combretum sp. Mookgopong, Limpopo 1 0 0 Kerriidae Tachardina Africana Ehretia rigida Rietondale, Tshwane, 2 5 0 Gauteng Kerriidae Tachardina sp. Acacia sp. Rietondale, Tshwane, 4 0 0 Gauteng Lecanodiaspididae Lecanodiaspis sp. Gardenia sp. Rietondale, Tshwane, 0 1 0 Gauteng Margarodidae Icerya purchase Celtis sp. Rietondale, Tshwane, 4 2 3 Gauteng Margarodidae Icerya seychellarum Litchi chinensis Buffelspoort, North West 3 1 2 Margarodidae Monophlebine sp. Grass Tshwane, Gauteng 0 1 0 Ortheziidae Orthezia insignis Lantana camara Rietondale, Tshwane, 0 4 3 Gauteng Pseudococcidae Antonina sp. Bamboo Rietondale, Tshwane, 0 5 0 Gauteng Pseudococcidae Delottococcus aberiae Cussonia sp. Rietondale, Tshwane, 1 4 0 Gauteng Pseudococcidae Delottococcus aberiae Ficus sp. Rietondale, Tshwane, 0 4 0 Gauteng

Family Species Host Plant Localities No. No. No. (18S) (28S) (CO1) Pseudococcidae Dysmicoccus brevipes Ananas comosus Angola * 3 5 2 Pseudococcidae Ferrisia malvastra Talinum paniculatum Rietondale, Tshwane, 6 2 0 Gauteng Pseudococcidae Hypogeococcus pungens Cereus sp. Rietondale, Tshwane, 2 1 0 Gauteng Pseudococcidae Nairobia bifrons Oleaeuropaea africana Roodeplaat, Gauteng 1 1 1 Pseudococcidae Nipaecoccus nipae Palm Tshwane, Gauteng 4 7 4 Pseudococcidae Nipaecoccus graminis Grass Tswaing Nature Reserve, 1 0 0 North West Pseudococcidae Nipaecoccus viridis Acacia sp. Faerie Glen, Tshwane, 3 4 4 Gauteng Pseudococcidae Nipaecoccus viridis Combretum Roodeplaat, Gauteng 9 0 0 erythrophyllum Pseudococcidae Paracoccus burnerae Adansonia digitata Rietondale, Tshwane, 5 1 0 Gauteng Pseudococcidae Paracoccus latebrosus Acacia nilotica Roodeplaat, Gauteng 2 0 0 Pseudococcidae Phenacoccus ficus Unidentified plant Tshwane, Gauteng 1 0 0 Pseudococcidae Phenacoccus manihoti Manihotes culenta Roodeplaat, Gauteng 1 4 3 Pseudococcidae Phenacoccus madeirensis Pelargonium sp. Vredehuis, Tshwane, 0 3 1 Gauteng Pseudococcidae Phenacoccus madeirensis Lantana sp. Rietondale, Tshwane, 1 2 3 Gauteng Pseudococcidae Phenacoccus madeirensis Hippeastrum sp. Brits, North West 4 3 5 Pseudococcidae Phenacoccus solenopsis Vernonia amygdalina Ife-Ife, Nigeria * 1 0 0 Pseudococcidae Planococcus citri Begonia rex Rietondale, Tshwane, 4 1 0 Gauteng Pseudococcidae Planococcus citri Solanum mauritianum Rietondale, Tshwane, 0 3 0 Gauteng

Family Species Host Plant Localities No. No. No. (18S) (28S) (CO1) Pseudococcidae Planococcus citri Ipomoea batatas Roodeplaat, Gauteng 3 4 0 Pseudococcidae Planococcus ficus Unidentified plant Tshwane, Gauteng 5 0 0 Pseudococcidae Pseudococcus longispinus Clivia sp. Vredehuis, Tshwane, 3 5 0 Gauteng Pseudococcidae Pseudococcus viburni Acacia saligna Hermanus, Western Cape 8 5 0 Pseudococcidae Sphaerococcus durus Leucosidea sericea Bushmansnek, Drakensberg, 5 10 0 KwaZulu-Natal Pseudococcidae Vryburgia transvaalensis Bidens pilosa Paarl, Western Cape 0 5 0

2.3 RESULTS

The PCR success rates within the families are indicated in Figure 2.1. These results indicate that the success rate of 28S was high (>70%) within all families tested excluding the Eriococcidae, whereas 18S provided a high amplification rate (>70%) in only the two families Eriococcidae and Ortheziidae. For CO1, which is the preferred DNA barcode gene region for animals, high amplification rate (>70%) was found for only three families (Coccidae, Kerriidae and

Margarodidae). However, the amplification rate was low for one of the most frequently encountered families, the Pseudococcidae (Figure 2.1). In addition to the test done within families, the average PCR success rate across all the families included in the study was calculated based on the results in Figure 2.1. Again, that the highest average success rate was for

28S (77%), followed by CO1 (69%) and 18S (48%) was retrieved.

100 90 80 70 60 18S 50 40 28S 30 20 CO1 10 0

Figure 2.1: Summary of PCR success rate (%) of 18S, 28S and CO1 across 10 scale insect families, with no bar areas represent no/unsuccessful PCR results. The families Lecanodiaspidae and Eriococcidae were not tested for CO1.

The minimum sequence lengths for 18S, 28S and CO1 were 505, 524 and 544 base pairs

(bp) respectively, whereas the maximum lengths were 752, 776 and 645 bp respectively.

Evidence for the existence of barcode gap was found in all three regions: CO1 (median inter- =

0.3 vs. median intra- = 0.0), 18S (median inter- = 0.04 vs. median intra- = 0.0), and 28S (median inter- = 0.034 vs. median intra- = 0.0). The Wilcoxon sum rank test was p < 0.001 for all three regions tested. The interspecific distance for CO1 was significantly greater than that for 18S and

28S (p < 0.001). The plot of the barcode gap showed that a barcode gap exists for about 58% of the tested species using 28S, 37.5 % using 18S and 53% using CO1 (Figure 2.2). The barcode gap for the combined markers 18S&28S, 18S&CO1 and 28S&CO1, exists for greater numbers of the tested species (Figure 2.3). The NJ trees of the single marker mirrored results for the barcode

35 gap such that 28S (Figure 2.4) performed better, followed by CO1 (Figure 2.5) and then 18S

(Figure 2.6). Though all trees showed species clustering to predominantly form well-supported groupings, the tribes and families were not exclusive of each, such that no real relationship could be inferred. A combination of markers gave an overall increase in discriminative ability. With an exception of a few incidences (i.e. Planococcus citri for 18S&28S and Aulacaspis tubercularis

SB 245 2 for 18S&CO1 and 28S&CO1), all species are monophyletic with high bootstap support

(100). For 18S&28S, Planococcus citri is not monophyletic (Figure 2.7). For 18S&CO1, A. tubercularis SB 245 2 groups with Parasaissetia nigra (bootstrap support =100, Figure 2.8), whereas for 28S&CO1, the species does not group with any species (Figure 2.9) including other

A. tubercularis. For 18S&28S however, this species groups with the other A. tubercularis isolates

SB 245 1 and SB 245 3 (Figure 2.4).

Table 2.2: Summary of Meier’s close match test, BOLD threshold ID test and the near neighbour method for all genetic markers

tested. “No ID” indicates the percentages of individuals that could not be identified.

Marker Meier’s close match test BOLD threshold ID test Near neighbor method Ambiguous Correct Incorrect Ambiguous Correct Incorrect Correct Incorrect No ID No ID Identification Identification Identification Identification Identification Identification Identification Identification 18S 55% 33% 8% 4% 55% 33% 8% 4% 60% 40% 28S 25% 65% 3% 7% 28% 63% 3% 6% 64% 36% CO1 34% 53% 6% 7% 34% 53% 6 % 7% 73% 27% 18S&28S 0 96% 4% 0 9% 87% 4% 0 96% 4% 18S&CO1 0 85% 4% 11% 0 89% 4% 7% 91.5% 8.5% 28S&CO1 0 85% 4% 11% 0 89% 4% 7% 91.5% 8.5%

The results for the identification accuracy test performed using three distance-based approaches, viz. Meier’s close match; near neighbour and BOLD thresh ID test criteria of

1% threshold, are indicated in Table 2.2. Among the three distance-based tests of identification accuracy, the near neighbour method provided the highest score of correct identification for COI and 18S: 73% and 60%, respectively. The performance of 28S using the near neighbour method was similar to that of best close match: 64% and 65%, respectively. The BOLD identification criteria provided the lowest identification accuracy

(Table 2.2). However, each time 18S or 28S was added to CO1, the performance increased significantly as follows: CO1 = 73%, 18S&CO1 = 91.5% and 28S&CO1 = 91.5% (near neighbour method in Table 2.2). The combination of 18S and 28S provided greater performance of 96% (near neighbour method in Table 2.2). Using the BOLD Threshold ID test, the difference in identification accuracy (correct ID) between CO1 and 28S was 1.1% in favour of 28S, whereas the accuracy of the combined markers was 41.9% more than that of 28S. This method however, assumes all sequences to be within the 1% threshold. In contrast, Meier’s Best Close Match test, in which an optimised threshold was computed, indicated 28S to have an identification accuracy of almost 10% higher than that of CO1.

Likewise, the combined analysis indicated an identification accuracy of 17% greater than that of 28S.

From the material studied, there is a minimum number of two and a maximum of seven specimens per species for each candidate barcode. The overall results from this study showed the 18S dataset to have 51 ‘genetic species’ instead of 37 morphological species,

41 ‘genetic species’ instead of 43 morphological species for 28S and 66 ‘genetic species’ instead of 63 morphological species for CO1.

Figure 2.2: Interspecific vs. intraspecific distances showing a barcode gap for individual markers18S, 28S and CO1.

Figure 2.3: Interspecific vs. intraspecific distances showing a barcode gap for combined markers18S&28S, 18S&CO1 and 28S&CO1.

Figure 2.4: 28S neighbour-joining tree of K2P distances with 1 000 replicates bootstrap support

Figure 2.5: CO1 neighbour-joining tree of K2P distances with 1 000 replicates bootstrap support

Figure 2.6: 18S neighbour-joining tree of K2P distances with 1 000 replicates bootstrap support

Figure 2.7: 18S&28S neighbour-joining tree of K2P distances with 1 000 replicates bootstrap support 59

Figure 2.8: 18S&CO1 neighbour-joining tree of K2P distances with 1 000 replicates bootstrap support

Figure 2.9: 28S&CO1 neighbour-joining tree of K2P distances with 1 000 replicates bootstrap support

2.4 DISCUSSION

The primary aim of the study is to investigate a method that will facilitate timeous identifications of scale insect species intercepted during international trade of agricultural commodities in South Africa. Traditional morphological identifications are not only time consuming, but often inconclusive for certain closely-related species, due to, inter alia, environmental factors altering morphological characters key for identification (Cox 1983).

A molecular approach, such as DNA barcoding, provides a complementary tool to morphology for identification purposes. Mitochondrial CO1 is the preferred universal barcode for the animal kingdom. However, because all mtDNA genes are maternally inherited, any occurrence of hybridization, male-killing microorganisms, paternal genome elimination (PGE), cytochrome incomparability inducing symbioses, horizontal gene transfer or other reticulate evolution phenomena in a lineage can lead to misleading results

(Croucher et al. 2004, Whitworth et al. 2007). The Neococcid group, comprising the majority of scale insect families, is defined by a number of synapormorphies, including a chromosome system involving PGE (Nur 1980), presence of bacterial endosymbiotes

(Dhami et al. 2012) and likely introgression. Therefore, identification based on mtDNA will most probably have a higher identification error compared to nuclear markers.

In scale insects, a multi-loci approach has proved to be more successful not only in species identification, but also in disentangling complexes of cryptic taxa in mealybugs

(Malausa et al. 2010). Furthermore, a multi-loci approach is more favourable as it is not susceptible to introgression, which is a problem for all single-gene methods because the gene genealogy often does not match that of the species (Monaghan et al. 2006, Whitworth

62 et al. 2007). In this study, the potential of the nuclear 18S and 28S as complementary markers to CO1 was investigated.

In molecular diagnostics, retrieval of DNA sequences should not be problematic, sequence alignment should be easy and the percentage confidence of the identification should be high. The PCR efficiency test in this study showed 28S to have a high success rate compared to 18S and CO1, with 18S showing the lowest success rate. The retrieved

28S sequences were easy to align and gave valuable information for distinguishing scale insects, an observation also made by Deng et al. (2012) in their study of DNA barcoding in six Ceroplastes species in China. Result from the current study shows 18S to be a poor marker for scale insect identification, due to the low PCR success percentage.

The DNA barcoding identification technique relies on the existence of a ‘DNA barcoding gap’ (Hebert et al. 2004), which is the difference between the smallest interspecific distance and the largest intraspecific distance. By using 28S as a single marker, a higher percentage of successful scale insects identification was recovered compared to CO1, however the DNA barcode gap is smaller than that of CO1. Combined markers however, increased the gap with an increased number of species for a combination of 18S, 28S and CO1, followed by that of 28S and CO1. The inclusion of the 18S in identification combinations is however discouraged by the low PCR success rate for this marker as indicated in this study. A combination of 28S and CO1 for the identification of scale insects is thus recommended. With the exception of one, A. tubercularis, all species included in this study are monophyletic. This species should be highlighted and studied

63 future i.e. the collection area re-visited, the colony with all life stages collected for laboratory rearing and then tested by molecular methods to resolve the ambiquity.

Although there are pitfalls in DNA barcoding (Taylor & Harris 2012), the approach provides a convenient, accurate, rapid, and reliable tool for species identification. Expertise required to apply and interpret molecular techniques are easier and faster to acquire than years of experience required to master morphological identification, particularly in scale insects where microscopic features, which may be affected by environmental factors, are used. Moreover, the cost and expertise required are much lower than that of morphological identification, which is the “Golden standard” of scale insect identification. A multi-loci approach, though more costly than a single gene approach, is justified by the increased success rate.

It should however be noted that not all scale insects occurring in South Africa are represented in the generated database. Virgilio et al. (2010) reported that most identification errors are caused by lack of reference data in the library or bank. South

African scale insect researchers and quarantine officials are therefore requested to provide sequence data to populate this database to ensure accuracy and increase the confidence level of barcode identifications. The data generated in this study is therefore of high value and is anticipated to increase trade and consumer confidence with trading partners.

CHAPTER 3

A MOLECULAR PHYLOGENETIC STUDY OF SOUTH AFRICAN SCALE

INSECTS (HEMIPTERA: COCCOIDEA)

3.1 INTRODUCTION

Scale insects are small cryptic insects of the order Hemiptera, generally classified as the superfamily Coccoidea (Gullan & Kosztarab 1997, Gullan & Martin 2003). Most scale insects are parasites of plants, feeding on sap directly from the plant’s vascular system.

They vary drastically in appearance from 1-2 mm long and live under protective test-like oyster shells (e.g. members of the Diaspididae; Miller & Davidson 2005, Miller & Gimpel

2008) to shiny pearl-like creatures of about 5 mm long (e.g. members of the Margarodidae

(Ben-Dov 2008) to those of about 3 mm covered with mealy wax i.e. the Pseudococcidae

(McKenzie 1967, Cox & Pearce 1983, Williams 1985). Most families are economically important, with a few valuable in various fields. For example the cochineal scales

(Dactylopiidae) are used as bio-control agents for Opuntia species with limited (South

Africa) to extreme (Australia) success (Volchasky et al. 1999, Ben-Dov 2008), the ground pearl (Margarodidae) are used in necklace production in South America (Ben-Dov 2008) and the Lac scales’s (Kerridae) resinous lac secretion is a basic ingredient in soles of shoes, felt hats, shoe polishes, artificial fruits, lithographic ink and hair dyes (Ben-Dov 2008).

There are an estimated 8 000 species of scale insects in up to 49 families, 33 of which are extant (Koteja 2001, Hodgson & Hardy 2013), divided traditionally into two

65 informal groups: the Archaecoccoids and the Neococcoids (Williams 2013). The

Archaeococcoid group is comprised of the Margarodidae (Morrison 1928), Carayonemidae

(Kozar & Konczne-Benedicty 2000), Ortheziidae (Miller et al. 2005A), Phenacoleachiidae

(Danzig 1980) and sometimes the Putoidae (Hodgson & Foldi 2006) and the Neococcoid group is comprised of all the other scale insect families (Borchsenius 1950, Miller 1984,

Danzig 1996). The main families in terms of size and economic importance are the

Asterolecaniidae, Coccidae, Dactylopiidae, Diaspididae, Eriococcidae, Kerridae,

Margarodidae, and Pseudococcidae (Gullan & Cook 2007).

One of the major concerns in scale insect systematics is uncertainty regarding the phylogenetic relationships between families and the inability to obtain consensus on the rank and names for higher taxa. Morphological characters are limited in resolving higher phylogeny in scale insects due to a combination of autopomorphic and plesiomorphic features exhibited in many groups (Gullan & Cook 2007). The results of phylogenetic studies based on the nuclear small subribosomal RNA gene, 18S (Cook et al. 2002, Gullan

& Cook 2007) match those of Koteja’s monophyly of neococcoids as well as family-level groupings for Coccoidea based on morphology (Koteja 2004). However, few morphological characters are available with which to test the monophyly of the archaecoccoids and from Cook et al.’s (2002) and Gullan & Cook’s (2007) studies, most archaeococcoid family-lineages stem from a basal polytomy. The extent of 18S divergence among scale insect families is higher than among their sister group, the aphids

(Aphidoidea) (Cook et al. 2002). The lack of phylogenetic resolution recovered among

66 scale insects families from studies using 18S and morphology suggests a relatively rapid evolutionary radiation.

As per recommendations by Gullan & Cook (2007), resolving relationships within the three frequently encountered and most species diverse Coccoidea families, viz. the

Coccidae, Diaspididae and Pseudococcidae, might provide a more natural classification, mainly because the three are the largest and most diverse groups of scale insects. An absolute congruency with morphology-based phylogenies, from studies using any individual gene is not expected due to effects of recognised evolutionary processes on the history of individual genes. A multigene approach, however, provides more resolved phylogenies (Olmstead & Sweere 1994, Rokas et al. 2003), even when a poor-performing gene is added to an initial gene (Gadagkar et al. 2005). Phylogenies from multiple genes can be obtained in two fundamental ways, (i) concatenated, head-to-toe supergene alignments analysed to generate a species tree or (ii) a consensus phylogeny from separately inferred gene phylogenies. The concatenation approach yields more accurate phylogenetic trees, even when the concatenated sequences evolved with very different patterns and no attempts are made to accommodate the differences when inferring phylogenies (Gadagkar et al. 2005).

In this study, a concatenated approach was employed to test monophyly at family level and to investigate the relationships between the eight scale insect families

Asterolecaniidae, Coccidae, Dactylopiidae, Diaspididae, Kerriidae, Margarodidae,

Ortheziidae and Pseudococcidae, using 18S, 28S and CO1. The gene regions were chosen

67 due to the difference in their evolutionary rates. Morphological traits were considered in interpreting the results.

3.2 MATERIALS AND METHODS

3.2.1 Taxon sampling

A total of 53 species were collected into 95% ethyl alcohol and stored at room temperature in the Agricultural Research Institute - Plant Protection Research Institute Biosystematics insect collection (ARC-PPRI Biosystematics, South Africa). The 53 in-group taxa represent eight economically important scale insect families in South Africa as follows:

Asterolecaniidae (1 species), Coccidae (15 species), Dactylopiidae (1 species), Diaspididae

(13 species), Kerriidae (1 species), Margarodidae (4 species), Ortheziidae (1 species) and

Pseudococcidae (17 species). The out-group Aphidoidea species was obtained from

GenBank. Table 3.1 lists the species collected, localities, their host plants and GenBank accession numbers.

Table 3.1: Collected species, host plants, collection localities and GenBank accession numbers, where n/a refers to sequences not available and (-) refers to those whose GenBank number are still being generated.

Species Current family Voucher Host plant Collection location GenBank accession number classification 28S 18S CO1 Asterolecanium quercicola Asterolecaniidae SB 188.1 Quencus robur Pretoria,Union buildings, Gauteng JQ651231 n/a JN309233 Asterolecanium quercicola Asterolecaniidae SB 91.6 Quencus robur Pretoria,Union buildings, Gauteng JQ651232 JQ650937 JN309295 Ceroplastes destructor Coccidae SB 164.1 Syzigium paniculata Gauteng, Roodeplaat, Gauteng JQ651204 JQ650966 JN309260 Ceroplastes ficus Coccidae SB 176.1 Tecoma stans Pretoria, Rietondale, Gauteng JQ651217 JQ650980 JN309279 Ceroplastes rusci Coccidae SB 249.1 Ficus carica Kalamata, Greece JQ651322 JQ651063 JN309369 Ceroplastes sinoiae Coccidae T2.1 Unidentified plant Pretoria, Central, Gauteng JQ651377 JQ651151 HQ97458 Coccus ehretiae Coccidae SB 77.1 Gymnosporia buxifolia Pretoria, Rientondale, Gauteng JQ651186 JQ650925 JN3092263 Coccus hesperidum Coccidae SB 230.1 Ficus sp. Pretoria, Gauteng JQ651279 JQ651023 JN309169 Coccus sordidus Coccidae SB 4.1 Fraxinus americana Pretoria, Rientondale, Gauteng (-) (-) n/a Marsipococcus proteae Coccidae SB 231.1 Protea caffra Johannesburg, Gauteng JQ651367 n/a JN309326 Marsipococcus proteae Coccidae SB 268.1 Protea caffra Johannesburg, Gauteng JQ651367 JQ650895 JN309326 Parasaissetia nigra Coccidae SB 167.1 Vitis vinifera Roodeplaat, Gauteng JQ651209 JQ650975 JN309269 Parasaissetia nigra Coccidae SB 122.7 Ficus sp. Pretoria, Rietondale, Gauteng JQ651196 JQ650949 JN309243 Parasaissetia nigra Coccidae SB 243 .2 Anrendera cordifolia Pretoria, Rietondale, Gauteng JQ651302 JQ651048 JN309243 Protopulvinaria pyriformis Coccidae SB 84.1 Hedera sp. Gauteng, Roodeplaat, Gauteng JQ651189 JQ650930 (-) Pulvinaria iceryi Coccidae SB 219.4 Opuntia sp. Gauteng, Roodeplaat, Gauteng JQ651261 n/a (-) Pulvinaria psidii Coccidae SB 235.6 Rhus leptodictya Pretoria, Gauteng JQ651281 JQ651034 (-) Saissetia coffeae Coccidae SB 20.7 Cyrtanthussp sanguineus Pretoria, Rientondale, Gauteng JQ650888 JQ650885 (-) Saissetia sp. Coccidae SB 4.1 Fraxinus americana Pretoria, Rietondale, Gauteng JQ650883 JQ650867 n/a Dactylopius opuntiae Dactylopiidae SB 72.6 Opuntia sp. Pretoria, Rietondale, Gauteng JQ651184 JQ650918 HQ97457 Abgrallaspis cyonophylli Diaspididae SB 242.3 Anredera cordifolia Pretoria, Rietondale, Gauteng JQ651300 JQ651045 JN3093486 Aonidia species Diaspididae SB 186.1 Aloe sp. Pretoria, Vredehuis, Gauteng JQ651225 JQ650981 JN309285 Aonidiella aurantii Diaspididae SB 52.3 Rosa sp. Pretoria, Rietondale, Gauteng JQ651175 n/a JN309213 Aspidiotus destructor Diaspididae SB 126.8 Musa sp. Mozambique JQ651198 JQ650954 JN309245 Aspidiotus nerii Diaspididae SB 103.1 Melia azedarach Pretoria, Vredehuis, Gauteng JQ651195 JQ650941 JN309236 Aulacaspis sp. Diaspididae SB 247.5 Leucosidea sericea Bushmansnek, Drakensberg, KZN JQ651316 JQ651057 JN309359 Aulacaspis tubercularis Diaspididae SB 245.2 Leucosidea sericea Bushmansnek, Drakensberg, KZN JQ651308 JQ651054 JN309358

Diaspis echinococti Diaspididae SB 55.7 Cereus jamacaru Castle gorge, Western Cape JQ651181 JQ650911 JN309148 Duplachionaspis sp. Diaspididae SB 191.6 Aloe sp. Pretoria, Vredehuis, Gauteng JQ651236 JQ650994 JN309300 Entaspidiotus lounsburyi Diaspididae SB 250.2 Carpobrotus edulis Johannesburg, Gauteng JQ651332 JQ651072 (-) Pseudaulacaspis pentagona Diaspididae SB152_3 Morus alba Gauteng, Roodeplaat JQ651172 JQ650902 (-) Pseudaulacaspis pentagona Diaspididae SB 42.2 Crassula sp. Pretoria, Reitondale, Gauteng JQ651172 JQ650902 JN309256 Selenaspis sp. Diaspididae SB 187.1 Aloe sp. Pretoria, Vredehuis, Gauteng JQ651226 JQ650984 (-) Separaspis capensis Diaspididae SB 289.1 Olea capensis Magaliesberg, North West JQ651118 JQ651120 JN309131 Separaspis capensis Diaspididae SB 290.1 Olea capensis Magaliesberg, North West JQ651121 JQ650867 JN309135 Separaspis proteae Diaspididae SB 73.5 Protea sp. Pretoria, Faerie Glen JQ650920 JQ650921 JN309408 Tachardina africana Kerriidae SB Ehretia rigida Roodeplaat, Gauteng JQ651268 JQ651021 (-) Aspidoproctus sp. Margarodidae SB318.5227.14 Acacia caffra Pretoria, Central, Gauteng (-) n/a n/a Icerya purchase Margarodidae SB 27.2 Celtis sp. Pretoria, Rietondale, Gauteng JQ651167 JQ651104 JN309205 Icerya seychellarum Margarodidae SB 238.3 Litchi chinensis Buffelspoort, North West JQ651286 JQ651039 JN309333 Monophlebine sp. Margarodidae SB319.1 Opuntia sp. Pretoria, Central, Gauteng (-) n/a n/a Orthezia insignis Ortheziidae SB 7.1 Lantana camara Pretoria, Rietondale, Gauteng JQ651158 n/a JN309193 Delottococcus aberiae Pseudococcidae SB 259.1 Ficus species Pretoria, Rietondale, Gauteng JQ651358 n/a (-) Dysmicoccus brevipes Pseudococcidae SB 209.1 Ananas comosus Angola JQ651250 JQ651006 JN309312 Ferrisia malvastra Pseudococcidae SB 11.2 Talinum paniculatum Pretoria, Rietondale, Gauteng JQ651162 JQ650872 (-) Hypogeococcus pungens Pseudococcidae SB 71.2 Opuntia sp. Pretoria, Rietondale, Gauteng JQ651183 JQ650914 n/a Nairobia bifrons Pseudococcidae SB 202.1 Oleaeuropaea africana Roodeplaat, Gauteng JQ651241 JQ651001 HQ97458 Nipaecoccus graminis Pseudococcidae SB286.1 Opuntia sp. Tswaing Nature Reseve, North JQ651258 JQ651119 (6- ) Nipaecoccus nipae Pseudococcidae SB 239.1 Palm tree Pretoria,West Gauteng JQ651287 JQ651040 JN309116 Nipaecoccus viridis Psedococcidae SB 76.4 Acacia sp. Pretoria, Faerie Glen, Gauteng JQ651185 JQ650923 HQ97457 Paracoccus burnerae Pseudococcidae SB 22.5 Adansonia digitata Pretoria, Rietondale, Gauteng JQ650893 n/a8 Phenacoccus madeirensis Pseudococcidae SB 132.2 Lantana sp. Pretoria, Rietondale, Gauteng JQ651200JQ651166 JQ651194 JN309234 Phenacoccus madeirensis Pseudococcidae SB 94.1 Pelargonium sp. Pretoria, Vredehuis, Gauteng JQ651192 n/a JN309234 Phenacoccus manihoti Pseudococcidae SB 215.6 Manihotesculanta Gauteng, Roodeplaat, Gauteng JQ651255 JQ651008 JN309165 Phenacoccus solenopsis Pseudococcidae SB 264.4 Vernonia amygdalina Nigeris, Ife-Ife JQ651366 JQ651097 n/a Planococcus citri Pseudococcidae SB 18.2 Begonia rex Pretoria, Rietondale, Gauteng JQ651164 JQ650880 (-) Planococcus citri Pseudococcidae SB 263.1 Sweet potato Roodeplaat, Gauteng JQ651362 JQ651092 (-) Planococcus ficus Pseudococcidae SB 138.7 Unidentified plant Pretoria, Central, Gauteng JQ651203 JQ650961 (-) Pseudococcus longispinus Pseudococcidae SB 54.1 Clivia sp. Pretoria, Vredehuis, Gauteng JQ651176 JQ650907 (-) Pseudococcus viburni Pseudococcidae SB 265.4 Acacia saligna Hermanus, Western Cape JQ651372 JQ651100 (-) Sphaerococcus sp. Pseudococcidae SB 183.1 Bamboo sp. Pretoria, Rietondale, Gauteng JQ651220 n/a (-) Vryburgia transvaalensis Pseudococcidae SB 241.1 Bidens pilosa Paarl, Western Cape JQ651294 n/a (-)

3.2.2 Molecular data

Nucleotide sequence data was generated from two nuclear ribosomal genes (18S and

28S) and an universal animal barcode region mitochondrial cytochrome oxidase 1

(CO1). The nuclear genes were chosen because they were previously used in reconstructing high level phylogenetic relationships (Cryan et al. 2000, Cook et al.

2002) and the mitochondrial gene was used mainly because of its high evolutionary rate and because phylogenies constructed including barcode libraries are less likely to be influenced by insufficient sampling (Hajibabaei et al. 2007). DNA extraction and PCR was performed as in Sethusa et al. (in press). All sequence data was edited in BioEdit

(Hall 1999). Multiple sequence alignment was performed in MAFFT 6 (Katoh et al.

2005), using the Q-INS-I iterative refinement algorithm and then edited manually in

BioEdit (Hall 1999).

3.2.3 Phylogenetic analyses

Congruency test: The separate datasets, nuclear (18S and 28S) and mitochondrial dataset (CO1), were assessed for congruence using partitioned Bremer support (DeSalle

& Brower 1997) with 1 000 heuristic searches in the program TreeRot v.3 (Sorensen &

Franzosa 2007) in combination with PAUP* v.4.0b.10 (Swofford 2002), to find the nodes at which support increases upon concatenating the data partitions or to identify the sites of incongruence. This method was followed in order to avoid constraining all gene regions to fit a single topology, especially if gene regions differ in evolutionary histories. Based on the congruence test, phylogenetic analyses using Maximum

Parsimony (MP) and Bayesian Inference (BI) methods were carried out.

MP analyses: MP analyses of the total matrix from the three genes was conducted using PAUP* v.4.0b.10 (Swofford 2002). Tree searches were conducted using 1 000 random sequence additions, retaining 10 trees at each step, with tree- bisection-reconnection (TBR) branch swapping and MulTrees in effect. The resulting trees were then used as starting trees in each subsequent search with the same parameters but without any limit for the number of trees per replica (swapping to completion), in order to see if the shortest trees were found in the previous analyses.

Delayed transformation character optimisation was used instead of acceleration of transformation for calculating branch lengths. Branch support was estimated using bootstrap analysis with 1 000 replicates, with reported bootstrap support (BS) of 50% -

74% regarded as low, 75% - 84% moderate and 85% - 100% as strong (as in Daru et al.

2013).

BI analysis: Bayesian analysis of a total data matrix was conducted using

MRBAYES 3.1.2 (Ronquist & Huelsenbeck 2003) on Cipres gateway (Miller et al.

2010). The best fitting model described tested by MODEL TEST 3.7 (Posada &

Crandall 1998) was used to determine the best fitting model for each of the genes.

Results of the Akaike information criteria (AIC, Akaike 1974) indicated the model

GTR+I+G to be best-fitting model for 18S, 28S and CO1, and was used for the three respective genes, with the analysis run for 25 million generations, with model parameters unlinked and estimated independently across partitions. Two independent runs were performed, each with four chains (three heated and one cold). To determine stationarity, log likelihood scores were plotted across generations and standard deviation of split frequencies between the two independent runs was examined for convergence. Of the 25 000 trees sampled in each run, the first 25% of sampled trees

72 were discarded as “burn-in” and the remaining trees were used to construct a 50% majority rule consensus tree showing posterior probabilities (PP) of all bi-partitions. PP values below 0.95 were considered to be weak support and those above 0.95 as strong support (as in Daru et al. 2013). To map BS and PP values, the nexus tree from BS analysis was imported and rescaled manually in MrEnt (Zuccon & Zuccon 2013).

3.3 RESULTS

3.3.1 Molecular evolution

Comparison of sequence partitions: Summary statistics for the MP analyses for the individual partitions (nuclear = 18S and 28S, mitochondrial = CO1) and the combined three-region data set (18S, 28S and CO1) are presented in Table 3.2. The nuclear dataset has a higher number of variable sites (31%) compared with the mitochondrial dataset (13%). The number of parsimony-informative characters for the nuclear data set is lower (32%) than that of the mitochondrial dataset (60%). The average number of changes per variable site for nuclear dataset (retention index RI = 0.697 and consistency index CI = 0.553) is higher than that of the mitochondrial dataset (RI =

0.598 and CI = 0.323). Analysis of the two datasets resulted in similar topology. Trees resulting from the combined nuclear regions 18S and 28S (Figure 3.1), the mitochondrial region CO1 (Figure 3.2) and the combined nuclear and mitochondrial regions (Figure 3.3) are presented.

Combined nuclear data: Individual nuclear analyses had a similar topology and were thus combined and treated as a single dataset (results shown in Table 3.2).

From the heuristic search, 43 (tree length = 2 808) most parsimonious phylogenetic

73 trees were retrieved. The Neococcoids (Diaspididae, Dactylopiidae, Coccidae,

Pseudococcidae, Kerriidae, and Asterolecaniidae) formed a well-supported monophyletic group (PP = 1.00), whereas the Archaecooccids (Ortheziidae and

Margarodidae) formed a non-supported monophyletic clade (Figure 3.1). Within the

Neococccoids and the Archaecoccoids, each family formed moderate to highly supported groups (Figure 3.1).

Mitochondrial data: Summary statistics for the CO1 data matrix are presented in Table 3.2. From the heuristic search, four (tree length = 2 613) most parsimonious phylogenetic trees were retrieved. Analysis of the mitochondrial dataset retrieved moderately supported monophyletic groups of Neococcoids and Archaecoccoids.

Groupings within the Neococcids are however not resolved (Figure 3.2).

Combined nuclear and mitochondrial dataset: Parsimony analysis of the total matrix yielded 49 equally parsimonious trees (length = 6 258). A strict consensus of the trees showed that the Archaeococcoids and Neococcoids formed two distinct monophyletic groups, with the exception of a Monophlebinae species (Margarodidae), which formed an unsupported clade together with the Asterolecaniidae and Kerriidae

(Figure 3.3). The congruency test showed congruency for the Diaspididae and

Margarodidae, congruency at internal nodes and conflict at the tips for the

Pseudococcidae, whereas the Coccidae gave mixed results (Figure 3.3).

Table 3.2: Statistics from MP analyses for the individual partitions and the combined three-region data set.

Nuclear data Mitochondrial data Combined data Number of taxa included 57 47 55 Number of included characters 1 540 669 2 209 Number of constant characters 572 183 535 Number of variable characters (parsimony- uninformative) 478 (31%) 85 (13%) 621 (28%) Number of parsimony- informative characters 490 (32%) 401 (60%) 992 (45%) Number of trees 43 4 49 Tree length 2 808 2 613 6 258 Consistency Index (CI) 0.553 0.323 0.438 Retention Index (RI) 0.697 0.598 0.621 Homoplasy Index (HI) 0.447 0.677 0.562 Model selected by Akaike information Criterion GTR+I+G GTR+I+G GTR+I+G

Figure 3.1: Nuclear analysis showing relationships between and within scale insect families with MP bootsrap and BI posterior probability support (BS/PP).

Figure 3.2: Mitochondrial analysis showing relationships between and within scale insect families with MP bootstrap and BI posterior probability support (BS/PP)

Figure 3.3: Combined analysis showing relationships between and within scale insect families with MP bootstrap and BI posterior probability support (BS/PP), and congruency indicated by green circles while red circles below the branches indicate conflict.

The Coccidae: The data is only for species from the sub-family Ceroplastinae represented by the type genus Ceroplastes and the sub-family Coccinae represented by the tribes

Coccini genus Coccus, Saissetiini genera Saissetia and Parasaissetia, Pulvinariini genera

Pulvinaria and Protopulvinaria, and Paralecaniini genus Marsipococcus as per classification of

Hodgson (1994). Of all the genera represented, Coccus is not monophyletic, with Coccus ehretiae forming a low BI supported clade (PP = 0.82) with the Ceroplastinae, and Coccus hesperidum grouping with Saissetia coffeae with high boostrap and BI support (BS = 100, PP =

1.00) and as expected for a Saissetia species closely related to Pulvinaria species (BS = 85, PP =

0.84) (Figure 3.4). Parasaissatia nigra, commonly confused with closely related Parasaissetia nairobica, Parassaissetia literea and Parasaissetia ficicola and accepted to be represented by two morphologically distinct species, has a species split with low bootstrap and BI support ( BS

= 62, PP = 0.64) (Figure 3.4).

The Diaspididae: In Figure 3.4, brackets indicate the sub-families and tribes. The data represent the two sub-families Aspidiotinae, tribe Aspidiotini and Diaspidinae, tribe Diaspidini

(Figure 3.4), as in Morse & Normark (2006). The strongly supported Aspidiotini (BS = 100; PP

= 1) comprises internal clades/sub-tribes the Aspidiotina (genera Aspidiotus, Aonidiella and

Abgrallaspis), the Aonidiina (genus Aonidia), the Selenaspidina (genera Entaspidiotus and

Selenaspidus) and the Furcaspidina (genus Separaspis) whose relationships are unresolved according to Takagi’s modification (Takagi 2002) of Borchsenius’s classification (Borchsenius

1966). The Diaspidinae, tribe Diaspini (BS = 99; PP = 0.64) comprises the subtribes Diaspidina

(genus Diaspis), the Chionaspidina (genus Duplachionaspis) and the Fioriniina (genus

Pseudaulacaspis) (Takagi 2002).

The Pseudococcidae: The data represent three sub-families Pseudococcinae tribes

Pseudococcini, Planococcini, Trabutini, the Phenacoccinae represented by Phenacoccus madeirensis and P. manihoti as per classifications by Hodgson (2012), the Sphaerococcinae represented by Sphaerocossus durus (Tang 1992). Included also is the ungrouped African taxa

Delottococcus aberiae, Paracoccus burnerae and Vryburgia transvaalensis plus the ungrouped

Ferrisia malvastra (Figure 3.4). Within the tribe Planococcini (genus Planococcus) two sister species Planococcus citri and Placococcus ficus are not exclusive of one another i.e. they group as a single species with high bootstrap and BI support (BS = 100, PP = 0.80) support (Figure

3.4). Sphaerococcini (genus Sphaerococcus) forms a low bootstrap supported and strong BI supported grouping with the African Vryburgia transaalensis (BS = 51; PP = 0.95). Of all the tribes, Pseudococcini seem to be closely related to Planococcini and Trabutini (Nipaecoccus,

Anonina and Hypogeococcus) and is sister to all the other tribes.

Figure 3.4. Combined analysis showing the represented sub-families and tribes, with species and tribal splits and unexpected groupings highlighted in red and ungrouped African taxa indicated by a pink circle and MP bootstrap and BI posterior probability support (BS/PP) on the branches.

3.4 DISCUSSION

Because the results of the nuclear and mitochondrial matrices are congruent, with conflicts mainly at the terminals, the discussion is restricted to results obtained from the combined data set

(Figure 3.3, green circle = congruency, red circle = conflict, and Figure 3.4 showing the different tribes). As recommended by Gullan & Cook (2007), discussion is focused on the three main families; Coccidae, Diaspididae and Pseudococcidae.

The Coccidae: For the third largest family of scale insects Coccidae, the wax covering varies across the family, but is relatively consistent within genera, from an inconspicuous transparent wax to an ornate waxy covering, to mealy wax secretion (Hodgson & Henderson

2000). Few attempts have been made to classify the soft scales. The family group name was introduced by Fallén (1814) and according to Williams (1969), the first mention of the family

Coccidae was by Samouella (1819). However at that time all scale insects were included under this name (Hodgson 1994). Steinweden (1929) was the first to restrict the family Coccidae to the soft scales as understood today. This author studied the type species of 32 genera and proposed three groups, the Coccus group, including Eurecanium, Lecanium, Protopulvinaria, Pulvinaria and Saissetia, the Toumeyella group, also including Neolecanium and Pseudophilippia and the

Exaeretopus group, also with Parafarmairia, Philephedia and Luzulaspis. Currently, the family has about over 1 000 species in 160 genera, classified into 10 sub-families, with the Coccinae divided in to four tribes (Hodgson 1994). My data consists of the two sub-families the

Cerosplastinae represented by the type genus Ceroplastes of the tribe Ceropastiini and the

Coccinae represented by tribes Cocciini, Saissetiini and Pulvinariini. Within the Coccinae however, my results suggests tribal splits for the Saissetiini, Pulvinariini and Coccini. According

81 to the current morphological classification (Hodgson 1994), Saissetia and Parasaissetia species are members of the Saissetiini; and Pulvinaria and Protopulvinaria species members of the

Pulvinariini. My results (Figures 3.3, 3.4) however, suggest that this tribal grouping is not supported by molecular data. For the Coccus species represented in this study, two isolates of C. hesperidium are placed distant from one another, with one grouping with Saissetia coffeae (BS =

100/ PP = 1.00), while the other groups with Coccus sordidus (BS = 100/ PP = 1.00) as expected.

Furthermore, the type species Coccus ehretiae groups with the Ceroplastinae (PP = 0.82). These results suggest that the Coccus grouping based on morphology (Hodgson 1994) is artificial and the genus needs to be revised.

The Diaspididae: The largest family of scale insects, the Diaspididae, characterised by a hard, waxy, scale-like covering which is secreted by and shaped with the pygidium (Foldi 1990), has females with simplified morphology, lacking clear distinction between head, thorax and abdomen, no wings or legs and only rudimentary eyes and antennae (Takagi 2002). Many classifications of armoured scales have been proposed (Ben-Dov & German 2003). The first quantitative attempt to infer the relationships of armoured scales was a phenetic analysis of male morphological characters for 26 species by Ghauri (1962). Much of this data was later reanalysed using principle component and principle coordinate analysis (Boratyński & Davies

1971, Davies 1981). Miller (1990) reported preliminary results of a cladistic analysis of 70 morphological characters of 33 taxa. Morse et al. (2005) later analysed the matrix in conjunction with molecular data for 28 species. The first most thorough molecular analysis was by Morse &

Normark (2006), who examined 112 specimens in 89 species; and Andersen et al. (2008) expanded the molecular data set by increasing the number of specimens to 254 representing 123

82 species. Their results mirrored those of classical morphology-based taxonomy of this family but showed lack of monophyly at subfamily, tribal and sub-tribal levels. This family is currently accepted to be divided into six sub-families, the Aspidiotinae, Leucaspidinae, Odonaspidinae,

Diaspidinae, Comstockiellinae and Ulococcinae (Ben-Dov & German, 2003).

With the exception of a species of Diaspidinae, Aulacaspis tubercularis which though not supported groups with Coccidae (Figure 3.3), the two represented sub-families Aspidiotinae and

Diaspidinae form monophyletic groups exclusive of one another (Figure 3.4). Within the

Aspidiotinae, tribe Aspidiotini, the represented sub-tribes Aspidiotina, Aonidiina and

Selenaspidina form distinct monophyletic groups (Figure 3.4); with the strongly supported

Furcaspidina (BS = 100; PP = 1) basal to the other sub-tribes (Figure 3.3). Contrary to previous work (Ben-Dov & German 2003, Miller & Gimpel 2008, Morse & Normark 2006), my results suggest a genus split for Aspidiotus (Figures 3.3, 3.4). The two represented species A. nerii and

A. destructor do not form a monophyletic clade, suggesting a need for the genus to be revised.

The Pseudococcidae: The second largest scale insect family, the mealybugs

(Pseudococcidae), are characterised by powdery or mealy wax secretion covering the body of the nymphs and adult females, and wingless adult females which ranges between 0.4 – 0.8 mm in body length depending on the species (McKenzie 1967). There is no satisfactory or generally accepted suprageneric classification for mealybugs (reviewed by Ben-Dov 1994). Between three and five subfamilies of mealybugs have been recognised at various times by different workers

(e.g. Koteja 1974A, 1974B, 1988, Danzig 1980, Williams 1985, Tang 1992) but no subfamily classification has found practical use largely because only one subfamily the Rhizoecinae

(Williams 1998), is reasonably well defined with stable generic content. Previously proposed subfamily groups for mealybugs are Pseudococcinae, Phenacoccinae, Trabutininae, Rhizoecinae and Sphaerococcinae, but these names are not in common use (Hodgson 2012). There is no agreed list of genera to be included in Pseudococcinae, Phenacoccinae and Trabutininae, and it is not clear whether any of the subfamilies are monophyletic or even whether the Phenacoccinae and Trabutininae are mutually exclusive (Downie & Gullan 2004). The study by Downie &

Gullan (2004) recognised three major clades of mealybugs, the subfamilies Pseudococcidae,

Phenacoccinae and Rhizoecinae. Within Pseudococcinae they recognised the tribes

Pseudococcini, Planococcini and Trabutinini, as well as the Ferrisia group and some ungrouped

African taxa.

The stable generic well-defined clade, the Rhizoecinae (Downie & Gullan 2004), was not sampled for this study, but only the Phenococcinae, the Pseudococcinae, and the

Sphaerococcinae. The Phenococcinae and the Sphaerococcinae however, are embedded in the

Pseudococcinae (Figure 3.4). The Pseudococcinae grouped into the three tribes Pseudococcini,

Planococcini as well as Trabutini, with the Pseudococcini and Planococcini sister to one another with low support (BS = 62). Though not supported, the subfamily Sphaerococcinae is sister to the African Vryburgia tranvaalensis. The Phenococcus species of the sub-families

Phenococcinae forms a well-supported clade (BS = 100; PP = 1) sister to the tribe Planococcini.

The grouping suggests Phenococcinae and Planococcini to be close to one another, as opposed the Planococcini and the two other Pseudococcinae tribes the Pseudococcini and Trabutini

(Figure 3.3).

The closely related Planococcus citri (Risso 1813) and Planococcus ficus (Signoret

1875), which group together as a single species (Figures 3.3, 3.4), are separated into different species based on the different arrangement of the pores and tubular ducts of the adult females

(http://www.sel.barc.usda.gov/scalekeys/mealybugs/key/mealybugs/media/html/SpeciesListFset. html). To confirm or challenge results obtained from this study, Inter-simple sequence repeat

(ISSR), a molecular technique that uses a single primer containing the repetitive sequence of a microsatellite and amplifies a DNA segment with a nucleotide sequence situated between two microsatellites may be employed (Maltagliati et al. 2006). The ISSR technique is simple and reliable in assessing genetic variability between a variety of living organisms from fish (Saad et al. 2012) to rice (Reddy et al. 2009) and snakes (Guicking et al. 2009). Microsatellites developed for a particular species can often be applied to closely related species however, the percentage loci that successfully amplify may decrease with increasing genetic distance (Jarne & Lagoda

1996). In addition to further molecular testing, similar to the suggested tribal splits, this relationship may be evaluated with more representation and a close up on the morphology and behavior of the taxa in question.

The one major limitation of this study is sample size, which is not comprehensive. The study however, provides genetic material (available online on both BOLD for CO1 and GenBank for 18S and 28S) and phylogenetic trees whose topology agrees with classical scale insect groupings up to sub-family level for Diaspididae and Coccidae and flags areas that need to be evaluated further e.g. the Phenacoccinae and Sphaerococcinae being embedded in the

Pseudococcinae, plus the suggested genera and tribal splits. These results are a good foundation on which to structure further study in that congruency and ability of the selected markers to give

85 meaningful results, using a concatenated approach, were evaluated. With continued and targeted sampling, scale insects phylogeny will be better estimated. With resolved scale insect phylogenies, a variety of evolutionary questions e.g. haplodiploidy, parthenogenetic genetic system, endosymbiosis and plant-insect coevolution, may be answered.

CHAPTER 4

THE POTENTIAL EFFECT OF CLIMATE CHANGE ON SCALE INSECT

DISTRIBUTION PATTERNS IN SOUTH AFRICA

4.1 INTRODUCTION

South Africa is diverse not only in terms of people, culture and languages but also in terms of biological resources and ecology. With only 2% of the planet’s land area, it is home to 6% of the world’s plant and mammal species, 8% of bird species and 5% of reptiles, many of which are found only in South Africa (Driver et al. 2011). The southern African coast is home to almost

15% of known coastal marine species, including 270 marine fish out of a world’s total of 325

(Driver et al. 2011). It is therefore recognised as one of the only 17-megadiverse countries

(Driver et al. 2011). Because of its colonial history and its location on major routes, South

Africa has been particularly prone to invasion by alien species (Picker & Griffits 2011).

Alien invasive species are those species that have been deliberately or accidentally introduced, have subsequently established self-sustaining wild populations and have spread significantly beyond their point of introduction (Picker & Griffits 2011). Pathways of introduction include transport of agricultural products and other freight, movement of travellers by air, sea, and land, release of ballest water from ships, colonization of ship hulls and other infrastructure in the sea, aquaculture and mariculture, interbasin transfer of water, plants introduced for forestry and biofuels, horticultural trade and trade in pests (Driver et al. 2011).

Known invasive alien species in South Africa include 660 plants, six mammal species, 10 birds,

87 at least six reptiles, at least seven crustacean species and more than 70 invertebrates (Driver et al.

2011). These species causes variety of problems, including out-competing, parasitizing or altering native animal and plant communities in the invaded habitat. Moreover, alien species are often free of their natural enemies from their area of origin, allowing them to reach unnaturally high densities in their new habitat (Picker & Griffiths 2011). Second to habitat loss due to urbinasation and agricultural activity, invasion by alien species is the most serious threat to the invaded area’s biodiversity, and poses serious concerns for all biomes and ecosystems (Driver et al. 2011). Primary and secondary global factors including land use changes such as forest sector activities, economics and trade, climate change and changes in atmospheric composition, tourism, conflict and reconstruction, regulatory regimes and biological control of pests support the introduction and spread of alien invasive species (Moore 2005). This study focuses on the influence of climate change on the spread of one of the invasive animal groups, scale insects

(Hemiptera: Sternorrhyncha: Coccoidea), in South Africa.

Climate change is a long-term shift in the statistics of the weather, including averages, such as a change in normals (expected average values for temperature and precipitation) for a given place and time of the year from one decade to another (NOAA 2007). Climate change is a normal part of the earth’s natural variability related to interactions between the atmosphere, ocean, land and changes in the amount of solar radiation reaching the earth. For many key parameters however, the climate system is already moving beyond the patterns of natural variability (IPPC 2007). Human behaviour has contributed to the escalation of climate change through increased emission of Green House Gases (GHG’s) especially CO2. It is estimated that developed countries are responsible for over three quarters of total GHG emissions (United

Nations 2009). Climate change may be a key factor contributing to the increased incidence and intensity of extreme weather events such as droughts, heat waves, floods and heavy storms (Stern

2007, Thomas et al. 2008), and presumably the severe hail storms experienced in South Africa recently.

There is uncertainty with regard to the speed of climate change and the scale of its impact. IPCC has projected that the average global temperature will increase by 1.1- 6.4% over the course of the twenty-first century with potentially dire consequences for humanity (IPCC

2007). Africa has been highlighted to be particularly vulnerable to climate change in the future due to its low adaptive capacity and its sensitivity to many of the projected changes (Callaway

2004, IPPC 2007). By 2020, it is projected that between 75 and 250 million residents of Africa may experience increased H2O stress as a result of climate change, with yields from rain dependent agriculture in some African countries declining up to 50% (IPCC 2007).

In South Africa, sea levels are projected to rise by 90-880 mm. It is projected that Durban will be affected by storms at 200 mm and serious erosion of beaches at 500 mm. Cape Town will be affected by storm flood damage and possibly erosion at 500 mm (Winkler 2002A). It is also projected that global warming will result in about a 10% decrease in precipitation starting in the west of the country and moving east. Mean annual rainfall is projected to decrease by 5-10%

(Kiker 2000). The areas of all biomes are projected to reduce from the west to the north (Turpie

2002, Midgley et al. 2005). It is postulated that Forest and Thicket Biomes will have disappeared and Succulent Karoo Biome will be nearly extinct by 2050 (Turpie 2002). Crop cultivation will most likely be negatively impacted (Winkler 2002B), and since an estimated three million South

African farmers produce food primarily to meet their family needs, rural poverty is predicted to become worsened by climate change (StatsSA 2007).

In addition to understanding and combating challenges associated with climate change in farming e.g. crop cultivation exclusively, farmers are also faced with the challenge of agricultural pests and how climate change will affect infestations and dispersal. Generally, climate change impacts on pests include change in phenotype, distribution, community composition and ecosystem dynamics that may eventually lead to the extinction or hike in numbers of a species (Walther et al. 2002). Climate, particularly temperature and precipitation, have a strong influence on the development, reproduction and survival of insect pests, as a result, it is highly likely that pests will be affected by any changes in climate (Deka et al. 1998). Milder and shorter winters may result in ealier breeding periods (Bale et al. 2002). Other changes include expansion of pest range, disruption of synchrony between pests and natural enemies, and increased frequency of pest outbreaks (Parmesan 2007).

One of the most studied aspects of climate change is the effect of increasing concentration of CO2 on crop and crop pests (Deka et al. 1998). A rise in CO2 generally increases the carbon to nitrogen ratio in plant tissue, thus reducing the plant’s nutritional quality (Coviella

& Trumble 1999). The agricultural pests combat this deficit by compensatory feeding. Insects may accelerate their food intake (Deka et al. 1998), thus weakening the host plant to the point of death. This is particularly important in scale insects, which feeds by sucking sap from plant tissue, with species like Aonidiella aurantii capable to killing a host plant by mere sap removal

(I. Millar, pers. com.).

In scale insects, a study on a common oak tree pest Parthenolecanium quercifex showed urban warming to directly lead to higher abundance (Meineke et al. 2013). These authors collected samples from warmer and cooler zones of their study area, and then compared the behaviour of the scale insect under these different conditions. Clusters of roads and buildings which are much more absorbent of sun rays than natural constituents abundant in rural areas, are responsible for average urban temperatures being several degrees higher in urban areas than those of the surrounding rural area (http://www.mnn.com/earth-matters/climate- weather/stories/tree-killing-insects-adaptating-to-warmer-cities). Parthenolecanium quercifex scales in the hotter urban zones seemed to have adapted to the warmer urban environments such that even egg sacs from the warmer urban zones produced almost four times more eggs than sacs from the cooler zones (Meineke et al. 2013). If climate change causes temperatures to rise as it is expected, scale insects infestations will became an even bigger problem than they currently are.

In general however, the abundance or scarcity of scale insect pests is influenced by climatic conditions such that moderately moist and warm conditions are favourable, while excessively hot plus dry climate and high degree of humidity plus temperature is unfavourable (Marlatt 1903,

Ting-Kui et al. 1994, Malumphy 2010). It is therefore reasonable to believe that climate change will alter the current distribution pattern of scale insects in South Africa, as we know it.

With increased international trade, free trade agreements between states and extensive importation and exportation of plant products e.g. fruit, ornamentals and vegetables, invasion by exotic species such as scale insects into new areas has increased, as well as their subsequent severe consequences (Stumpf & Lamdin 2000). Scale insects and their close relatives, aphids and

91 whiteflies, are among the most important pests of forest and landscape trees (Meineke et al.

2013), and are also considered to be major agricultural pests, often causing serious problems when introduced into a new area without natural enemies (Hodges 2007). They are one of the most successful arthropod groups in terms of invading new geographic areas (Malumphy 2010).

The economic importance and losses associated with these invasions in South Africa includes, but are not limited to, reduced quality of fruit and fruit by-products such as wine (Walton et al.

2009).

When introduced as plant contaminants, scale insects can spread quickly to a variety of other plants and be extremely difficult to control (Johnson 2002). The key to scale insect control is prevention of infestation and persistence and regular scheduling of control methods. It would be most advantageous to timely contain and eradicate these pests before they spread, thus reducing costs associated with control. This however, requires knowledge of when and where introduction occurred. Unfortunately little is known about when most non-native scale insects were first introduced in South Africa, and generalisation based on the little available information will give a distorted or biased view in studies of these pests.

Climate change has been reported to cause reduction and expansion in the ranges of a variety of animals, including pest species, and has altered community composition and dynamics

(e.g., Parmesan 2006, La Sorte & Thompson 2007, Moritz et al. 2008). One common approach to predict how climate changes will influence species ranges is species distribution modelling

(SDM), a method that uses the relationships between species occurrence and climate for prediction (e.g., Peterson et al. 2002, Thomas et al. 2004, Anciaes & Peterson 2006). SDM's are

92 of most importance in Pest Risk Assessment (PRA) prior to or soon after a pest species becomes established in a new area (Kriticos et al. 2013). This is mainly because high-risk areas will be identified and quantified, thus enabling biosecurity resources to be allocated accordingly, to limit the spread of unwanted organisms or initiate eradication. This practice will be particularly beneficial in scale insect pest control, where successful eradication using insecticides is dependent upon application during a small window frame i.e. nymph stage, prior to a protective scale being formed (Amitai 1992).

Though SDMs are fundamentally limited in their ability to enable forecasting and subsequent decision making, managers must nevertheless make decisions. It is an undisputed fact that SDMs have helped map cryptically similar species, revealed geographic patterns, contributed to understanding of niche, suggested places to search for undetected new populations of the modelled species and helped find suitable sites for re-introduction of threatened species

(Leathwick 1998, Leathwick & Austin 2001, Lidsay & Bayoh 2004, Broennimann et al. 2007).

Though there are other measures for scale insect control, e.g. application of insecticides (Smith

1964), prevention of invasion is the most cost effective way of control. The challenge however, is to understand the limits of fundamental niche of the species and thus mapping the set places that species might inhabit (Guereschi et al. 2013).

In this study, scale insect range shift as influenced by climate change was investigated using maximum entropy model software, MaxEnt, which estimates species distribution by finding the distribution of maximum entropy (the closest to uniform), subject to the constraint that the expected value of each variable under the estimated distribution matches its empirical

93 average (Philips et al. 2006). A stratified sample of 43 species with more than eight occurrence points per species was used. Projections were computed per family to avoid overlooking patterns at family level, which would be important for farmers in different crop farming sectors (different scales are pests for different plant groups; see Table 1.1 in Chapter 1). This is the first South

African study to investigate possible new habitat for scale insect infestation. Results from the study will highlight areas most vulnerable to scale insect infestation and enable quarantine officials to be proactive in scale insects management.

4.2 MATERIALS AND METHODS

4.2.1 Study area and occurrence collection

The study area comprised all the provinces of South Africa. Distribution data was collected from three sources: 1) SIBI project, 2) DAFF and 3) a large proportion from SANC. The combined collection was treated as a whole and a stratified sample representing the families

Asterolecaniidae, Coccidae, Dactylopiidae, Diaspididae, Kirriidae, Margarodidae, Ortheziidae and Pseudococcidae was used. Further selection was based on the number of collection points per species i.e. only those species with more than eight collection localities were used (see Table

4.1). A total number of 43 species as listed in Table 4.1 were then used for model testing.

Following which, only 26 species representing the families Coccidae, Diaspididae,

Margarodidae, Ortheziidae and Pseudococcidae were further analysed, with other species excluded by model failure.

Table 4.1: Species used for SDM and their family classification. (Appendix 4.1 gives a more detailed version of the collection).

Accepted family Species classification Asterolecaniidae Asterolecanium quercicola Ceroplastes destructor, Ceroplastes ficus, Ceroplastes rusci, Ceroplastes sinoiae, Coccus ehretiae, Coccus hesperidum, Coccidae Marsipococcus proteae, Parasaissetia nigra, Protopulvinaria pyriformis, Pulvinaria iceryi, Saissetia coffeae Dactylopiidae Dactylopius opuntiae Abgrallaspis cyanophylli, Aonidiella aurantii, Aspidiotus destructor, Aspidiotus nerii, Aulacapis tubercularis, Diaspis Diaspididae echinocacti, Entaspidiotus lounsburyi, Pseudaulacaspis pentagona, Separaspis capensis, Separaspis proteae Kirriidae Tachardina Africana Margarodidae Icerya purchasi, Icerya syechellarum Ortheziidae Orthezia insignis Delottococcus aberiae, Dysmicoccus brevipes, Ferrisia malvastra, Hypogeococcus pungens, Nairobia bifrons, Nipaecoccus graminis, Nipaecoccus nipae, Nipaecoccus viridis, Pseudococcidae Paracoccus burnerae, Phenacoccus madeirensis, Phenacoccus manihoti, Planococcus citri, Planococcus ficus, Pseudococcus longispinus, Pseudococcus viburni, Vryburgia transvaalensis

Although the SANC scale insect collection is large, with over 7 000 sample accessions, no planned systematic survey of the South African scale insects has been undertaken yet. SANC collection samples have generally been gathered in an unsystematic, non-uniform manner, according to no planned ecological sampling method. The distribution patterns indicated by collection points used in this study however correlate with known scale insect distributions in

South Africa (I. Millar, pers. com.). Figures 4.1 A-F below shows the distribution of scale insects to date as per collected records.

Figure 4.1A: Map of the study area showing localities where scale insects of the family

Coccidae were collected in the different agricultural regions in South Africa (Layer source:

SANBI- Biodiversity and monitoring division).

Figure 4.1B: Map of the study area showing localities where scale insects of the family

Diaspididae were collected in the different agricultural regions in South Africa (Layer source:

SANBI- Biodiversity and monitoring division).

Figure 4.1C: Map of the study area showing localities where scale insects of the family

Margarodidae were collected in the different agricultural regions in South Africa (Layer source:

SANBI- Biodiversity and monitoring division)

Figure 4.1D: Map of the study area showing localities where scale insects of the family

Ortheziidae were collected in the different agricultural regions in South Africa (Layer source:

SANBI- Biodiversity and monitoring division).

Figure 4.1E: Map of the study area showing localities where scale insects of the family

Pseudococcidae were collected in the different agricultural regions in South Africa (Layer source: SANBI- Biodiversity and monitoring division).

Figure 4.1F: Map of the study area showing localities where all scale insects were collected.

(Layer source: SANBI- Biodiversity and monitoring division).

4.2.2 Climatic data

Current and projected climatic variables listed in Table 4.2 were downloaded from WorldClim dataset (Hijmans et al. 2005). These particular climate variables were chosen because they are important in the survival and distribution of small arthropods (De Meyers et al. 2010). The climatic data represented interpolated records from 1950–2000 at 2.5 minutes resolution.

Assuming maximum energy usage, future climatic data at 2.5 minutes resolution for the year

2080 was obtained using the Commonwealth Scientific and Industrial Research Organization

(CSIRO-Mk3.0) general circulation model (GCM) and the SRES A1B carbon emission scenario

(Hijmans et al. 2005).

Table 4.2: Bioclimatic variables used as predictors in MaxEnt modeling of species geographic distribution.

Variable Climatic variables code BIO 1 Annual Mean Temperature BIO 2 Mean Diurnal Range (Mean of monthly (max temp - min temp)) BIO 3 Isothermality (BIO2/BIO7) (* 100) BIO 4 Temperature Seasonality (standard deviation *100) BIO 5 Max Temperature of Warmest Month BIO 6 Min Temperature of Coldest Month BIO 7 Temperature Annual Range (BIO5-BIO6) BIO 8 Mean Temperature of Wettest Quarter BIO 9 Mean Temperature of Driest Quarter BIO 10 Mean Temperature of Warmest Quarter BIO 11 Mean Temperature of Coldest Quarter BIO 12 Annual Precipitation BIO 13 Precipitation of Wettest Month BIO 14 Precipitation of Driest Month BIO 15 Precipitation Seasonality (Coefficient of Variation) BIO 16 Precipitation of Wettest Quarter BIO 17 Precipitation of Driest Quarter BIO 18 Precipitation of Warmest Quarter BIO 19 Precipitation of Coldest Quarter

4.2.3 Determination of suitable habitat

Species distribution modeling program MaxEnt version 3.3.3 (Phillips et al. 2006) was used to generate the bioclimatic envelope describing the current distribution range. MaxEnt was chosen for these purposes because it is considered effective with sparse, irregular sampled data with minor location errors, plus it is suitable for application using present-only data (Phillips et al.

2006). Moreover a comparative study by Elith et al. in 2006, showed MaxEnt to give better performance than other distribution models.

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The best approach in approximating an unknown probability distribution is to ensure that the approximate satisfies all known constraints on the unknown distribution and that the distribution has maximum entropy i.e. that is the most spread out, or closest to uniform (Jaynes

1957). In the absence of influences other than those included as constraints in the model, the geographic distribution of a species will tend towards the distribution of maximum entropy

(Aoki 1989, Schneider & Kay 1994). MaxEnt is able to estimates probability distribution of a species by finding maximum entropy, subject to a set of constraints representing incomplete information about the target species (Phillips et al. 2006).

For model calibration and validation, occurrence records for each species were randomly split into75% calibration data used to build the model reffered to as training sample and the 25% data against which the model predictions can be compared reffered to as the test samples.

Duplicate records were excluded from grid cells with the same presence record to reduce the impact of model over fitting. Fifteen sub-sampling replicates with 5 000 iterations per species were run for each model. Model outputs follow a logistic distribution, ranging from zero

(climatically unsuitable areas) to one (climatically suitable areas). A 10-percentile training presence threshold (Ficetola et al. 2007, Phillips & Dudik 2008) was used for transforming the modeled probability of occurrence into binary predictions of species presence or absence, such that 90% of the data is used in developing the model. The MaxEnt output files were processed and visualised using the ArcGIS software version 10. Using the raster calculator in spatial analyst tools of ArcGIS, the shift in geographic extent of suitable habitat per family and combined was calculated (O’Donnell et al. 2012).

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The accuracy and performance of the SDM output provided by MaxEnt were evaluated using Receiver Operation Characteristic (ROC) analysis (Elith et al. 2006, Phillips et al. 2006).

The Area Under the ROC Curve (AUC) ranges between zero and one, with AUC values >0.9 showing “very good”, >0.8-0.9 “good” and >0.7-08 “useful” discrimination ability. Only species whose ranges fall entirely within South Africa and had more than eight collection localities were selected for predictive modeling. The jackknife analysis was used to indicate the most informative variable.

In projecting, the difference in projected distribution between current and future climate scenarios was computed by calculating the difference in per species, per-pixel probabilities of occurrence and then converted to kilometers. An increased probability of occurrence under future climate projections is indicated by a positive value, and a reduced probability of occurrence (current > future) is indicated by a negative value. The difference in summed values between current and future climate scenarios either indicates expansion (positive value) or contraction (negative value) of the geographical extent of the climate envelope describing the current distribution climatically suitable areas.

4.3 RESULTS

4.3.1 Model test

Computed data provided 26.1% “very good”, 47.8% “good”, 8.7% “useful” AUC values and

17.4% “unusable” for our SDM. Figure 4.2 shows example jackknife analysis results of model

102 performance for those species with “very good”, “good”, “usable” and “unuseful” AUC values as stated above, with complete results in Appendix 4.2. The four example species on Figure 4.2 were chosen to portray variation of range in results obtained from this study. Among the tested variables, precipitation of the coldest quarter has the highest explanatory power followed by temperature annual range (maximum temperature of the warmest month-minimum temperature of the coldest month), precipitation of the wettest quarter, minimum temperature of the coldest month and then annual precipitation in general (Table 4.3). Different variables were however used for predictions in each species, as informed by the model test results (Appendix 4.3).

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Figure 4.2: Model performance results showing examples of “very good” (A), “good” (B),

“useful” (C) and “unuseful” (D) performance. Complete results are in Appendix 4.2

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Table 4.3: The percentage contribution of environmental variables in predicting geographic distribution models. Each variable was tested independently on individual species at a time.

Variable A. A. A. C. C. C. C. D. D. E. F. I. M. code destructor nerri tubercularis destructor ehretiae hesperidum Rusci aberiae Opuntiae lounsburyi malvastra syechelarum proteae BIO 1 0 0 0 0 0 0 0.5% 0.1% 6.8% 0 0.1% 2.7% 0 BIO 2 0 1.6% 0 0 0 0.3% 0 12.9% 0 0 0 0 0 BIO 3 17.1% 0 1.4% 3.2% 0 0.2% 0 1.3% 6.2% 19.9% 5% 0 0 BIO 4 0 0.4% 0.3% 14.1% 0 0.5% 0 5.3% 18.4% 0 0 0 0.4% BIO 5 0 0.1% 0 0 0 0 0 0 13.7% 0.2% 0 0 0.1% BIO 6 28.8% 5.2% 41.4% 0 0 27.3% 0 8.5% 8.1% 0 0 6.5% 0 BIO 7 2.7% 6.9% 0 29.2% 0 19% 0 39.1% 0 8.3% 0 0 2.8% BIO 8 0 0 0.1% 0 0 0.1% 0.1% 0 2.3% 0 0 0.9% 0 BIO 9 4.2% 1.5% 0 1.8% 0.9% 4.7% 5.1% 0.2% 9.5% 0.6% 3.2% 0 7.5% BIO 10 0 0 0 0 0 0.2% 0.2% 0.2% 0 0 0 0.9% 0.3% BIO 11 19% 0.8% 18% 0.4% 0 4.2% 0 5.2% 18.8% 0.1% 0.8% 29.6% 0 BIO 12 0.9% 22.8% 0.5% 11.2% 0 3.9% 0 1.8% 4.1% 0 0 0 0.5% BIO 13 9.5% 2.1% 0 2.5% 4.3% 0 0 2.8% 1.3% 0 0 0.1% 0 BIO 14 0 1.1% 0 0 0 14% 0 3.7% 0.4 0 0 0 0 BIO 15 17.7% 0.6% 5.2% 0.4% 78.9% 0 11.8% 0.1% 0.4% 0.1% 2.7% 0.2% 0.1% BIO 16 0 6.5% 0 35.3% 0.4% 0.1% 0.8% 6.5% 7.8% 0 76.5% 1.9% 2.4% BIO 17 0 0.6% 0 0 0 0.1% 0 1% 0.6% 0 0 0.1% 0 BIO 18 0 1.2% 33.2% 0 10.2% 3.1% 0 0.6% 1.4% 0.5% 9.3% 56.4% 0 BIO 19 0 48.4% 0 1.8% 5.5% 22.3% 81.4% 10.6% 0 70.3% 2.3% 0.7% 85.8%

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Table 4.3 continues

N. N. O. P. P. P. P. P. P. P. S. S. T. V. nipae viridia insignis burnerae citri longispinus nigra pentagona pyriformis viburni capensis coffeae africana transvaalensis Average 0 1.1% 0.1% 0.6% 1% 1.4% 0 0 0 0 0 0 1% 0 0.570% 0 0 0 0.3% 0 0.1% 0.4% 0 0 0 0 0 0.5% 0 0.596% 0 4.2% 0.1% 2.4% 3.5% 0 0.7% 0 0 0 1.6% 0 88.4% 1% 5.785% 13.9% 0 0 9.4% 0.3% 0.2% 6.9% 0 0 0.2% 3.6% 0 0.2% 0.4% 2.759% 0 0 0.1% 0 0.3% 0.8% 0 0 0 0.2% 0 0.2% 0 0 0.581% 15.6% 0 0.4% 3.9% 14.5% 13.4% 1.3% 0.2% 0.6% 0 32.6% 1.1% 0 1% 7.792% 51.8% 0 3.4% 30.2% 33.6% 21.6% 37.3% 3.2% 2.2% 1.9% 17.2% 2.4% 0 0.8% 11.614% 0 0.1% 0 1.1% 0.2% 0.1% 0 0 0 0 0 0 0.8% 0 0.214% 0 0 2.3% 2.5% 0.8% 3.2% 6.7% 3.8% 2.6% 12.8% 14.1% 2.3% 0.9% 21.4% 4.170% 0 0.1% 0 0 1.6% 1.5% 0 0 0 0 0 0 0 0 0.185% 16.4 2.6% 1.2% 8.5% 7.2% 6.6% 1% 0 0 0.4% 0 0.2% 0 0 5.222% 2.4% 0 28.1% 0.2% 4.4% 16.5% 13.7% 18% 5.2% 2.2% 0 53.5% 0 0.2% 7.041% 0 5.5% 64.2% 15.4% 8.5% 1.1% 9.8% 0 5.5% 0 0.3% 33.8% 0.2% 0.5% 6.2% 0 0 0 0.3% 3.9% 2.6% 0.2% 7% 0 0 0 0 0 0 1.229% 0 21.6% 0 0.6% 0.5% 9.7% 0.5% 0 0 0 0 0 7.7% 1.7% 5.944% 0 1.2% 0 6.9% 0.8% 0.1% 13.4% 57.9% 73.2% 2.3% 0 0 0 3.4% 11.014% 0 0 0 0.2% 0 0 0 0 0 0 0 0 0 0 0.096% 0 62.4% 0 0.3% 0.9% 0.4% 0.2% 1.6% 3.55 2.6% 0 0 0 0 6.957% 0 1% 0 17.3% 18% 20.7% 7.6% 8.3% 7.3% 77.4% 30.7% 6.6% 0 69.5% 21.981%

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4.3.2 Potential distribution as computed under current and future climate conditions

Coccidae: Figure 4.3A displays the potential distribution for scale insects of the family Coccidae

(soft scales) as computed from SDM under current climate conditions. The distribution shows the coastal borders of the country to be invaded or suitable for soft scales invasion, with

Limpopo, Mpumalanga and KwaZulu-Natal more vulnerable. The Free State, Northern Cape, west of North West, north east of Western Cape and south west of the Eastern Cape are less suitable. The distribution model reveals however, that soft scales range habitat suitability may potentially shift by 2080 making Northern Cape, North West and west of the Free State moderately vulnerable to invasion (Figure 4.3B). Limpopo, KwaZulu-Natal and the Western

Cape are projected to become less suitable for soft scales or less vulnerable to soft scale invasion. From the Eastern Cape, a northwesterly shift sweeping through Swaziland up to the eastern side of the Free State is projected. Projections for the year 2080 using the SRES A1B high carbon emission scenario showed that some species would experience a range expansion.

Combined soft scales species maps showed that there would be a range expansion, with 192.9l km2 of suitable area gained as a result of climate change by 2080.

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Figures 4.3A and B: Combined maps of scale insects of the family Coccidae in South Africa showing (A): current projected distribution and (B): future-current (2080) estimated change in distribution of scale insects of the family Coccidae with red showing areas more vulnerable, baby blue to yellow moderately vulnerable and dark blue areas less vulnerable to Coccidae infestation using the raw probability values from MaxEnt.

Diaspididae: Figure 4.4A displays the potential distribution for scale insects of the family Diaspididae (armoured scales) as computed from SDM under current climate conditions.

Similar to soft scales, the distribution shows the coastal borders of the country to be invaded or suitable for armoured scales invasion, with Limpopo, Mpumalanga and KwaZulu-Natal more vulnerable. The Free State, Northern Cape, west of North West, north east of Western Cape and south west of the Eastern Cape are less suitable. The distribution model also reveals that armoured scale insects habitat suitability may potentially shift by 2080 making Northern Cape,

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North West and west of Free State moderately vulnerable to invasion (Figure 4.4B). Limpopo,

KwaZulu-Natal and the Western Cape are projected to become less suitable for armoured scales or less vulnerable to armoured scale invasion. From the Eastern Cape, a northwesterly shift sweeping through Swaziland up to the eastern side of the Free State is projected. Projections for the year 2080 using the SRES A1B high carbon emission scenario showed that some species would experience a range expansion. Combined armoured scales species maps showed that there would be a range expansion, with 243.49 km2 of suitable area gained as a result of climate change by 2080.

Figures 4.4A and B: Combined maps of scale insects of the family Diaspididae in South Africa showing (A): current projected distribution and (B): future-current (2080) estimated change in distribution of scale insects of the family Diaspididae with red showing areas more vulnerable, baby blue to yellow moderately vulnerable and dark blue areas less vulnerable to Diaspididae infestation using the raw probability values from MaxEnt.

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Margarodidae: Figure 4.5A displays the potential distribution for scale insects of the family Margarodidae (ground pearls) represented by Icerya seycherlarum as computed from

SDM under current climate conditions. The distribution shows the Eastern Coastal Belt suitable for ground pearls invasion, with Limpopo, Mpumalanga, KwaZulu-Natal and east of the Eastern

Cape most vulnerable. The Free State, Northern Cape, Western Cape and most of North West are less suitable. Gauteng is moderately suitable for ground pearl invasion. The distribution model reveals however, that ground pearls habitat suitability may potentially expand by 2080 making the Free State, Northern Cape, North West and the broader Eastern Cape moderately vulnerable to ground pearls invasion (Figure 4.5B). The far west of the country, from Hermanus in the

Western Cape to Richtersveld in the Northern Cape, is projected to be more suitable by 2080

(Figure 4.5B). Same as the armoured scales, ground pearls maps showed that there would be a range expansion, with 243.49 km2 of suitable area gained as a result of climate change by 2080.

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Figures 4.5 A and B: Maps of scale insects of the family Margarodidae (represented by Icerya seycherlarum) in South Africa showing (A): current projected distribution and (B): future- current (2080) estimated change in distribution of scale insects of the family Margarodidae with red showing areas more vulnerable, baby blue to yellow moderately vulnerable and dark blue areas less vulnerable to Margarodidae infestation using the raw probability values from MaxEnt.

Ortheziidae: Figure 4.6A displays the potential distribution for scale insects of the family Ortheziidae (ensign coccids) as computed from SDM under current climate conditions.

The distribution shows Limpopo from Musina to Ga-Mahlakwana, Mpumalanga from

Strydfontein to Piet Retief, the broader KwaZulu-Natal, Eastern Cape from Mount Currie to

Bizana, south to East London and Caledon in the Western Cape to be most vulnerable to ensign coccids invasion. The Free State, Northern Cape, Western Cape excluding Caledon and the west of North West are less suitable, with Gauteng and east of North West moderately suitable. The

111 distribution model reveals however, that ensign coccids habitat suitability may potentially expand by 2080 making the Free State, Northern Cape, west of North West and the broader

Eastern Cape moderately vulnerable to ensign coccids invasion (Figure 4.6B). The north of

Eastern Cape and north east of the Free State will be more suitable to ensign coccids. The broader east of North West and Limpopo from Musina to Ga-Mahlakwana will be less suitable

(Figure 4.6B). Same as the armoured scales and the ground pearls, ensign coccids species maps showed that there would be a range expansion, with 243.49 km2 of suitable area gained as a result of climate change by 2080.

Figures 4.6A and B: Maps of scale insects of the family Ortheziidae (represented by Orthezia insignis) in South Africa showing (A): current projected distribution and (B): future-current

(2080) estimated change in distribution of scale insects of the family Ortheziidae with red showing areas more vulnerable, baby blue to yellow moderately vulnerable and dark blue areas less vulnerable to Ortheziidae infestation using the raw probability values from MaxEnt.

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Pseudococcidae: Figure 4.7A displays the potential distribution for scale insects of the family Pseudococcidae (mealybugs) as computed from SDM under current climate conditions.

The distribution shows the coastal borders of the country to be invaded or suitable for mealybugs invasion, with Limpopo, Mpumalanga, KwaZulu-Natal and the Western Cape more vulnerable.

The Free State, the broader Northern Cape, west of North West, north east of Western Cape and south west of the Eastern Cape are less suitable. The west of the Northern Cape from

Doornfontein to Richtersveld is moderately suitable. The distribution model reveals however, that mealybugs range habitat suitability may potentially shift to the north by 2080 making north of Northern Cape, north of North West and the broader Limpopo more vulnerable to invasion

(Figure 4.7B). The Western Cape, south of Northern Cape and broader Eastern Cape is projected to be moderately to less suitable for mealybugs invasion. Projections for the year 2080 using the

SRES A1B high carbon emission scenario showed that some species would experience a range expansion. Combined mealybugs species maps also showed that there would be range expansion, with 243.49 km2 of suitable area gained as a result of climate change by 2080.

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Figures 4.7A and B: Combined maps of scale insects of the family Pseudococcidae in South

Africa showing (A): current projected distribution and (B): future-current (2080) estimated change in distribution of scale insects of the family Pseudococcidae with red showing areas more vulnerable, baby blue to yellow moderately vulnerable and dark blue areas less vulnerable to scale insect infestation using the raw probability values from MaxEnt.

Combined scale insects: Figure 4.8A displays the potential distribution for scale insects as computed from SDM under current climate conditions. The potential distribution is similar to the actual biogeographic range as displayed in Figure 4.1F. The distribution model reveals however, that scale insects may potentially expand range further to south east of KwaZulu-Natal,

Mpumalanga and Limpopo (Figure 4.8B). The potential distribution exceeds the known current distribution by about 468 km2 north from the west of the country and about 127 km2 in a westerly direction in Limpopo. The model also shows that areas of highest suitability comprise

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Limpopo, Mpumalanga, North West and the Eastern Cape, with suitability reducing from the eastern coastal belt into the interior in these provinces. In the Western Cape, Figure 4.8A shows the highest suitability from Simons Town, the bottom tip of the country, in a north westerly direction, with north east of the Western Cape, including areas such as Oudtshorn, Laingburg,

Prince Albert and Beaufort West unsuitable.

Figures 4.8A and B: Combined maps of scale insects in South Africa showing (A): current projected distribution and (B): future-current (2080) estimated change in distribution of scale insects (combined) with red showing areas more vulnerable, baby blue to yellow moderately vulnerable and dark blue areas less vulnerable to scale insect infestation using the raw probability values from MaxEnt.

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The SDM computed under conditions projected for the year 2080 projects a substantial shift of habitat suitability for scale insects (Figure 4.8B). The model projects range expansion to north west of Limpopo, south of Gauteng, south west of Mpumalanga, north east of Free State, the broader KwaZulu-Natal area and north of the Eastern Cape Province. The shift depicting a general shift into the Interior of the country with North west and Northern Cape which were formerly unsuitable, being more suitable by 2080. The Western Cape, north east of Limpopo and north east of Mpumalanga is projected to be less suitable by 2080.

Projections for the year 2080 using the SRES A1B high carbon emission scenario showed that some species would experience a range expansion. Combined species maps showed that

839.86 km2 (total future range = 55575.60 km2) of suitable area will be gained as a result of climate change in 2080 from the current 54735.74 km2. Taking into account all the tested species, the proportion that will experience range expansion by 2080 is projected to be 78.3%.

4.4 DISCUSSION

SDM’s can determine which areas have or will have suitable climate for a particular species but not necessarily where the species will be found (Mika et al. 2008). One of the main challenges in

SDM studies is the availability of collection records with which models can be run. Museum records (similar to SANC collections) contain valuable information on the collection of species for distribution and abundance studies, and even conservation biology (Graham et al. 2004).

Species records from museums however, often show temporal bias, with more records closer to active expert groups for a particular taxonomic group (Soberon et al. 2000) as observed in this

116 study i.e. more records are in Gauteng close to ARC-PPRI scale insect working group and Cape

Town close to DAFF scale insect working group (Figure 4.1F). Moreover, a single collector never collects museum records, thus the records are not standard and often have faulty Global

Positioning Systems (GPS) coordinates or vague locality description. In this study, the older the record, the more vague the locality description was, with old town names often used in locality descriptions. Much effort was therefore needed for geo-referencing in combination with consultations. Misidentifications also pose a challenge in SDM approaches. In this study however, only records attached to voucher specimens whose identity was confirmed at ARC-

PPRI, Biosystematics division were used.

One significant uncertainty for all models of species response to climate change is that they do not consider the possibility that species might adapt to the changing climate (Mika et al.

2008). Moreover, SDM’s rely on the assumption that a species is currently in equilibrium with the present climate, and the model extrapolates this equilibrium assumption into the future to generate potential range forecast (Yates et al. 2010). Several studies demonstrated that the presence or absence of one species could affect the population and range of another (Connell

1961, Davies et al. 1998, Leathwick & Austin 2001). Complete pest risk assessment including adaptability, life cycle, control options, success of eradication, spread, interactions with other species and transportation means in addition to current and future bioclimatic envelope location as suggested by McKenney et al. (2003), are thus more advisable. Despite these limitations however, SDM’s provides insight into the potential future distributions of a species, including the identification of areas most likely to be invaded based on climatic suitability (Pearson et al.

2002). These models are also a useful tool to support risk management efforts for controlling

117 invasive species (McKenney et al. 2003), and can help to narrow down species that future fieldwork should focus on (Beaumont & Hughes 2002).

An important step in modeling is evaluating model performance (Fielding & Bell 1997,

Hernandez et al. 2006, Elith & Graham 2009). The most common evaluation measure is AUC, which measures model performance across all possible thresholds. In this study the AUC results were catogorised and those species for which the model performed badly were excluded in further analysis (Figures 4.2D).

For the soft and armoured scales, the SDM computed current climate is similar to the occurrence records, indicating that the variables tested are somewhat appropriate to predict biogeographic range. There are however no soft scales records in the climatically suitable west of KwaZulu-Natal bordering Swaziland and north east of KwaZulu-Natal in Ngwavuma area.

For the armoured scales, there are no records in the climatically suitable north east of KwaZulu-

Natal in Ngwavuma area. This may be as a result of lack of introduced species, environmental and demographic stochasticity or lack of appropriate search records. Furthermore, there are records in the climatically unsuitable Northern Cape (Figures 4.3A, 4.4A). This may be because numerous species of scale insects are polyphagous and are transported as plant and plant products contaminates, they may therefore be found in unsuitable areas, however in small numbers as observed in this study (Figures 4.1A, B).

Potential distribution under future climate change for both soft and armoured scales showed that there will be a shift in ranges of suitable habitat making north west of the Eastern

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Cape and east of the Free State more suitable for the two scale insect families (Figures 4.3B,

4.4B). The Free State contributes significantly to the 40% potato production in the country together with Mpumalanga, Limpopo, Eastern Cape, Northern Cape and the high laying areas of

KwaZulu-Natal (http://www.southafrica.info/business/economy/sector/542547.htm). The Eastern

Cape is a major producer of chicory, tomatoes, citrus fruit, deciduous fruit and tea. About 30 000 hectors are used for maize production and 10 000 hectors for forestry in the Eastern Cape.

Furthermore, as part of its provincial growth and development plan (PGDP), the government plans to assign a further 460 000 hectors for food and biofuel crop farming in the Eastern Cape

(http://www.ecdc.co.za/opportunities/agriculture_and_minerals). Invasion by soft scales, pests of woody plants and trees, and armoured scales, pests of a variety of plants (Ben-Dov et al.

2006), would pose a challenge for quarantine officials and results in economic losses for the country.

For the ground pearls, the SDM computed current climate is again similar with the occurrence records indicating that the variables tested are somewhat appropriate to predict biogeographic range. There are however no records in the climatically suitable Eastern Coastal

Belt of the Eastern Cape, west of KwaZulu-Natal around Bilanjil Falls and south west of

Limpopo in the Gransvley area (Figure 4.5A). This may be as a result of lack of search effort, which is common in ground pearls usually found on the roots of the host plants (De Klerk 1985).

Potential distribution under future climate change showed that there will be a shift in ranges of suitable habitat making the west of the Western Cape and Northern Cape more suitable while Limpopo is projected to became less suitable for ground pearls by 2080 (Figure 4.5B).

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Ground pearls are important pests of grape vines (Ben-Dov 2006) and since the Western Cape produces 3.1% of the world’s wine and is rated as number nine in the overall wine production, management such as containment, control and eradication of these scales is important on a global scale (http://www.southafrica.info/business/investing/opportunities/wcape.htm). The challenge in ground pearl management however, is that they are ground dwellers and difficult to detect. As a result, infestation prevention is about the only reasonable cost effective manner of management.

For the ensign coccids, the SDM computed current climate is somewhat congruent with the occurrence records indicating that the variables tested are somewhat appropriate to predict biogeographic range. There are however no records in the climatically suitable Eastern Coastal

Belt of the Eastern Cape, the broader KwaZulu-Natal excluding the infested Pinetown and

Newlands area and broader Limpopo Province (Figure 4.6A). This may be as a result of lack of introduced species, environmental and demographic stochasticity and lack of appropriate search records.

Potential distribution under future climate change showed that there will be a shift in ranges of suitable habitat making the north east of Free State more suitable for ensign coccids.

The Western Cape, Northern Cape, Eastern Cape and the west of the North West is projected to become moderately suitable (Figure 4.6B). Ensign coccids are pests of green house plants (Ben-

Dov et al. 2006), and the industry would be negatively affected by the spread of these pests nationwide.

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For the mealybugs, the SDM computed current climate is similar with the occurrence records indicating that the variables tested are appropriate to predict biogeographic range. There are however no records in the climatically suitable north of Limpopo and north east of KwaZulu-

Natal (Figure 4.7A). This may be as a result of lack of introduced species, environmental and demographic stochasticity or lack of appropriate search records.

Potential distribution under future climate change showed that there would be a general northward shift in ranges of suitable habitat with Limpopo being the most vulnerable to mealybug infestations (Figure 4.7B). Limpopo’s agricultural produce forms a huge portion of the national export with Vhembe and eastern Mopani district contributing to the Johannesburg fresh produce market and Limpopo growers as a group contributing about 45% of the produce sold at

Africa’s biggest market. About 75% of South Africa’s mango, 65% papaya, 60% tomatoes, 36% tea, 25% citrus, banana and litchis are produced in Limpopo

(http://www.idrt.gov.za/LPFDB/industries_agriculture.htm ). With mealybugs being pests of the wider agricultural plants (Ben-Dov et al. 2006), the country could face severe economic losses by 2080.

For combined scale insets, the SDM computed current climate is similar with the occurrence of records indicating that the variables tested are somewhat appropriate to predict scale insect biogeographic range. There are numerous possible explanations for firstly lack of records in climatically suitable areas, and secondly the presence of records in climatically unsuitable areas. For the first scenario, the pattern may be as a result of lack of introduced species, environmental and demographic stochasticity and lack of appropriate search records. For

121 example, the Pondolands (rural Eastern Cape) is generally under surveyed due to lack of proper roads and supportive infrastructure for collectors, as observed in butterflies (Mecenero et al.

2013). For the second scenario, numerous species of scale insects are polyphagous and are transported as plant and plant products contaminates, they may therefore be found in unsuitable areas, however in small numbers as observed in this study (Northern Cape, Figure 4.1F).

Potential distribution under future climate change showed that there will be expanded ranges of suitability of habitat in some provinces, reduced ranges in others and other areas will retreat as they will no longer be climatically suitable for scale insects. The prediction map shows potential expansion could occur into two provinces previously unsuitable, i.e. the Northern Cape and Free State, while range will shift in a north westerly direction for the North West, south for

Gauteng, Mpumalanga and the Eastern Cape, and west for KwaZulu-Natal (Figure 4.8B). The

Western Cape however, will be climatically unsuitable for scale insects (Figure 4.8B). This area is however important for example, grape farming which is a major economical contributor

(Walton et al. 2009), and should not be given less attention. Based on the rate at which biological invasions tend to proceed, it is unsurprising that there are large areas that are projected to be climatically suitable for scale insects by 2080 (Figure 4.8B).

About 13% of South Africa’s surface area could be used for crop production. Primary agriculture contributes about 3.2% to the gross domestic product (GDP) in South Africa and provides almost 9% of formal employment

(http://www.saps.gov.za/statistics/reports/farmattacks/farmattacks_2003.htm). According to

StatsSA (1999) Agricultural Survey, commercial farming generated a gross income of R32.9bn

122 in 1996. Horticultural produce, field crops and forestry products contributed 28%, 27% and 5% respectively. Scale insect infestations are a direct threat to these earnings. It is therefore mandatory for preventative and control measures to be put into place. For those areas with potentially suitable climate that have not been infested yet, strong regulations should be put into place for transportation of plant material which may be carriers of scale insects, particularly from areas currently infested or predicted to be suitable for scale insects. For those areas infested as a result of negligence and naturally infested plantations, barriers have to be put into place and immediate eradication commenced. Other than preventing introduction, the most effective way to manage scale insect is to discover them early and attempt to eradicate before the scale is formed. Results obtained from this study are useful in identifying high-risk areas, which should be observed closely to prevent or control invasion. From this study, it appears that without appropriate intervention, scale insects could spread from their current distribution by 78.3%.

123

CHAPTER 5

SUMMARY AND FUTURE RESEARCH

South Africa’s Agricultural sector is faced with major challenges of uncertainties with regard to the Restitution of Land Right Act 22 assented in 1994 (www.info.gov.za/acts/1994/a22-94.pdf).

A total of R1.835 million was spent in mediation, R7.0 million in land facilitation services and

R3.35 million in development of restitution archives, in addition to all the other logistical costs associated with rolling-out such a major project (www.treasury.gov.za/publications/other/devco- op/section_2/08.pdf). The act was aimed to correct the wrongs of the past, the implication however, was that experienced farmers are to be replaced with inexperienced and under resourced local communities. It therefore still falls on the government to assist new farmers in maintaining production, and relationships with the local and international market. This is of paramount importance in those markets were South Africa is a prominent role player e.g. South

Africa is ranked as number eight in the world for producing liquids from grapes, with a total of

1.0128 million liters split between wine, brandy, distilled wine and grape juice production

(Wines of South Africa 2012).

In addition to the above-mentioned challenges, the new farmers have also inherited insect pest problems. In South Africa, despite control measures, insects are responsible for an estimated 50% of crop losses (Giliomee 2013). Identification of these pests is key to proper control and eradication. For scale insects, identification is a challenge even for experienced taxonomist. For example, Planococcus ficus, an important viral vector spreading grapevine

124 leafroll associated virus 3 (GLRa V-3) and believed to have been introduced in the Western Cape around 1930, was first identified as Planococcus citri by Joubert in 1943. It was only in 2000 that the species was correctly identified by I Millar of ARC-PPRI (Walton & Pringle 2004). The amount of time required to acquire the necessary skills needed for accurate species identification using traditional methods like morphological keys is extremely long; there is a need for these pests to be timely identified. It was thus mandatory for alternative methods to be investigated. In this study, the use of DNA barcoding in scale insect identification was evaluated. Firstly, PCR success rate for the three marker 18S, 28S and CO1 was evaluated, and then the discriminative ability of the three single markers and different marker combinations i.e. 18S&28S, 18S&CO1 and 28S&CO1, using three distance based methods: BOLD criteria of 1% threshold, best close match and near neighbour methods. The PCR results indicated a success rate of 48%, 77% and

69% for 18S, 28S and CO1 respectively. Among the three distance methods used, the near neighbour method provided the highest score of correct identification. The identification score for 18S, 28S and CO1 were 60.3%, 64.2% and 72.7% respectively. The results for combined markers 18S&28S, 18S&CO1 and 28S&CO1 were 95.7%, 91.5% and 91.5% respectively. The use of 18S is however discouraged by the low PCR success rate. Based on PCR results and discriminative ability, a combination of 28S&CO1 is recommended for scale insect identification. Using the combined markers, species will be correctly identified within three days, and then control and management may commence.

Due to current challenges associated with morphological identification, relationships between and within scale insect families are unresolved. In this study, phylogenetic relationships between and within the three major families Coccidae, Diaspididae and Pseudococcidae were

125 evaluated. Using the concatenated approach, the phylogeny was reconstructed based on 18S, 28S and CO1. Firstly, congruency between the nuclear (18S and 28S) and the mitochondrial (CO1) data was tested. Based on the congruence test, phylogenetic analyses using MP and BI methods were then carried out. The results obtained are congruent with current classification in that the three families are exclusive of one another, and for the Coccidae and Diaspididae, tribes are grouping as expected (Hodgson 1994, Andersen et al. 2008). According to Figures 3.3 and 3.4 however, there are some artificial grouping that needs further investigation, either by employing more sensitive molecular techniques i.e. ISSR or engaging in an in-depth study of the morphology and behavior of the highlighted taxa, in relation to the molecular groupings. Results obtained from a combination of molecular, morphology and behavioral studies will enables quarantine officials to timely apply control measured, and thus reduce funds and funds equivalents currently used in scale insect control.

Control and monitoring of scale insects require labor-intensive physical sampling from infested plants and subsequent pesticide administration. Practically, this is most effective in summer, when the insects are in exposed locations and populations are high, by which time however, crop damage has already taken place (Walton & Pringle 2004). Moreover, control by spraying is hampered by the protective scales, plus scale insects usually attach themselves under barks and in crevices on the stems and roots of their host plants and are thus difficult to reach.

Integrated pest management (IPM; chemical and biological control) gives better results than any strategy individually. Poor implementation of one however, can easily have a negative effect on the entire IPM process (Walton & Pringle 2004, Sorribas-Mellado 2011). To date, the most successful and cost effective strategy is prevention of infestations. In order to prevent infestation

126 however, one has to be in a position to anticipate range expansion. This is important in scale insects, which when introduced into a new area, seem to spread at an alarming rate. For example,

Planococcus ficus, which was introduced in 1930 in the Western Cape, spread to the Hex River

Valley and all grape producing areas in the province by 1935 (Walton & Pringle 2004). In this study, the SDM method MaxEnt was employed to generate bioclimatic envelope describing the current and future distribution range based on selected variables. The SDM projections under current climatic conditions were similar to collection localities and known scale insect distribution in South Africa. The projections under future climatic conditions (2080) indicated a range expansion to the north west of Limpopo, south of Gauteng, south west of Mpumalanga, north east of Free State, the broader KwaZulu-Natal and north of Eastern Cape. The Western

Cape is projected to be less suitable by 2080. This range expansion will result in more resources being lost in agricultural produce production as well as control and management measures.

Results obtained from the current study, will enable quarantine officials to take necessary measures to prevent the losses.

Future scale insect studies should focus on populating the barcording reference database with species not currently represented in the current database. Secondly, future studies should focus on improving resolution with the three scale insect families Coccidae, Diaspididae and

Pseudococcidae. Thirdly complete pest risk assessment including adaptability, life cycle, control options, success of eradication, spread, interactions with other species and transportation means should be conducted to better predict range shifts for scale insects.

127

DNA barcoding results obtained from the current study however, are based on a large sample size with a broad coverage of 10 families, and indicate that nearly l00% of the tested species could be correctly identified using the technique. By using the suggested combination of

CO1&28S, scale insects may be identified by any technically trained personnel, in any laboratory in the word. This is a huge improvement from the current situation were only a limited number of experts are capable of giving accurate identification using morphology, and results are to be awaited for, for prolonged periods at the expense of the farmers. Results obtained from the current phylogenetic study, highlights artificial grouping and with further investigation could lead to tribal and genera splits and recognition of overlooked taxa, thus enhancing global knowledge of scale insects diversity. For quarantine official responsible for prevention of pest outbreaks and pest control, the current distribution study provides scientifically valid information, upon which decision may be made to minimise expenditure, and ensure sustained or improved dividends for small and large scale farmers, and the country as a whole.

In conclusion, the first hypothesis that amplification of all three genes has equal success rates is false and that any nuclear marker will equally increase the efficacy of DNA barcoding in scale insect identification is refuted. Results obtained from this study proved that, though the near neighbor method showed the performance of both 18S&CO1 and 28S&CO1 to be 91.5%, the low PCR success rate of 18S makes identification using 18S&CO1 to be less sensitive than that of 28S&CO1. The third hypothesis that molecular phylogenies will reveal artificial groupings in scale insects is supported. Results obtained from this study revealed some artificial groupings at subfamily (for the Pseudococcidae) and genus (for the Coccidae and Diaspididae) level (Figure 3.4). Finally, the fourth hypothesis that the geographic range of scale insect will

128 expand in the future due to climate change is supported. Results obtained from this study showed that, without appropriate interventions, scale insects could spread from their known range with a projected 839.86 km2 by 2080 due to climate change.

129

CHAPTER 6

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APPENDICES

APPENDIX 2.1: Voucher information and GenBank accession numbers of 18S, 28S and CO1 sequences generated in this study. The voucher specimens are deposited in the SANC of insects hosted at the ARC-PPRI Bioystematics division.

18S Asterolecaniidae- Asterolecanium quercicola, PPRI-SB91.6: JQ650937, PPRI-SB188.1:

JQ650987, PPRI-SB188.2: JQ650988, PPRI-SB188.3: JQ650989; Asterolecanium stentae,

PPRI-SB234.1: JQ651028, PPRI-SB234.2: JQ651029; Coccidae- Ceroplastes rusci, PPRI-

SB249.1: JQ651063, PPRI-SB249.2: JQ651064, PPRI-SB249.3: JQ651065, PPRI-SB249.4:

JQ651066, PPRI-SB249.5: JQ651067, PPRI-SB249.6: JQ651068, PPRI-SB249.7: JQ651069,

PPRI-SB249.10: PPRI-SB249.10; Ceroplastes sinoiae, T2.1: JQ651151, T2.2: JQ651152, T2.3:

JQ651153, T2.4: JQ651154, T2.5: JQ651155; Ceroplastes ficus, PPRI-SB176.1: JQ650980;

Ceroplastes species, PPRI-SB225.7: JQ651020, PPRI-SB193.6: Q650996, PPRI-SB193.7:

JQ650997, PPRI-SB299.1: JQ651130, PPRI-SB299.2: JQ651131, PPRI-SB299.3: JQ651132,

PPRI-SB299.4: JQ651133; Ceroplastes destructor, PPRI-SB164.2: JQ650965, PPRI-SB164.7:

JQ650966; Coccus ehretiae, PPRI-SB77.1: JQ650925, PPRI-SB77.2: JQ650926, PPRI-SB77.3:

JQ650927, PPRI-SB77.4: JQ650928, PPRI-SB77.5: JQ650929; Coccus hesperidum, PPRI-

SB230.2: JQ651023, PPRI-SB230.4: JQ651024, PPRI-SB230.9: JQ651026, PPRI-SB230.10:

JQ651027, PPRI-SB166.1: JQ650967, PPRI-SB166.2: JQ650968, PPRI-SB166.3: JQ650969,

PPRI-SB166.4: JQ650970, PPRI-SB166.5: JQ650971; Coccus species, PPRI-SB208.6:

JQ651004; Parasaissetia nigra, PPRI-SB122.7: JQ650949, PPRI-SB167.1: JQ650972, PPRI-

SB167.2: JQ650973, PPRI-SB167.3: JQ650974, PPRI-SB167.4: JQ650975, PPRI-SB167.5:

161

JQ650976, PPRI-SB167.8: JQ650977, PPRI-SB167.9: JQ650978, PPRI-SB167.10: JQ650979,

PPRI-SB243 .2: JQ651048, PPRI-SB243 .4: JQ651049, PPRI-SB243 .5: JQ651050;

Protopulvinaria pyriformis, PPRI-SB84.1: JQ650930, PPRI-SB84.2: JQ650931, PPRI-SB84.3:

JQ650932, PPRI-SB84.4: JQ650933, PPRI-SB84.5: JQ650934, PPRI-SB84.6: JQ650935;

Pulvinaria iceryi, PPRI-SB219.4: JQ651018, PPRI-SB219.5: JQ651019, Pulvinaria mesembryanthemi, PPRI-SB306.1: JQ651137, PPRI-SB306.2: JQ651138, PPRI-SB306.3:

JQ651139, PPRI-SB306.4: JQ651140, PPRI-SB306.5: JQ651141; Pulvinaria psidii, PPRI-

SB235.6: JQ651034, PPRI-SB235.7: JQ651035, PPRI-SB235.8: JQ651036; Saissetia coffeae,

PPRI-SB20.1: JQ650883, PPRI-SB20.3: JQ650884, PPRI-SB20.5: JQ650885, PPRI-SB20.8:

JQ650888; Coccidae species, PPRI-SB4.2: JQ650866, PPRI-SB4.3: JQ650867, PPRI-SB4.4:

JQ650868; Dactylopiidae- Datylopius confusus, PPRI-SB327.1: JQ651147, PPRI-SB327.3:

JQ651148, PPRI-SB327.4: JQ651149, PPRI-SB327.5: JQ651150; Dactylopius opuntiae, PPRI-

SB72.4: JQ650916, PPRI-SB72.5: JQ650917, SB298.1: JQ651126, SB298.2: JQ651127,

SB298.3: JQ651128, SB298.4: JQ651129; Diaspididae- Africaspis species, PPRI-SB244.1:

JQ651051, PPRI-SB244.2: JQ651052; Abgrallaspis cyonophylli, PPRI-SB242.1: JQ651044,

PPRI-SB242.3: JQ651045, PPRI-SB242.4: JQ651046, PPRI-SB242.5: JQ651047; Aspidiotus destructor, PPRI-SB126.1: JQ650950, PPRI-SB126.2: JQ650951, PPRI-SB126.5: JQ650952,

PPRI-SB126.7: JQ650953, PPRI-SB126.8: JQ650954, PPRI-SB126.9: JQ650955; Aspidiotus nerii, PPRI-SB6.3: JQ650869, PPRI-SB6.4: JQ650870, PPRI-SB6.5: JQ650871, PPRI-SB103.1:

JQ650941, PPRI-SB103.3: JQ650942, PPRI-SB103.4: JQ650943, PPRI-SB103.5: JQ650944,

PPRI-SB190.1: JQ650990, PPRI-SB190.2: JQ650991, PPRI-SB190.4: JQ650993, PPRI-

SB195.4: JQ650998, PPRI-SB195.8: JQ650999, PPRI-SB195.9: JQ651000; Aulacaspis species,

PPRI-SB247.4: JQ651056, PPRI-SB247.5: JQ651057, PPRI-SB247.6: JQ651058, PPRI-

162

SB247.7: JQ651059, PPRI-SB247.8: JQ651060, PPRI-SB247.9: JQ651061; Aulacaspis tubercularis, PPRI-SB245.1: JQ651053, PPRI-SB245.2: JQ651054, PPRI-SB245.3: JQ651055;

Diaspis echinococti, PPRI-SB55.6: JQ650910, B 55.7: JQ650911, PPRI-SB55.8: JQ650912,

PPRI-SB55.9: JQ650913; Duplachionaspis species, PPRI-SB191.6: JQ650937; Entaspidiotus lounsburyi, PPRI-SB250.1: JQ651071, PPRI-SB25: JQ651072, PPRI-SB250.3: JQ651073,

PPRI-SB250.4: JQ651074, PPRI-SB250.5: JQ651075, PPRI-SB250.6: JQ651076, PPRI-

SB250.7: JQ651077, PPRI-SB250.8: JQ651078, PPRI-SB250.9: JQ651079, PPRI-SB307.1:

JQ651142, PPRI-SB307.2: JQ651143, PPRI-SB307.4: JQ651144, PPRI-SB307.5: JQ651145;

Neoselenaspis kenyae, PPRI-SB303.3: JQ651135, PPRI-SB303.4: JQ651136; Pseudaulacaspis pentagona, PPRI-SB42.3: JQ650902, PPRI-SB42.4: JQ650903, PPRI-SB42.5: JQ650904, PPRI-

SB102.1: JQ650938, PPRI-SB102.2: JQ650939, PPRI-SB102.4: JQ650940; Pseudaulacaspis species, PPRI-SB89.1: JQ650936; Selenaspis species, PPRI-SB187.1: JQ650984, PPRI-

SB187.2: JQ650985, PPRI-SB187.3: JQ650986; Separaspis capensis, PPRI-SB289.1:

JQ651118, PPRI-SB289.5: JQ651119, PPRI-SB290.1: JQ651120, PPRI-SB290.2: JQ651121;

Separaspis proteae, PPRI-SB73.3: JQ650919, PPRI-SB73.5: JQ650920, PPRI-SB73.6:

JQ650921; Diaspididae species, PPRI-SB254.1: JQ651080, PPRI-SB254.2: JQ651081, PPRI-

SB254.3: JQ651082, PPRI-SB254.4: JQ651083, PPRI-SB254.5: JQ651084, PPRI-SB254.6:

JQ651085, PPRI-SB254.7: JQ651086, PPRI-SB254.8: JQ651087, PPRI-SB254.9: JQ651088,

PPRI-SB254.10: JQ651089; Eriococcidae- Eriococcus araucariae, PPRI-SB110.1: JQ650945,

PPRI-SB110.2: JQ650946, PPRI-SB110.3: JQ650947, PPRI-SB110.5: JQ650948; Kirriidae-

Tachardina affluens, PPRI-SB321.3: JQ651146; Tachardina africana, PPRI-SB227.14:

JQ651021, PPRI-SB227.15: JQ651022; Tachardiana species, PPRI-SB1.1: JQ650862, PPRI-

SB1.2: JQ650863, PPRI-SB1.3: JQ650864, PPRI-SB1.4: JQ650865; Margorodidae- Icerya

163 purchasi, PPRI-SB27.1: JQ650894, PPRI-SB27.2: JQ650895, PPRI-SB27.3: JQ650896, PPRI-

SB27.5: JQ650897; Icerya seychellarum, PPRI-SB238.1: JQ651037, PPRI-SB238.2: JQ651038,

PPRI-SB238.3: JQ651039; Pseudococcide- Delottococcus aberiae, PPRI-SB256.1: JQ651090;

Dysmicoccus brevipes, PPRI-SB209.3: JQ651005, PPRI-SB209.4: JQ651006, PPRI-SB209.5:

JQ651007; Ferrisia malvastra, PPRI-SB11.2: JQ650872, PPRI-SB11.5: JQ650873, PPRI-

SB11.6: JQ650874, PPRI-SB11.7: JQ650875, PPRI-SB11.8: JQ650876, PPRI-SB11.9:

JQ650877; Hypogeococcus pungens, PPRI-SB71.4: JQ650914, PPRI-SB71.5: PPRI-SB71.5;

Nairobia bifrons, PPRI-SB202.6: JQ651001; Nipaecoccus nipae, PPRI-SB239.1: JQ651040,

PPRI-SB239.2: JQ651041, PPRI-SB239.8: JQ651042, PPRI-SB239.10: JQ651043; Nipaecoccus viridis, PPRI-SB76.2: JQ650922, B 76.4: JQ650923, PPRI-SB76.5: JQ650924, PPRI-SB216.1:

JQ651009, PPRI-SB216.2: JQ651010, PPRI-SB216.3: JQ651011, PPRI-SB216.4: JQ651012,

PPRI-SB216.5: JQ651013, PPRI-SB216.6: JQ651014, PPRI-SB216.7: JQ651015, PPRI-

SB216.8: JQ651016, PPRI-SB216.9: JQ651017; Paracoccus burnerae, PPRI-SB22.1:

JQ650889, PPRI-SB22.2: JQ650890, PPRI-SB22.3: JQ650891, PPRI-SB22.4: JQ650892, PPRI-

SB22.5: PPRI-SB22.5; Phenococcus ficus, PPRI-SB138.1: JQ650959, PPRI-SB138.6:

JQ650960, PPRI-SB138.7: JQ650961, PPRI-SB138.8: JQ650962, PPRI-SB138.9: JQ650963;

Paracoccus latebrosus, PPRI-SB204.4: JQ651002, PPRI-SB204.5: JQ651003; Phenacoccus manihoti, PPRI-SB215.6: JQ651008; Phenacoccus madeirensis, PPRI-SB273.1: JQ651102,

PPRI-SB287.1: JQ651105, PPRI-SB287.2: JQ651106, PPRI-SB287.4: JQ651107, PPRI-

SB287.5: JQ651108; Phenacoccus solenopsis, PPRI-SB264.3: JQ651097; Planococcus citri,

PPRI-SB18.1: JQ650879, PPRI-SB18.2: JQ650880, PPRI-SB18.4: JQ650881, PPRI-SB18.5:

JQ650882, PPRI-SB263.1: JQ651092, PPRI-SB263.3: JQ651094, PPRI-SB263.5: JQ651096;

Pseudococcus longispinus, PPRI-SB54.2: JQ650907, PPRI-SB54.4: JQ650908, PPRI-SB54.5:

164

JQ650909; Pseudococcus viburni, PPRI-SB265.1: JQ651098, PPRI-SB265.2: JQ651099, PPRI-

SB265.4: JQ651100, PPRI-SB265.5: JQ651101, PPRI-SB292.2: JQ651122, PPRI-SB292.3:

JQ651123, PPRI-SB292.4: JQ651124, PPRI-SB292.5: JQ651125; Sphaerococcus durus, PPRI-

SB247.5: JQ651057, PPRI-SB247.6: JQ651058, PPRI-SB247.7: JQ651059, PPRI-SB247.8:

JQ651060, PPRI-SB247.9: JQ651061.

28S

Asterolecaniidae- Asterolecanium quercicola, PPRI-SB188.1: JQ651231, PPRI-SB188.2:

JQ651232, PPRI-SB188.3: JQ651233, PPRI-SB188.4: JQ651234; Coccidae- Ceroplastes rusci,

PPRI-SB249.1: JQ651322, PPRI-SB249.2: JQ651323, PPRI-SB249.3: JQ651324, PPRI-

SB249.4: JQ651325, PPRI-SB249.5: JQ651326, PPRI-SB249.6: JQ651327, PPRI-SB249.7:

JQ651328, PPRI-SB249.8: JQ651329, PPRI-SB249.9: JQ651330; Ceroplastes sinoiae, T2.1:

JQ651377, T2.5: JQ651378; Ceroplastes ficus, PPRI-SB176.1: JQ651217, PPRI-SB176.2:

JQ651218, PPRI-SB176.6: JQ651219; Ceroplastes species, PPRI-SB225.6: JQ651262, PPRI-

SB225.7: JQ651263, PPRI-SB225.10: JQ651264, PPRI-SB193.1: JQ651238, PPRI-SB193.6:

JQ651239, PPRI-SB246.1: JQ651310; Ceroplastes destructor, PPRI-SB164.1: JQ651204, PPRI-

SB164.7: JQ651205; Coccus ehretiae, PPRI-SB77.1: JQ651186, PPRI-SB77.4: JQ651187,

PPRI-SB77.5: JQ651188; Coccus hesperidum, PPRI-SB230.1: JQ651279, PPRI-SB230.7:

JQ651280, PPRI-SB166.1: JQ651206, PPRI-SB166.2: JQ651207, PPRI-SB166.3: JQ651208;

Coccus species, PPRI-SB208.3: JQ651246, PPRI-SB208.4: JQ651247, PPRI-SB208.5:

JQ651248, PPRI-SB208.6: JQ651249; Marsipococcus proteae, PPRI-SB268.1: JQ651367;

Parasaissetia nigra, PPRI-SB122.7: JQ651367, PPRI-SB122.8: JQ651197, PPRI-SB167.1:

JQ651209, PPRI-SB167.2: JQ651210, PPRI-SB167.3: JQ651211, PPRI-SB167.5: JQ651212,

PPRI-SB167.6: JQ651213, PPRI-SB167.7: JQ651214, PPRI-SB167.8: JQ651215, PPRI-

165

SB167.9: JQ651216, PPRI-SB228.1: JQ651270, PPRI-SB228.2: JQ651271, PPRI-SB228.3:

JQ651272, PPRI-SB228.4: JQ651273, PPRI-SB228.5: JQ651274, PPRI-SB228.6: JQ651275,

PPRI-SB228.7: JQ651276, PPRI-SB228.8: JQ651277, PPRI-SB228.9: JQ651278, PPRI-

SB243.2: JQ651302, PPRI-SB243.4: JQ651303, PPRI-SB243.5: JQ651304; Protopulvinaria pyriformis, PPRI-SB84.1: JQ651189, PPRI-SB84.2: JQ651190, PPRI-SB84.5: JQ651191;

Pulvinaria iceryi, PPRI-SB219.2: JQ651260, PPRI-SB219.4: JQ651261; Pulvinaria psidii,

PPRI-SB205.1: JQ651242, PPRI-SB205.2: JQ651243, PPRI-SB205.3: JQ651244, PPRI-

SB205.5: JQ651245, PPRI-SB235.6: JQ651281; Pulvinaria saccharia, PPRI-SB237.1:

JQ651282, PPRI-SB237.2: JQ651283, PPRI-SB237.3: JQ651284, PPRI-SB237.4: JQ651285;

Dactylopiidae- Dactylopius species, PPRI-SB257.2: JQ651350, PPRI-SB257.3: JQ651351,

PPRI-SB257.5: JQ651352, PPRI-SB257.6: JQ651353, PPRI-SB257.7: JQ651354, PPRI-

SB257.8: JQ651355, PPRI-SB257.9: JQ651356, PPRI-SB257.10: JQ651357; Diaspididae-

Africaspis species, PPRI-SB244.1: JQ651305; Abgrallaspis cyonophylli, PPRI-SB242.1:

JQ651299, PPRI-SB242.3: JQ651300, PPRI-SB242.4: JQ651301; Aonidia species, PPRI-

SB186.1: JQ651225; Aonidiella aurantii, PPRI-SB52.1: JQ651174, PPRI-SB52.3: JQ651175;

Aspidiotus destructor, PPRI-SB126.8: JQ651198, PPRI-SB126.9: JQ651199; Aspidiotus nerii,

PPRI-SB103.1: JQ651195, PPRI-SB195.4: JQ651240; Aulacaspis species, PPRI-SB247.1:

JQ651312, PPRI-SB247.2: JQ651313, PPRI-SB247.3: JQ651314, PPRI-SB247.4: JQ651315,

PPRI-SB247.5: JQ651316, PPRI-SB247.6: JQ651317, PPRI-SB247.7: JQ651318, PPRI-

SB247.8: JQ651319, PPRI-SB247.9: JQ651320, PPRI-SB247.10: JQ651321; Aulacaspis tubercularis, PPRI-SB245.1: JQ651307, PPRI-SB245.2: JQ651308, PPRI-SB245.3: JQ651309;

Diaspis echinocacti, PPRI-SB55.7: JQ651181, PPRI-SB55.8: JQ651182; Duplachionaspis species, PPRI-SB191.6: JQ651236, PPRI-SB191.9: JQ651237; Entaspidiotus lounsburyi, PPRI-

166

SB250.1: JQ651331, PPRI-SB250.2: JQ651332, PPRI-SB250.3: JQ651333, PPRI-SB250.5:

JQ651334, PPRI-SB250.7: JQ651335, PPRI-SB250.8: JQ651336, PPRI-SB250.9: JQ651337,

PPRI-SB250.10: JQ651338; Pseudaulacaspis pentagona, PPRI-SB42.2: JQ651172, PPRI-

SB42.5: JQ651173; Selenaspidus species, PPRI-SB187.1: JQ651226, PPRI-SB187.2: JQ651227,

PPRI-SB187.3: JQ651228, PPRI-SB187.4: JQ651229, PPRI-SB187.5: JQ651230; Diaspididae species, PPRI-SB254.4: JQ651339, PPRI-SB254.8: JQ651340, PPRI-SB254.9: JQ651341,

PPRI-SB254.10: JQ651342; Kirriidae- Tachardina africana, PPRI-SB227.11: JQ651265, PPRI-

SB227.12: JQ651266, PPRI-SB227.13: JQ651267, PPRI-SB227.14: JQ651268, PPRI-SB227.15:

JQ651269; Margarodidae- Icerya purchaie, PPRI-SB27.2: JQ651167, PPRI-SB27.3:

JQ651168; Icerya seychellarum, PPRI-SB238.2: JQ651286; Ortheziidae- Orthezia insignis,

PPRI-SB7.1: JQ651158, PPRI-SB7.3: JQ651159, PPRI-SB7.4: JQ651160, PPRI-SB7.5:

JQ651161; Pseudococcidae- Delottococcus aberiae, PPRI-SB256.1: JQ651343, PPRI-SB256.3:

JQ651345, PPRI-SB256.4: JQ651346, PPRI-SB256.7: JQ651347, PPRI-SB259.1: JQ651358,

PPRI-SB259.2: JQ651359, PPRI-SB259.3: JQ651360, PPRI-SB259.4: JQ651361; Dysmicoccus brevipes, PPRI-SB209.1: JQ651250, PPRI-SB209.2: JQ651251, PPRI-SB209.3: JQ651252,

PPRI-SB209.4: JQ651253, PPRI-SB209.5: JQ651254; Ferrisia malvastra, PPRI-SB11.2:

JQ651162, PPRI-SB11.6: JQ651163; Hypogeococcus pungens, PPRI-SB71.2: JQ651183;

Nairobia bifrons, PPRI-SB202.1: JQ651241; Nipaecoccus nipae, PPRI-SB239.1: JQ651287,

PPRI-SB239.3: JQ651288, PPRI-SB239.4: JQ651289, PPRI-SB239.5: JQ651290, PPRI-

SB239.6: JQ651291, PPRI-SB239.8: JQ651292, PPRI-SB239.10: JQ651293; Nipaecoccus viridis, PPRI-SB76.4: JQ651185; Paracoccus burnerae, PPRI-SB22.5: JQ651166; Phenacoccus manihoti, PPRI-SB215.6: JQ651255, PPRI-SB215.8: JQ651257, PPRI-SB215.9: JQ651258,

PPRI-SB215.10: JQ651259; Phenacoccus madeirensis, PPRI-SB94.1: JQ651192, PPRI-SB94.2:

167

JQ651193, PPRI-SB94.3: JQ651194, PPRI-SB132.2: JQ651200, PPRI-SB132.5: JQ651201,

PPRI-SB287.3: JQ651369, PPRI-SB287.4: JQ651370, PPRI-SB287.5: JQ651371; Planococcus citri, PPRI-SB18.2: JQ651164, PPRI-SB18.4: JQ651165, PPRI-SB32.1: JQ651169, PPRI-

SB32.3: JQ651170, PPRI-SB32.5: JQ651171, PPRI-SB263.1: JQ651362, PPRI-SB263.2:

JQ651363, PPRI-SB263.3: JQ651364, PPRI-SB263.5: JQ651365; Pseudococcus longispinus,

PPRI-SB54.1: JQ651176, PPRI-SB54.2: JQ651177, PPRI-SB54.3: JQ651178, PPRI-SB54.4:

JQ651179, PPRI-SB54.5: JQ651180; Pseudococcus viburni, PPRI-SB292.1: JQ651372, PPRI-

SB292.2: JQ651373, PPRI-SB292.3: JQ651374, PPRI-SB292.4: JQ651375, PPRI-SB292.5:

JQ651376; Sphaerococcus durus, PPRI-SB247.1: JQ651312; PPRI-SB247.2: JQ651313, PPRI-

SB247.3: JQ651314, PPRI-SB247.4: JQ651315, PPRI-SB247.5: JQ651316, PPRI-SB247.6:

JQ651317, PPRI-SB247.7: JQ651318, PPRI-SB247.8: JQ651319, PPRI-SB247.9: JQ651320,

PPRI-SB247.10: JQ651321; Sphaerococcus species, PPRI-SB183.1: JQ651220, PPRI-SB183.2:

JQ651221, PPRI-SB183.3: JQ651222, PPRI-SB183.4: JQ651223, PPRI-SB183.5: JQ651224;

Vryburgia transvaalensis, PPRI-SB241.1: JQ651294, PPRI-SB241.2: JQ651295, PPRI-SB241.6:

JQ651296, PPRI-SB241.7: JQ651297, PPRI-SB241.8: JQ651298; Pseudococcidae species,

PPRI-SB256.1: JQ651343, PPRI-SB256.3: JQ651345, PPRI-SB256.4: JQ651346, PPRI-

SB256.7: JQ651347, PPRI-SB256.10: JQ651348, PPRI-SB259.1: JQ651358, PPRI-SB259.2:

JQ651359, PPRI-SB259.3: JQ651360, PPRI-SB259.4: JQ651361.

CO1

Asterolecaniidae- Asterolecanium quercicola, PPRI-SB91.6: JN309233, PPRI-SB188.4:

JN309295; Asterolecanium stentae, PPRI-SB224.3: PPRI-SB224.3, PPRI-SB234.1: JN309410,

PPRI-SB234.2: JN309411, PPRI-SB234.3: JN309412, PPRI-SB234.4: JN309413, PPRI-

SB234.5: JN309414, PPRI-SB234.6: JN309415, PPRI-SB234.7: JN309415, PPRI-SB234.8:

168

JN309417, PPRI-SB234.9: JN309418, PPRI-SB234.10: JN309419; Coccidae- Ceroplastes rusci, PPRI-SB249.1: JN309369, PPRI-SB249.2: JN309370, PPRI-SB249.3: JN309371, PPRI-

SB249.4: JN309372, PPRI-SB249.5: JN309373, PPRI-SB249.6: JN309374, PPRI-SB249.7:

JN309375, PPRI-SB249.8: JN309376, PPRI-SB249.9: JN309377; Ceroplastes sinoiae, T2.1:

HQ974583, T2.4: HQ974584; Ceroplastes ficus, PPRI-SB176.1: JN309279, PPRI-SB176.2:

JN309280, PPRI-SB176.3: JN309281, PPRI-SB176.4: JN309282, PPRI-SB176.5: JN309283;

Ceroplastes species, PPRI-SB225.6: HQ974596, PPRI-SB225.7: JN309315, PPRI-SB225.8:

JF883983, PPRI-SB225.10: HQ974597, PPRI-SB193.6: JN309304, PPRI-SB193.7: JN309306,

PPRI-SB246.1: JN309363, PPRI-SB299.1: JN309189, PPRI-SB299.2: JN309179, PPRI-

SB299.3: JN309180, PPRI-SB299.4: JN309181; Ceroplastes destructor, PPRI-SB164.1:

JN309260, JN309260: JN309261, PPRI-SB164.3: JN309262, PPRI-SB164.4: JN309263; Coccus ehretiae, PPRI-SB77.1: JN309226, PPRI-SB77.2: JN309227, PPRI-SB77.3: JN309228, PPRI-

SB77.5: JN309229; Coccus hesperidum, PPRI-SB230.1: JN309169, PPRI-SB230.2: JN309170,

PPRI-SB230.3: JN309171, PPRI-SB230.4: JN309172, PPRI-SB230.5: JN309173, PPRI-

SB230.6: JN309174, PPRI-SB230.7: JN309175, PPRI-SB230.8: JN309176, PPRI-SB230.9:

JN309177, PPRI-SB230.10: HQ974602, Marsipococcus proteae, PPRI-SB231.1: JN309326,

PPRI-SB231.2: JN309327, PPRI-SB231.3: JN309328, PPRI-SB268.1: JN309402, PPRI-

SB268.2: JN309403, PPRI-SB268.3: JN309404, PPRI-SB268.4: JN309405, PPRI-SB268.5:

JN309406; Parasaissetia nigra, PPRI-SB122.7: JN309243, PPRI-SB122.8: JN309244, PPRI-

SB167.1: JN309269, PPRI-SB167.2: JN309270, PPRI-SB167.3: JN309271, PPRI-SB167.4:

JN309272, PPRI-SB167.5: JN309273, PPRI-SB167.6: JN309274, PPRI-SB167.7: JN309275,

PPRI-SB167.8: JN309276, PPRI-SB167.9: JN309277, PPRI-SB167.10: JN309278, PPRI-

SB228.1: JN309316, PPRI-SB228.2: JN309317, PPRI-SB228.3: JN309318, PPRI-SB228.4:

169

JN309319, PPRI-SB228.5: JN309320, PPRI-SB228.6: JN309321, PPRI-SB228.7: JN309322,

PPRI-SB228.8: JN309323, PPRI-SB228.9: JN309324, PPRI-SB228.10: JN309325, PPRI-

SB243.1: JN309353, PPRI-SB243.2: JN309354, PPRI-SB243.3: JN309355, PPRI-SB243.4:

JN309356, PPRI-SB243.5: JN309357, PPRI-SB13.2: JN309196, PPRI-SB13.3: JN30919;

Saissetia species, PPRI-SB3.11: JN309183, PPRI-SB3.12: JN309184, PPRI-SB3.13: JN309185,

PPRI-SB3.14: JN309186, PPRI-SB3.15: JN309187; Dactylopiidae- Dactylopius opuntiae,

PPRI-SB72.4: HQ974576, PPRI-SB72.5: HQ974577, PPRI-SB298.3: JN309242, PPRI-

SB298.4: JN309305, PPRI-SB257.3: JN309388; Dactylopius species, PPRI-SB257.3: JN309388,

PPRI-SB257.4: JN309389, PPRI-SB257.5: JN309390, PPRI-SB257.6: JN309391, PPRI-

SB257.7: JN309392, PPRI-SB257.8: JN309393; Diaspididae- Abgrallaspis cyanophylli, PPRI-

SB242.1: JN309348, PPRI-SB242.2: JN309349, PPRI-SB242.3: JN309350, PPRI-SB242.4:

JN309351, PPRI-SB242.5: JN309352; Aonidia species, PPRI-SB186.1: JN309285, PPRI-

SB186.2: JN309286, PPRI-SB186.3: JN309287, PPRI-SB186.4: JN309288, PPRI-SB186.5:

JN309289; Aonidiella aurantii, PPRI-SB52.1: JN309213, PPRI-SB52.2: JN309214, PPRI-

SB52.3: JN309215, PPRI-SB52.4: JN309216, PPRI-SB52.5: JN309424; Aspidiotus destructor,

PPRI-SB126.1: JN309245, PPRI-SB126.8: JN309246, PPRI-SB126.9: JN309247; Aspidiotus nerii, PPRI-SB6.1: JN309188, PPRI-SB6.3: JN309190, PPRI-SB6.4: JN309191, PPRI-SB6.5:

JN309192, PPRI-SB103.1: JN309236, PPRI-SB103.3: JN309237, PPRI-SB103.4: JN309238,

PPRI-SB190.1: JN309296, PPRI-SB190.2: JN309297, PPRI-SB190.4: JN309298, PPRI-

SB190.5: JN309299, PPRI-SB190.6: JN309160, PPRI-SB195.6: JN309161, PPRI-SB195.7:

JN309162, PPRI-SB195.8: JN309163, PPRI-SB195.9: JN309164; Aulacaspis tubercularis,

PPRI-SB245.1: JN309358, PPRI-SB245.2: JN309359, PPRI-SB245.3: JN309360, PPRI-

SB245.4: JN309361; Diaspis echinocacti, PPRI-SB55.6: JN309148, PPRI-SB55.8: JN309149,

170

PPRI-SB55.9: JN309150; Duplachionaspis species, PPRI-SB191.2: JN309300, PPRI-SB191.6:

JN309301, PPRI-SB191.9: JN309302, PPRI-SB191.11: JN309120, PPRI-SB191.12: JN309121;

Pseudaulacaspis pentagona, PPRI-SB152.2: JN309256, PPRI-SB152.3: JN309257, PPRI-

SB152.4: JN309258, PPRI-SB152.5: JN309259; Separaspis capensis, PPRI-SB289.1:

JN309131, PPRI-SB289.5: JN309134, PPRI-SB290.1: JN309135, PPRI-SB290.2: JN309136,

PPRI-SB290.3: JN309137, PPRI-SB290.4: JN309138; Separaspis proteae, PPRI-SB73.6:

JN309408; Margarodidae- Icerya purchase, PPRI-SB27.3: JN309205, PPRI-SB27.4:

JN309206, PPRI-SB27.5: JN309207; Icerya seychellarum, PPRI-SB238.1: JN309333, PPRI-

SB238.3: JN309334; Ortheziidae- Orthezia insignis, PPRI-SB7.1: JN309193, PPRI-SB7.4:

JN309194, PPRI-SB7.5: JN309195; Outgroup- Megoura nigra: EU071324.1; Pseudococcidae-

Dysmicoccus brevipes, PPRI-SB209.1: JN309312; PPRI-SB209.2: JN309313; Nairobia bifrons,

PPRI-SB202.2: HQ974586; Nipaecoccus nipae, PPRI-SB279.1: JN309116, PPRI-SB279.2:

JN309117, PPRI-SB279.3: JN309118, PPRI-SB279.4: JN309119; Nipaecoccus viridis, PPRI-

SB76.1: HQ974578, PPRI-SB76.2: HQ974579, PPRI-SB76.3: HQ974580, PPRI-SB76.4:

HQ974581; Phenacoccus manihoti, PPRI-SB215.6: JN309165, PPRI-SB215.10: HQ974590,

PPRI-SB215.11: JN309152; Phenacoccus madeirensis, PPRI-SB94.3: JN309234, PPRI-

SB132.2: JN309248, PPRI-SB132.4: JN309249, PPRI-SB132.5: JN309250, PPRI-SB287.1:

JN309126, PPRI-SB287.2: JN309127, PPRI-SB287.3: JN309128, PPRI-SB287.4: JN309129,

PPRI-SB287.5: JN309130.

171

Appendix 4.1: List of Coccoidea species studied, collecting localities and voucher numbers.

Current family Species AccessionNumber Locality Province Latitude Longitude classification Asterolecaniidae Asterolecanium quercicola HEMC01728 Pretoria Gauteng -25.73857 28.20733 Asterolecaniidae Asterolecanium quercicola HEMC01999 East Rand Gauteng -26.0359 28.1929 Asterolecaniidae Asterolecanium quercicola HEMC02161 Pretoria Gauteng -25.73857 28.20733 Asterolecaniidae Asterolecanium quercicola HEMC03110 Pretoria Gauteng -25.73857 28.20733 Asterolecaniidae Asterolecanium quercicola HEMC03413 Thaba Nchu Free Stae -33.96088 25.6216 Asterolecaniidae Asterolecanium quercicola HEMC03746 Pretoria Gauteng -25.73857 28.20733 Asterolecaniidae Asterolecanium quercicola HEMC03884 Johannesburg Gauteng -26.179311 28.042399 Asterolecaniidae Asterolecanium quercicola HEMC06379 Potchefstroom Gauteng -26.7361 27.07553 Asterolecaniidae Asterolecanium quercicola SB188_1 Union building Gauteng -25.74082 28.21171 Asterolecaniidae Asterolecanium quercicola SB188_2 Union building Gauteng -25.74082 28.21171 Coccidae Ceroplastes destructor HEMC00093 Emalahleni Mpumalanga -25.4684 31.04401 Coccidae Ceroplastes destructor HEMC00514 Cape Town Western Cape -34.0289 18.41937 Coccidae Ceroplastes destructor HEMC00515 Cradock Eastern Cape -32.1245 25.642 Coccidae Ceroplastes destructor HEMC00517 Cape Town Western Cape -34.0289 18.41937 Coccidae Ceroplastes destructor HEMC00557 Letaba Mpumalanga -23.8602 30.29855 Coccidae Ceroplastes destructor HEMC01332 Rustenburg North West -25.7239 27.2912 Coccidae Ceroplastes destructor HEMC02076 Emalahleni Mpumalanga -25.4684 31.04401 Coccidae Ceroplastes destructor HEMC02078 Polokwane Limpopo -23.874485 29.464295 Coccidae Ceroplastes destructor HEMC02093 Polokwane Limpopo -23.874485 29.464295 Coccidae Ceroplastes destructor HEMC02098 Kiepersol Gauteng -25.4547 31.00644 Coccidae Ceroplastes destructor HEMC02107 CAPE Point Western Cape -34.35722 18.49750 Coccidae Ceroplastes destructor HEMC02118 Emalahleni Mpumalanga -25.4684 31.04401 Coccidae Ceroplastes destructor HEMC02157 Mookgopong Limpopo -24.4275 28.59419 Coccidae Ceroplastes destructor HEMC02169 Polokwane Limpopo -23.874485 29.464295 Coccidae Ceroplastes destructor HEMC02171 Betty’s Bay Western Cape -34.09106 18.813352 Coccidae Ceroplastes destructor HEMC02196 Simonstown Western Cape -34.171106 18.426863 Coccidae Ceroplastes destructor HEMC02200 Simonstown Western Cape -34.171106 18.426863 Coccidae Ceroplastes destructor HEMC02202 Polokwane Limpopo -23.874485 29.464295

172

Current family Species AccessionNumber Locality Province Latitude Longitude classification Coccidae Ceroplastes destructor HEMC02255 Letaba Mpumalanga -23.8602 30.29855 Coccidae Ceroplastes destructor HEMC02612 Stellenbosch Western Cape -33.936557 18.86755 Coccidae Ceroplastes destructor HEMC02644 Pretoria Gauteng -25.73857 28.20733 Coccidae Ceroplastes destructor HEMC04208 Franschhoek Western Cape -33.9033 19.1572 Coccidae Ceroplastes destructor HEMC07060 Hoedspruit Limpopo -24.35452 30.95959 Coccidae Ceroplastes destructor HEMC07064 Hoedspruit Limpopo -24.35452 30.95959 Coccidae Ceroplastes destructor SB164_1 Roodeplaat Gauteng -25.60398 28.35429 Coccidae Ceroplastes ficus HEMC00102 Pretoria Gauteng -25.73857 28.20733 Coccidae Ceroplastes ficus HEMC00523 Gauteng Gauteng -25.73857 28.20733 Coccidae Ceroplastes ficus HEMC01317 Pretoria Gauteng -25.73857 28.20733 Coccidae Ceroplastes ficus HEMC03309 Pretoria Gauteng -25.73857 28.20733 Coccidae Ceroplastes ficus SB176_1 Rietondale Gauteng -25.73286 28.30494 Coccidae Ceroplastes ficus SB176_2 Rietondale Gauteng -25.73286 28.30494 Coccidae Ceroplastes ficus SB176_3 Rietondale Gauteng -25.73286 28.30494 Coccidae Ceroplastes ficus SB176_4 Rietondale Gauteng -25.73286 28.30494 Coccidae Ceroplastes ficus SB176_5 Rietondale Gauteng -25.73286 28.30494 Coccidae Ceroplastes rusci HEMC00898 Umkomaas KwaZulu-Natal -30.206082 30.795128 Coccidae Ceroplastes rusci HEMC00966 Durban KwaZulu-Natal -29.8458 31.0083 Coccidae Ceroplastes rusci HEMC02094 Worcester Western Cape -33.633122 19.431285 Coccidae Ceroplastes rusci HEMC02152 Worcester Western Cape -33.633122 19.431285 Coccidae Ceroplastes rusci HEMC02177 Polokwane Limpopo -23.874485 29.464295 Coccidae Ceroplastes rusci HEMC02956 Willowmore Western Cape -29.8 30.85 Coccidae Ceroplastes rusci HEMC03285 Stellenbosch Western Cape -33.936557 18.86755 Coccidae Ceroplastes rusci HEMC03454 De Doorns Western Cape -33.695866 19.495344 Coccidae Ceroplastes rusci HEMC03536 Stellenbosch Western Cape -33.936557 18.86755 Coccidae Ceroplastes rusci HEMC03670 Citrusdal Western Cape -32.5946 19.0286 Coccidae Ceroplastes rusci HEMC04119 Durban KwaZulu-Natal -29.8458 31.0083 Coccidae Ceroplastes rusci HEMC04134 Elandsbaai Western Cape -32.3155 18.3966 Coccidae Ceroplastes rusci HEMC04293 Somerset West Western Cape -34.1009 18.8427 Coccidae Ceroplastes rusci HEMC04315 Pretoria Gauteng -25.73857 28.20733 Coccidae Ceroplastes rusci HEMC04343 Pretoria Gauteng -25.73857 28.20733 Coccidae Ceroplastes rusci HEMC04424 Mtubatuba KwaZulu-Natal -28.4771 32.1546

173

Current family Species AccessionNumber Locality Province Latitude Longitude classification Coccidae Ceroplastes rusci HEMC04833 Emalahleni Mpumalanga -25.4684 31.04401 Coccidae Ceroplastes sinoiae HEMC00521 Pretoria Gauteng -25.73857 28.20733 Coccidae Ceroplastes sinoiae HEMC00559 Emalahleni Mpumalanga -25.4684 31.04401 Coccidae Ceroplastes sinoiae HEMC00869 Pretoria Gauteng -25.73857 28.20733 Coccidae Ceroplastes sinoiae HEMC01315 Pretoria Gauteng -25.73857 28.20733 Coccidae Ceroplastes sinoiae HEMC01321 Pretoria Gauteng -25.73857 28.20733 Coccidae Ceroplastes sinoiae HEMC02254 Pretoria Gauteng -25.73857 28.20733 Coccidae Ceroplastes sinoiae HEMC07072 Pretoria Gauteng -25.73857 28.20733 Coccidae Ceroplastes sinoiae T2_1 Pretoria central Gauteng -25.73857 28.20733 Coccidae Coccus ehretiae HEMC00117 Pretoria Gauteng -25.73857 28.20733 Coccidae Coccus ehretiae HEMC00713 Kuruman Northern Cape -27.473730 24.434667 Coccidae Coccus ehretiae HEMC00720 Pretoria Gauteng -25.73857 28.20733 Coccidae Coccus ehretiae HEMC01225 Pretoria Gauteng -25.73857 28.20733 Coccidae Coccus ehretiae HEMC01322 Pretoria Gauteng -25.73857 28.20733 Coccidae Coccus ehretiae HEMC01324 Pretoria Gauteng -25.73857 28.20733 Coccidae Coccus ehretiae HEMC01401 Pienaars River Limpopo -23.6921 27.6829 Coccidae Coccus ehretiae HEMC02176 Polokwane Limpopo -23.874485 29.464295 Coccidae Coccus ehretiae HEMC03315 Krugersdorp North West -26.106771 27.724278 Coccidae Coccus ehretiae HEMC03621 Grahamstown Eastern Cape -33,30271 26.52383 Coccidae Coccus ehretiae HEMC04263 Pretoria Gauteng -25.73857 28.20733 Coccidae Coccus ehretiae HEMC04328 Irene Gauteng -25.87907 28.20743 Coccidae Coccus ehretiae HEMC04827 Humansdorp Eastern Cape -33.9371 24.941 Coccidae Coccus ehretiae HEMC05330 Grahamstown Eastern Cape -33,30271 26.52383 Coccidae Coccus ehretiae HEMC05456 Zeerust North West -25.53293 26.06376 Coccidae Coccus ehretiae SB77_1 Ferie Glen Gauteng -25.79878 28.32704 Coccidae Coccus hesperidum HEMC00136 Pretoria Gauteng -25.73857 28.20733 Coccidae Coccus hesperidum HEMC00533 Pretoria Gauteng -25.73857 28.20733 Coccidae Coccus hesperidum HEMC00562 Fort Beaufort Eastern Cape -32.7833 26.63333 Coccidae Coccus hesperidum HEMC00718 Pretoria Gauteng -25.73857 28.20733 Coccidae Coccus hesperidum HEMC00773 Addo Eastern Cape -33.5685 25.69216 Coccidae Coccus hesperidum HEMC00775 Port Elizabeth Eastern Cape -33.93264 25.56995 Coccidae Coccus hesperidum HEMC00780 Addo Eastern Cape -33.5685 25.69216

174

Current family Species AccessionNumber Locality Province Latitude Longitude classification Coccidae Coccus hesperidum HEMC00817 Johannesburg Gauteng -26.179311 28.042399 Coccidae Coccus hesperidum HEMC00835 Pretoria Gauteng -25.73857 28.20733 Coccidae Coccus hesperidum HEMC00929 Worcester Western Cape -33.633122 19.431285 Coccidae Coccus hesperidum HEMC00987 Brandvlei Western Cape -33.736046 19.414215 Coccidae Coccus hesperidum HEMC00988 Pretoria Gauteng -25.73857 28.20733 Coccidae Coccus hesperidum HEMC01065 Grahamstown Eastern Cape -33,30271 26.52383 Coccidae Coccus hesperidum HEMC01076 Stellenbosch Western Cape -33.936557 18.86755 Coccidae Coccus hesperidum HEMC01086 Stellenbosch Western Cape -33.936557 18.86755 Coccidae Coccus hesperidum HEMC01161 Port Elizabeth Eastern Cape -33.93264 25.56995 Coccidae Coccus hesperidum HEMC01165 Pretoria Gauteng -25.73857 28.20733 Coccidae Coccus hesperidum HEMC01222 Durban KwaZulu-Natal -29.8458 31.0083 Coccidae Coccus hesperidum HEMC01226 Port Elizabeth Eastern Cape -33.93264 25.56995 Coccidae Coccus hesperidum HEMC01231 Runtenburg North West -25.7239 27.2912 Coccidae Coccus hesperidum HEMC01242 Tzaneen Limpopo -23.816924 30.303665 Coccidae Coccus hesperidum HEMC01277 Pretoria Gauteng -25.73857 28.20733 Coccidae Coccus hesperidum HEMC01303 Pretoria Gauteng -25.73857 28.20733 Coccidae Coccus hesperidum HEMC02026 Pretoria Gauteng -25.73857 28.20733 Coccidae Coccus hesperidum HEMC02028 Pretoria Gauteng -25.73857 28.20733 Coccidae Coccus hesperidum HEMC02257 Durban KwaZulu-Natal -29.8458 31.0083 Coccidae Coccus hesperidum HEMC02736 Kleinmond Western Cape -34.26315 19.18767 Coccidae Coccus hesperidum HEMC03199 Tzaneen Limpopo -23.816924 30.303665 Coccidae Coccus hesperidum HEMC03207 Pretoria Gauteng -25.73857 28.20733 Coccidae Coccus hesperidum HEMC03350 Port Elizabeth Eastern Cape -33.93264 25.56995 Coccidae Coccus hesperidum HEMC03383 Kirstenbosch Western Cape -26.0321 28.0405 Coccidae Coccus hesperidum HEMC03392 Port Elizabeth Eastern Cape -33.93264 25.56995 Coccidae Coccus hesperidum HEMC03440 Paarl Western Cape -33.731986 18.977615 Coccidae Coccus hesperidum HEMC03625 Stellenbosch Western Cape -33.936557 18.86755 Coccidae Coccus hesperidum HEMC03657 Louis Trichardt Limpopo -23.0468 29.9196 Coccidae Coccus hesperidum HEMC04212 Pretoria Gauteng -25.73857 28.20733 Coccidae Coccus hesperidum HEMC04244 Amanzimtoti KwaZulu-Natal -30.0522 30.855 Coccidae Coccus hesperidum HEMC04301 Caledon Western Cape -34.332488 19.694723 Coccidae Coccus hesperidum HEMC04369 Piettenberg Bay Western Cape -34.07834 23.370122

175

Current family Species AccessionNumber Locality Province Latitude Longitude classification Coccidae Coccus hesperidum HEMC04375 Wolseley Western Cape -33.3850 19.1928 Coccidae Coccus hesperidum HEMC04560 Pretoria Gauteng -25.73857 28.20733 Coccidae Coccus hesperidum HEMC04780 Emalahleni Mpumalanga -25.4684 31.04401 Coccidae Coccus hesperidum HEMC04798 Stellenbosch Western Cape -33.936557 18.86755 Coccidae Coccus hesperidum HEMC04799 Emalahleni Mpumalanga -25.4684 31.04401 Coccidae Coccus hesperidum HEMC04958 Pietermaritzburg KwaZulu-Natal -29.6009 30.3681 Coccidae Coccus hesperidum HEMC05133 Krugersdorp North West -26.106771 27.724278 Coccidae Coccus hesperidum HEMC05164 Stellenbosch Western Cape -33.936557 18.86755 Coccidae Coccus hesperidum HEMC05168 Cape Town Western Cape -34.0289 18.41937 Coccidae Coccus hesperidum HEMC05171 Stellenbosch Western Cape -33.936557 18.86755 Coccidae Coccus hesperidum HEMC05180 Oudtshoorn Western Cape -33.3922 22.2150 Coccidae Coccus hesperidum HEMC05233 Rosebank Gauteng -26.143708 28.042513 Coccidae Coccus hesperidum HEMC05237 Namaqualand Northern Cape -31.6096 187361 Coccidae Coccus hesperidum HEMC05251 Uitenhage Eastern Cape -33.763741 25.393641 Coccidae Coccus hesperidum HEMC05253 Umtata Eastern Cape -33.763741 25.393641 Coccidae Coccus hesperidum HEMC05255 King Williams Town Eastern Cape -32.875909 27.390604 Coccidae Coccus hesperidum HEMC05259 Uitenhage Eastern Cape -33.763741 25.393641 Coccidae Coccus hesperidum HEMC05266 Messina Limpopo -22.229833 29.635333 Coccidae Coccus hesperidum HEMC05287 Steytlerville Western Cape -33.332601 24.346050 Coccidae Coccus hesperidum HEMC05322 Port Elizabeth Eastern Cape -33.93264 25.56995 Coccidae Coccus hesperidum HEMC05378 Stellenbosch Western Cape -33.936557 18.86755 Coccidae Coccus hesperidum HEMC05382 Stellenbosch Western Cape -33.936557 18.86755 Coccidae Coccus hesperidum HEMC05401 Stellenbosch Western Cape -33.936557 18.86755 Coccidae Coccus hesperidum HEMC05416 Grahamstown Eastern Cape -33,30271 26.52383 Coccidae Coccus hesperidum HEMC05441 Emalahleni Mpumalanga -25.4684 31.04401 Coccidae Coccus hesperidum HEMC05450 Swartruggens North West -25.65208 26.68347 Coccidae Coccus hesperidum HEMC05464 Stellenbosch Western Cape -33.936557 18.86755 Coccidae Coccus hesperidum HEMC05465 Paarl Western Cape -33.731986 18.977615 Coccidae Coccus hesperidum HEMC05499 Kirstenbosch Western Cape -26.0321 28.0405 Coccidae Coccus hesperidum HEMC05521 Stellenbosch Western Cape -33.936557 18.86755 Coccidae Coccus hesperidum HEMC05674 Kareedouw Eastern Cape -33.9906 24.2134 Coccidae Coccus hesperidum HEMC05715 Gansbaai Western Cape -34.579085 19.443410

176

Current family Species AccessionNumber Locality Province Latitude Longitude classification Coccidae Coccus hesperidum HEMC05947 Grahamstown Eastern Cape -33,30271 26.52383 Coccidae Coccus hesperidum HEMC06375 Roodeplaat Gauteng -25.60398 28.35429 Coccidae Coccus hesperidum HEMC06431 Hoedspruit Limpopo -24.35452 30.95959 Coccidae Coccus hesperidum HEMC06432 Burgersfort Limpopo -24.6736 30.3292 Coccidae Coccus hesperidum HEMC06463 Pretoria Gauteng -25.73857 28.20733 Coccidae Coccus hesperidum HEMC06475 Nkwalini Valley KwaZulu-Natal -28.726180 31.525150 Coccidae Coccus hesperidum HEMC06604 Pretoria Gauteng -25.73857 28.20733 Coccidae Coccus hesperidum HEMC06605 Pretoria Gauteng -25.73857 28.20733 Coccidae Coccus hesperidum HEMC06622 Buffelspoort Station North West -25.826260 27.42036 Coccidae Coccus hesperidum HEMC06646 Pretoria Gauteng -25.73857 28.20733 Coccidae Coccus hesperidum HEMC06728 Pretoria Gauteng -25.73857 28.20733 Coccidae Coccus hesperidum HEMC06739 Robertson Western Cape -25.336579 27.178802 Coccidae Coccus hesperidum HEMC06741 Stellenbosch Western Cape -33.936557 18.86755 Coccidae Coccus hesperidum HEMC06743 Stellenbosch Western Cape -33.936557 18.86755 Coccidae Coccus hesperidum HEMC06748 Swellendam Western Cape -34.024263 20.449523 Coccidae Coccus hesperidum HEMC06896 Buffeljagsrivier Western Cape -34.023871 20.460685 Coccidae Coccus hesperidum HEMC07010 Worcester Western Cape -33.633122 19.431285 Coccidae Coccus hesperidum SB230_1 Pretoria central Gauteng -25.73857 28.20733 Coccidae Marsipococcus proteae HEMC00108 Pretoria Gauteng -25.73857 28.20733 Coccidae Marsipococcus proteae HEMC00750 Cape Town Western Cape -34.0289 18.41937 Coccidae Marsipococcus proteae HEMC00767 Cape Point Western Cape -34.35722 18.49750 Coccidae Marsipococcus proteae HEMC00829 Johannesburg Gauteng -26.179311 28.042399 Coccidae Marsipococcus proteae HEMC00945 Stellenbosch Western Cape -33.936557 18.86755 Coccidae Marsipococcus proteae HEMC00964 Cape Point Western Cape -34.35722 18.49750 Coccidae Marsipococcus proteae HEMC00965 Cape Point Western Cape -34.35722 18.49750 Coccidae Marsipococcus proteae HEMC01090 Stellenbosch Western Cape -33.936557 18.86755 Coccidae Marsipococcus proteae HEMC01108 Johannesburg Gauteng -26.179311 28.042399 Coccidae Marsipococcus proteae HEMC01309 Pretoria Gauteng -25.73857 28.20733 Coccidae Marsipococcus proteae HEMC01327 Pretoria Gauteng -25.73857 28.20733 Coccidae Marsipococcus proteae HEMC03576 Brandvlei Western Cape -33.736046 19.414215 Coccidae Marsipococcus proteae HEMC04122 Leipoldtville Western Cape -32.22372 18.47884 Coccidae Marsipococcus proteae HEMC04271 Robertson Western Cape -25.336579 27.178802

177

Current family Species AccessionNumber Locality Province Latitude Longitude classification Coccidae Marsipococcus proteae HEMC04431 Citrusdal Western Cape -32.5946 19.0286 Coccidae Marsipococcus proteae HEMC05017 Stellenbosch Western Cape -33.936557 18.86755 Coccidae Marsipococcus proteae HEMC05085 Somerset west Western Cape -34.1009 18.8427 Coccidae Marsipococcus proteae HEMC05093 Villiersdorp Western Cape -33.684696 18.933101 Coccidae Marsipococcus proteae HEMC05353 Riversonderend Western Cape -34.1481 19.9028 Coccidae Marsipococcus proteae HEMC05458 Hermanus Western Cape -34.42041 19.24101 Coccidae Marsipococcus proteae HEMC05617 Greyton Western Cape -34.0471 19.6077 Coccidae Marsipococcus proteae HEMC05655 N. of Clanwilliam Western Cape -32.119946 19.059219 Coccidae Marsipococcus proteae HEMC06889 Botanical Garden Gauteng -26.08716 27.84463 Coccidae Marsipococcus proteae SB231_1 Gauteng Gauteng -26.08716 27.84463 Coccidae Marsipococcus proteae SB268_1 Letsitele Limpopo -23.816924 30.303665 Coccidae Parasaissetia nigra (-) Durban KwaZulu-Natal -29.8458 31.0083 Coccidae Parasaissetia nigra HEMC00103 East London Eastern Cape -33.93197 18.4708 Coccidae Parasaissetia nigra HEMC00311 Pretoria Gauteng -25.73857 28.20733 Coccidae Parasaissetia nigra HEMC00439 Emalahleni Mpumalanga -25.4684 31.04401 Coccidae Parasaissetia nigra HEMC00553 Stellenbosch Western Cape -33.936557 18.86755 Coccidae Parasaissetia nigra HEMC00695 Babanango KwaZulu-Natal -28.78072 32.03828 Coccidae Parasaissetia nigra HEMC00700 Pretoria Gauteng -25.73857 28.20733 Coccidae Parasaissetia nigra HEMC00703 Baviaanspoort Gauteng -25.097470 29.98281 Coccidae Parasaissetia nigra HEMC00704 Citrusdal Western Cape -32.5946 19.0286 Coccidae Parasaissetia nigra HEMC00715 Emalahleni Mpumalanga -25.4684 31.04401 Coccidae Parasaissetia nigra HEMC00735 Magoebaskloof Limpopo -23.888611 29.996111 Coccidae Parasaissetia nigra HEMC00770 Pretoria Gauteng -25.73857 28.20733 Coccidae Parasaissetia nigra HEMC00771 Durban KwaZulu-Natal -29.8458 31.0083 Coccidae Parasaissetia nigra HEMC00853 Umkomaas KwaZulu-Natal -30.206082 30.795128 Coccidae Parasaissetia nigra HEMC00857 Pretoria Gauteng -25.73857 28.20733 Coccidae Parasaissetia nigra HEMC00867 Pretoria Gauteng -25.73857 28.20733 Coccidae Parasaissetia nigra HEMC00911 Pretoria Gauteng -25.73857 28.20733 Coccidae Parasaissetia nigra HEMC00917 Pretoria Gauteng -25.73857 28.20733 Coccidae Parasaissetia nigra HEMC01007 Umkomaas KwaZulu-Natal -30.206082 30.795128 Coccidae Parasaissetia nigra HEMC01015 Runtenburg North West -25.7239 27.2912 Coccidae Parasaissetia nigra HEMC01018 Tsitsikama Western Cape -33.82676 23.71549

178

Current family Species AccessionNumber Locality Province Latitude Longitude classification Coccidae Parasaissetia nigra HEMC01081 Mookgopong Limpopo -24.4275 28.59419 Coccidae Parasaissetia nigra HEMC01082 Pretoria Gauteng -25.73857 28.20733 Coccidae Parasaissetia nigra HEMC01083 Baviaanspoort Gauteng -25.097470 29.98281 Coccidae Parasaissetia nigra HEMC01084 Pretoria Gauteng -25.73857 28.20733 Coccidae Parasaissetia nigra HEMC01180 Polokwane Limpopo -23.8745 29.4643 Coccidae Parasaissetia nigra HEMC01311 Pretoria Gauteng -25.73857 28.20733 Coccidae Parasaissetia nigra HEMC01316 Pretoria Gauteng -25.73857 28.20733 Coccidae Parasaissetia nigra HEMC01923 Pretoria Gauteng -25.73857 28.20733 Coccidae Parasaissetia nigra HEMC01997 Skukuza Mpumalanga -24.9898 31.5926 Coccidae Parasaissetia nigra HEMC02027 Polokwane Limpopo -23.874485 29.464295 Coccidae Parasaissetia nigra HEMC02174 Durban KwaZulu-Natal -29.8458 31.0083 Coccidae Parasaissetia nigra HEMC02256 Mafikeng North West -25.8554 25.6414 Coccidae Parasaissetia nigra HEMC02631 Oribi Gorge KwaZulu-Natal -30.6998 30.2733 Coccidae Parasaissetia nigra HEMC04728 Lower sabie Mpumalanga -25.1198 31.9159 Coccidae Parasaissetia nigra HEMC04947 Roodeplaat Gauteng -25.60398 28.35429 Coccidae Parasaissetia nigra HEMC05630 Klipheuwel Western Cape -33.7913 18.69916 Coccidae Parasaissetia nigra HEMC05669 Pretoria Gauteng -25.73857 28.20733 Coccidae Parasaissetia nigra HEMC06668 Worcester Western Cape -33.633122 19.431285 Coccidae Parasaissetia nigra HEMC06687 Pietermaritzburg KwaZulu-Natal -29.6009 30.3681 Coccidae Parasaissetia nigra HEMC06723 Worcester Western Cape -33.633122 19.431285 Coccidae Parasaissetia nigra HEMC06742 Stellenbosch Western Cape -33.936557 18.86755 Coccidae Parasaissetia nigra HEMC06744 Swellendam Western Cape -34.024263 20.449523 Coccidae Parasaissetia nigra HEMC06749 Vredendal Western Cape 31.6658 18.5066 Coccidae Parasaissetia nigra HEMC06883 Worcester Western Cape -33.633122 19.431285 Coccidae Parasaissetia nigra HEMC07011 Rietondale Gauteng -25.73286 28.30494 Coccidae Parasaissetia nigra HEMC07104 Rietondale Gauteng -25.73286 28.30494 Coccidae Parasaissetia nigra SB122_7 Roodeplaat Gauteng -25.60398 28.35429 Coccidae Parasaissetia nigra SB167_1 Rietondale Gauteng -25.73286 28.30494 Coccidae Protopulvinaria pyriformis (-) Pietermaritzburg KwaZulu-Natal -29.6009 30.3681 Coccidae Protopulvinaria pyriformis HEMC00077 Umkomaas KwaZulu-Natal -30.206082 30.795128 Coccidae Protopulvinaria pyriformis HEMC00447 Emalahleni Mpumalanga -25.4684 31.04401 Coccidae Protopulvinaria pyriformis HEMC00701 Durban KwaZulu-Natal -29.8458 31.0083

179

Current family Species AccessionNumber Locality Province Latitude Longitude classification Coccidae Protopulvinaria pyriformis HEMC00902 Stellenbosch Western Cape -33.936557 18.86755 Coccidae Protopulvinaria pyriformis HEMC00944 Mooketsi Limpopo -23.556405 30.145047 Coccidae Protopulvinaria pyriformis HEMC01166 Stellenbosch Western Cape -33.936557 18.86755 Coccidae Protopulvinaria pyriformis HEMC02050 Paarl Western Cape -33.731986 18.977615 Coccidae Protopulvinaria pyriformis HEMC05209 Stellenbosch Western Cape -33.936557 18.86755 Coccidae Protopulvinaria pyriformis HEMC05215 Durban KwaZulu-Natal -29.8458 31.0083 Coccidae Protopulvinaria pyriformis HEMC05227 Roodeplaat Gauteng -25.60398 28.35429 Coccidae Protopulvinaria pyriformis HEMC05265 Stellenbosch Western Cape -33.936557 18.86755 Coccidae Protopulvinaria pyriformis HEMC05452 Stellenbosch Western Cape -33.936557 18.86755 Coccidae Protopulvinaria pyriformis HEMC05467 Stellenbosch Western Cape -33.936557 18.86755 Coccidae Protopulvinaria pyriformis HEMC06286 Pretoria Gauteng -25.73857 28.20733 Coccidae Protopulvinaria pyriformis HEMC06402 Roodeplaat Gauteng -25.60398 28.35429 Coccidae Pulvinaria iceryi (-) Pretoria Gauteng -25.73857 28.20733 Coccidae Pulvinaria iceryi HEMC00813 Durban KwaZulu-Natal -29.8458 31.0083 Coccidae Pulvinaria iceryi HEMC05077 Onderstepoort Gauteng -26.0321 28.0405 Coccidae Pulvinaria iceryi HEMC06333 Roodeplaat Gauteng -25.60398 28.35429 Coccidae Pulvinaria iceryi SB219_1 Roodeplaat Gauteng -25.60398 28.35429 Coccidae Pulvinaria iceryi SB219_2 Roodeplaat Gauteng -25.60398 28.35429 Coccidae Pulvinaria iceryi SB219_3 Roodeplaat Gauteng -25.60398 28.35429 Coccidae Pulvinaria iceryi SB219_4 Roodeplaat Gauteng -25.60398 28.35429 Coccidae Saissetia coffeae (-) Pretoria Gauteng -25.73857 28.20733 Coccidae Saissetia coffeae HEMC00120 Pietermaritzburg KwaZulu-Natal -29.6009 30.3681 Coccidae Saissetia coffeae HEMC00894 Stellenbosch Western Cape -33.936557 18.86755 Coccidae Saissetia coffeae HEMC02051 Cape Town Western Cape -34.0289 18.41937 Coccidae Saissetia coffeae HEMC02805 Stellenbosch Western Cape -33.936557 18.86755 Coccidae Saissetia coffeae HEMC02983 Hout Bay Western Cape -34.0460 18.3652 Coccidae Saissetia coffeae HEMC00933 Belville Western Cape -33.8705 18.6067 Coccidae Saissetia coffeae HEMC05372 Johannesburg Gauteng -26.179311 28.042399 Coccidae Saissetia coffeae HEMC05501 Cape Town Western Cape -34.0289 18.41937 Coccidae Saissetia coffeae HEMC05558 Nkwalini Valley KwaZulu-Natal -28.726180 31.525150 Coccidae Saissetia coffeae HEMC06314 Lydenburg District Mpumalanga -25.0957 30.4555 Coccidae Saissetia coffeae HEMC06315 Rietondale Gauteng -25.73286 28.30494

180

Current family Species AccessionNumber Locality Province Latitude Longitude classification Dactylopiidae Dactylopius opuntiae HEMC00419 Mookgopong Limpopo -24.4275 28.59419 Dactylopiidae Dactylopius opuntiae HEMC00525 Pretoria Gauteng -25.73857 28.20733 Dactylopiidae Dactylopius opuntiae HEMC00575 Pretoria Gauteng -25.73857 28.20733 Dactylopiidae Dactylopius opuntiae HEMC01131 Mookgopong Limpopo -24.4275 28.59419 Dactylopiidae Dactylopius opuntiae HEMC02005 Uitenhage Eastern Cape -33.763741 25.393641 Dactylopiidae Dactylopius opuntiae HEMC02030 Nylstroom Limpopo -24.7453 28.6675 Dactylopiidae Dactylopius opuntiae HEMC02033 Mookgopong Limpopo -24.4275 28.59419 Dactylopiidae Dactylopius opuntiae HEMC02043 Uitenhage Eastern Cape -33.763741 25.393641 Dactylopiidae Dactylopius opuntiae HEMC02091 Bloemfontein Free state -33.93197 18.47081 Dactylopiidae Dactylopius opuntiae HEMC02140 Graaff-Reinet Eastern Cape -32.2527 24.5324 Dactylopiidae Dactylopius opuntiae HEMC02153 Fort Beaufort Eastern Cape -32.7833 26.63333 Dactylopiidae Dactylopius opuntiae HEMC02173 Addo Eastern Cape -33.5685 25.69216 Dactylopiidae Dactylopius opuntiae HEMC02175 Fort Beaufort Eastern Cape -32.7833 26.63333 Dactylopiidae Dactylopius opuntiae HEMC02187 Graaff-Reinet Eastern Cape -32.2527 24.5324 Dactylopiidae Dactylopius opuntiae HEMC02195 Fort Beaufort Eastern Cape -32.7833 26.63333 Dactylopiidae Dactylopius opuntiae HEMC02205 Graaff-Reinet Eastern Cape -32.2527 24.5324 Dactylopiidae Dactylopius opuntiae HEMC02226 Bedford Eastern Cape -32.41345 26.07353 Dactylopiidae Dactylopius opuntiae HEMC02296 Grahamstown Eastern Cape -33,30271 26.52383 Dactylopiidae Dactylopius opuntiae HEMC02313 Windburg Free state - 28.513333 27.010278 Dactylopiidae Dactylopius opuntiae HEMC02315 Parys Free state -26.860667 27.287567 Dactylopiidae Dactylopius opuntiae HEMC02472 Heidelberg, Gauteng Gauteng -26.503840 28.351140 Dactylopiidae Dactylopius opuntiae HEMC02474 Bloemfontein Free state -33.93197 18.47081 Dactylopiidae Dactylopius opuntiae HEMC02551 Parys Free state -26.860667 27.287567 Dactylopiidae Dactylopius opuntiae HEMC02553 Brandfort Free state -28.6983 26.4591 Dactylopiidae Dactylopius opuntiae HEMC02564 Kroonstad Free state -27.553 27.6728 Dactylopiidae Dactylopius opuntiae HEMC02611 Grahamstown Eastern Cape -33,30271 26.52383 Dactylopiidae Dactylopius opuntiae HEMC02735 Klerksdorp North West -26.8635 26.661 Dactylopiidae Dactylopius opuntiae HEMC02737 Vryburg North West -26.958429 24.72976 Dactylopiidae Dactylopius opuntiae HEMC02755 Mossel Bay Western Cape -34.148109 22.101465 Dactylopiidae Dactylopius opuntiae HEMC02763 Barkly West Northern Cape - 28.083333 24.516667 Dactylopiidae Dactylopius opuntiae HEMC02765 Mafikeng North West -25.8554 25.6414 Dactylopiidae Dactylopius opuntiae HEMC02768 Wolmaransstad Northern Cape -27.20.87 25.9778

181

Current family Species AccessionNumber Locality Province Latitude Longitude classification Dactylopiidae Dactylopius opuntiae HEMC02771 Bloemhof Northern Cape -27.06415 25..60619 Dactylopiidae Dactylopius opuntiae HEMC02777 Potchefstroom Northern Cape -26.7361 27.07553 Dactylopiidae Dactylopius opuntiae HEMC02789 Kuruman Northern Cape -27.473730 24.434667 Dactylopiidae Dactylopius opuntiae HEMC02793 Kimberley Northern Cape -28.7347 24.7606 Dactylopiidae Dactylopius opuntiae HEMC02802 Zeerust North West -25.53293 26.06376 Dactylopiidae Dactylopius opuntiae HEMC02813 Setlagole North West -27.531488 24.785714 Dactylopiidae Dactylopius opuntiae HEMC02819 Warrenton Northern Cape -28.117 24.850 Dactylopiidae Dactylopius opuntiae HEMC02820 Groot Marico North West -25.60000 26.41667 Dactylopiidae Dactylopius opuntiae HEMC02935 Cradock Eastern Cape -32.1245 25.642 Dactylopiidae Dactylopius opuntiae HEMC05213 Uitenhage Eastern Cape -33.763741 25.393641 Dactylopiidae Dactylopius opuntiae SB72_6 Rietondale Gauteng -25.73286 28.30494 Diaspididae Abgrallaspis cyanophylli HEMC04793 Long Tom Pass Mpumalanga -25.1477 30.6204 Diaspididae Abgrallaspis cyanophylli HEMC06133 Emalahleni Mpumalanga -25.4684 31.04401 Diaspididae Abgrallaspis cyanophylli HEMC06623 Buffelspoort Station North West -25.826260 27.42036 Diaspididae Abgrallaspis cyanophylli HEMC07097 Rietondale Gauteng -25.73286 28.30494 Diaspididae Abgrallaspis cyanophylli HEMC07102 Rietondale Gauteng -25.73286 28.30494 Diaspididae Abgrallaspis cyanophylli SB242_1 Rietondale Gauteng -25.73286 28.30494 Diaspididae Abgrallaspis cyanophylli SB242_2 Rietondale Gauteng -25.73286 28.30494 Diaspididae Abgrallaspis cyanophylli SB242_3 Rietondale Gauteng -25.73286 28.30494 Diaspididae Abgrallaspis cyanophylli SB242_4 Rietondale Gauteng -25.73286 28.30494 Diaspididae Abgrallaspis cyanophylli SB242_5 Rietondale Gauteng -25.73286 28.30494 Diaspididae Aonidiella aurantii HEMC00483 Pretoria Gauteng -25.73857 28.20733 Diaspididae Aonidiella aurantii HEMC00508 Pietermaritzburg KwaZulu-Natal -29.6009 30.3681 Diaspididae Aonidiella aurantii HEMC01272 Messina Limpopo -22.229833 29.635333 Diaspididae Aonidiella aurantii HEMC01280 Addo Eastern Cape -33.5685 25.69216 Diaspididae Aonidiella aurantii HEMC01281 Citrusdal Western Cape -32.5946 19.0286 Diaspididae Aonidiella aurantii HEMC01756 Stellenbosch Western Cape -33.936557 18.86755 Diaspididae Aonidiella aurantii HEMC01805 Franschhoek Western Cape -33.9033 19.1572 Diaspididae Aonidiella aurantii HEMC01840 Stellenbosch Western Cape -33.936557 18.86755 Diaspididae Aonidiella aurantii HEMC02221 Groblersdal Limpopo -25.134 29.4221 Diaspididae Aonidiella aurantii HEMC03052 Letaba Mpumalanga -23.8602 30.29855 Diaspididae Aonidiella aurantii HEMC03063 Malelane Mpumalanga -25.2905 31.3136

182

Current family Species AccessionNumber Locality Province Latitude Longitude classification Diaspididae Aonidiella aurantii HEMC03159 Stellenbosch Western Cape -33.936557 18.86755 Diaspididae Aonidiella aurantii HEMC05285 Steytlerville Eastern Cape -33.332601 24.346050 Diaspididae Aonidiella aurantii HEMC05740 Buffelspoort North West -25.826260 27.42036 Diaspididae Aonidiella aurantii HEMC05916 Groblersdal Limpopo -25.134 29.4221 Diaspididae Aonidiella aurantii HEMC05927 Letsitele Limpopo -23.9056 30.3633 Diaspididae Aonidiella aurantii HEMC05928 Letsitele Limpopo -23.9056 30.3633 Diaspididae Aonidiella aurantii HEMC05929 Patensie Eastern Cape -28.3427 27.3234 Diaspididae Aonidiella aurantii HEMC05940 Letaba Estate Mpumalanga -23.8602 30.29855 Diaspididae Aonidiella aurantii HEMC05982 Letaba Estate Mpumalanga -23.8602 30.29855 Diaspididae Aonidiella aurantii HEMC05999 Letaba Estate Mpumalanga -23.8602 30.29855 Diaspididae Aonidiella aurantii HEMC06005 Pretoria Gauteng -25.73857 28.20733 Diaspididae Aonidiella aurantii HEMC06006 Letaba Mpumalanga -23.8602 30.29855 Diaspididae Aonidiella aurantii HEMC06007 Emalahleni Mpumalanga -25.4684 31.04401 Diaspididae Aonidiella aurantii HEMC06076 Clanwilliam Western Cape -32.119946 19.059219 Diaspididae Aonidiella aurantii HEMC06245 Cape Town Western Cape -34.0289 18.41937 Diaspididae Aonidiella aurantii HEMC06248 Eshowe KwaZulu-Natal -28.7592 31.3845 Diaspididae Aonidiella aurantii HEMC06249 Hazyview Mpumalanga -25.0366 31.2001 Diaspididae Aonidiella aurantii HEMC06288 Pretoria Gauteng -25.73857 28.20733 Diaspididae Aonidiella aurantii HEMC06349 Tzaneen Limpopo -23.816924 30.303665 Diaspididae Aonidiella aurantii HEMC06351 Letsitele Limpopo -23.9056 30.3633 Diaspididae Aonidiella aurantii HEMC06500 Paarl Western Cape -33.731986 18.977615 Diaspididae Aonidiella aurantii HEMC06619 Citrusdal Western Cape -32.5946 19.0286 Diaspididae Aonidiella aurantii HEMC06648 Paarl Western Cape -33.731986 18.977615 Diaspididae Aonidiella aurantii HEMC06654 Paarl Western Cape -33.731986 18.977615 Diaspididae Aonidiella aurantii HEMC06733 Groblersdal Limpopo -25.134 29.4221 Diaspididae Aonidiella aurantii HEMC06754 Worcester Western Cape -33.633122 19.431285 Diaspididae Aonidiella aurantii HEMC06833 Stellenbosch Western Cape -33.936557 18.86755 Diaspididae Aonidiella aurantii HEMC06834 Stellenbosch Western Cape -33.936557 18.86755 Diaspididae Aonidiella aurantii HEMC06985 Robertson Western Cape -25.336579 27.178802 Diaspididae Aonidiella aurantii HEMC07084 Polokwane Limpopo -23.8745 29.4643 Diaspididae Aonidiella aurantii SB52_3 Rietondale Gauteng -25.73286 28.30494 Diaspididae Aspidiotus destructor HEMC00159 Durban KwaZulu-Natal -29.8458 31.0083

183

Current family Species AccessionNumber Locality Province Latitude Longitude classification Diaspididae Aspidiotus destructor HEMC01476 Durban KwaZulu-Natal -29.8458 31.0083 Diaspididae Aspidiotus destructor HEMC04593 Makatini Exp Farm KwaZulu-Natal -27.0813 32.3385 Diaspididae Aspidiotus destructor HEMC06376 Komatipoort Limpopo -25.4223 31.9441 Diaspididae Aspidiotus destructor SB126_1 Rietondale Gauteng -25.73286 28.30494 Diaspididae Aspidiotus destructor SB126_2 Rietondale Gauteng -25.73286 28.30494 Diaspididae Aspidiotus destructor SB126_3 Rietondale Gauteng -25.73286 28.30494 Diaspididae Aspidiotus destructor SB126_4 Rietondale Gauteng -25.73286 28.30494 Diaspididae Aspidiotus destructor SB126_5 Rietondale Gauteng -25.73286 28.30494 Diaspididae Aspidiotus destructor SB126_8 Rietondale Gauteng -25.73286 28.30494 Diaspididae Aspidiotus nerii HEMC00195 Pretoria Gauteng -25.73857 28.20733 Diaspididae Aspidiotus nerii HEMC00531 Pretoria Gauteng -25.73857 28.20733 Diaspididae Aspidiotus nerii HEMC00632 Stellenbosch Western Cape -33.936557 18.86755 Diaspididae Aspidiotus nerii HEMC00633 Stellenbosch Western Cape -33.936557 18.86755 Diaspididae Aspidiotus nerii HEMC01130 Pretoria Gauteng -25.73857 28.20733 Diaspididae Aspidiotus nerii HEMC01133 Pretoria Gauteng -25.73857 28.20733 Diaspididae Aspidiotus nerii HEMC01398 Port Elizabeth Eastern Cape -33.93264 25.56995 Diaspididae Aspidiotus nerii HEMC01409 Pretoria Gauteng -25.7241 28.1398 Diaspididae Aspidiotus nerii HEMC01424 Congoskraal Eastern Cape -33.6483 25.9908 Diaspididae Aspidiotus nerii HEMC01527 Pretoria Gauteng -25.73857 28.20733 Diaspididae Aspidiotus nerii HEMC01543 Pretoria Gauteng -25.73857 28.20733 Diaspididae Aspidiotus nerii HEMC01571 Citrusdal Western Cape -32.5946 19.0286 Diaspididae Aspidiotus nerii HEMC01572 Hartmanskloof North West -25.6324 26.4429 Diaspididae Aspidiotus nerii HEMC01609 Pietermaritzburg KwaZulu-Natal -29.6009 30.3681 Diaspididae Aspidiotus nerii HEMC01612 Durban KwaZulu-Natal -29.8458 31.0083 Diaspididae Aspidiotus nerii HEMC01655 Congoskraal Eastern Cape -33.6483 25.9908 Diaspididae Aspidiotus nerii HEMC01715 Pretoria Gauteng -25.73857 28.20733 Diaspididae Aspidiotus nerii HEMC01734 Pretoria Gauteng -25.73857 28.20733 Diaspididae Aspidiotus nerii HEMC01766 Vryburg North West -26.958429 24.72976 Diaspididae Aspidiotus nerii HEMC01880 Stellenbosch Western Cape -33.936557 18.86755 Diaspididae Aspidiotus nerii HEMC01912 Pretoria Gauteng -25.73857 28.20733 Diaspididae Aspidiotus nerii HEMC02054 Prince Alfred Hamlet Western Cape -33.6203 22.5157 Diaspididae Aspidiotus nerii HEMC02148 Pretoria Gauteng -25.73857 28.20733

184

Current family Species AccessionNumber Locality Province Latitude Longitude classification Diaspididae Aspidiotus nerii HEMC02273 Pretoria Gauteng -25.73857 28.20733 Diaspididae Aspidiotus nerii HEMC02579 Brandfort Free State -28.6983 26.4591 Diaspididae Aspidiotus nerii HEMC02580 Ficksburg Free State -28.537 27.7156 Diaspididae Aspidiotus nerii HEMC02704 Rivonia Gauteng -26.0497 28.0687 Diaspididae Aspidiotus nerii HEMC02876 Mafikeng North West -25.8554 25.6414 Diaspididae Aspidiotus nerii HEMC02878 Vryburg North West -26.958429 24.72976 Diaspididae Aspidiotus nerii HEMC02903 Addo Eastern Cape -33.5685 25.69216 Diaspididae Aspidiotus nerii HEMC03345 Halfway House Gauteng -25.9998 28.1137 Diaspididae Aspidiotus nerii HEMC03347 Ladysmith KwaZulu-Natal -28.4514 29.5367 Diaspididae Aspidiotus nerii HEMC03429 Kempton Park Gauteng -26.0511 28.2122 Diaspididae Aspidiotus nerii HEMC03526 Port Elizabeth Eastern Cape -33.93264 25.56995 Diaspididae Aspidiotus nerii HEMC03543 Muizenburg Western Cape -34.0154 18.3945 Diaspididae Aspidiotus nerii HEMC03609 Gordons Bay Western Cape -34.1745 18.8925 Diaspididae Aspidiotus nerii HEMC03828 Zeerust North West -25.53293 26.06376 Diaspididae Aspidiotus nerii HEMC04035 Citrusdal Western Cape -32.5946 19.0286 Diaspididae Aspidiotus nerii HEMC04740 Johannesburg Gauteng -26.179311 28.042399 Diaspididae Aspidiotus nerii HEMC05300 Rawsonville Western Cape -33.6821 19.3213 Diaspididae Aspidiotus nerii HEMC05417 Stellenbosch Western Cape -33.936557 18.86755 Diaspididae Aspidiotus nerii HEMC05445 Pretoria Gauteng -25.73857 28.20733 Diaspididae Aspidiotus nerii HEMC05491 Letaba Mpumalanga -23.8602 30.29855 Diaspididae Aspidiotus nerii HEMC05520 Stellenbosch Western Cape -33.936557 18.86755 Diaspididae Aspidiotus nerii HEMC05532 Cape Town Western Cape -34.0289 18.41937 Diaspididae Aspidiotus nerii HEMC05534 Cape Town Western Cape -34.0289 18.41937 Diaspididae Aspidiotus nerii HEMC05755 Citrusdal Western Cape -32.5946 19.0286 Diaspididae Aspidiotus nerii HEMC05765 Loskop Dam Mpumalanga -25.4299 29.3091 Diaspididae Aspidiotus nerii HEMC05795 Stellenbosch Western Cape -33.9597 18.8721 Diaspididae Aspidiotus nerii HEMC05823 Gods W Graskop Mpumalanga -27.0966 29.8745 Diaspididae Aspidiotus nerii HEMC05865 Kleinmond Western Cape -34.26315 19.18767 Diaspididae Aspidiotus nerii HEMC05873 Stellenbosch Western Cape -33.936557 18.86755 Diaspididae Aspidiotus nerii HEMC05877 Ventersdorp Gauteng -33.9597 18.8721 Diaspididae Aspidiotus nerii HEMC05973 Pretoria Gauteng -25.73857 28.20733 Diaspididae Aspidiotus nerii HEMC06237 Komatipoort Limpopo -25.4223 31.9441

185

Current family Species AccessionNumber Locality Province Latitude Longitude classification Diaspididae Aspidiotus nerii HEMC06287 Stellenbosch Western Cape -33.936557 18.86755 Diaspididae Aspidiotus nerii HEMC06392 Pretoria Gauteng -25.73857 28.20733 Diaspididae Aspidiotus nerii HEMC06453 Pretoria Gauteng -25.73857 28.20733 Diaspididae Aspidiotus nerii HEMC06456 Pretoria Gauteng -25.73857 28.20733 Diaspididae Aspidiotus nerii HEMC06501 Paarl Western Cape -33.731986 18.977615 Diaspididae Aspidiotus nerii HEMC06511 Tzaneen Limpopo -23.816924 30.303665 Diaspididae Aspidiotus nerii HEMC06561 Johannesburg Gauteng -26.179311 28.042399 Diaspididae Aspidiotus nerii HEMC06636 Garcia Pass Western Cape -33.5647 21.1313 Diaspididae Aspidiotus nerii HEMC07014 Kirstenbosch Western Cape -26.0321 28.0405 Diaspididae Aspidiotus nerii HEMC07015 Bettys Bay Western Cape -34.09106 18.813352 Diaspididae Aspidiotus nerii HEMC07016 Cape Town Western Cape -34.0289 18.41937 Diaspididae Aspidiotus nerii HEMC07017 Heidelberg Gauteng -26.503840 28.351140 Diaspididae Aspidiotus nerii SB103_1 Vredehuis Gauteng -25.71 28.1917 Diaspididae Aulacapis tubercularis HEMC01103 Durban KwaZulu-Natal -29.8458 31.0083 Diaspididae Aulacapis tubercularis HEMC01173 Durban KwaZulu-Natal -29.8458 31.0083 Diaspididae Aulacapis tubercularis HEMC01496 Durban KwaZulu-Natal -29.8458 31.0083 Diaspididae Aulacapis tubercularis HEMC01602 Letsitele Limpopo -23.9056 30.3633 Diaspididae Aulacapis tubercularis HEMC01627 Pietermaritzburg KwaZulu-Natal -29.6009 30.3681 Diaspididae Aulacapis tubercularis HEMC01630 Durban KwaZulu-Natal -29.8458 31.0083 Diaspididae Aulacapis tubercularis HEMC01848 Barberton Mpumalanga -25.7399 30.8566 Diaspididae Aulacapis tubercularis HEMC03348 Emalahleni Mpumalanga -25.4684 31.04401 Diaspididae Aulacapis tubercularis HEMC04528 Illovo Beach KwaZulu-Natal -30.1128 30.8389 Diaspididae Aulacapis tubercularis HEMC04783 Emalahleni Mpumalanga -25.4684 31.04401 Diaspididae Aulacapis tubercularis HEMC04785 Emalahleni Mpumalanga -25.4684 31.04401 Diaspididae Aulacapis tubercularis HEMC04808 Munnik Limpopo -23.6342 29.8982 Diaspididae Aulacapis tubercularis HEMC05957 Pretoriuskop Mpumalanga -25.2093 31.2354 Diaspididae Aulacapis tubercularis HEMC05961 Messina Limpopo -22.229833 29.635333 Diaspididae Aulacapis tubercularis HEMC06004 Pretoria North Gauteng -25.6622 28.149 Diaspididae Aulacapis tubercularis HEMC06284 Hoedspruit Limpopo -24.35452 30.95959 Diaspididae Aulacapis tubercularis HEMC06356 Hazyview Mpumalanga -25.0366 31.2001 Diaspididae Aulacapis tubercularis HEMC06367 Hoedspruit Limpopo -24.35452 30.95959 Diaspididae Aulacapis tubercularis HEMC06369 Hoedspruit Limpopo -24.35452 30.95959

186

Current family Species AccessionNumber Locality Province Latitude Longitude classification Diaspididae Aulacapis tubercularis HEMC06908 Hoedspruit Limpopo -24.35452 30.95959 Diaspididae Aulacapis tubercularis HEMC07061 Hoedspruit Limpopo -24.35452 30.95959 Diaspididae Aulacapis tubercularis SB245_2 Hazyview Mpumalanga -25.0366 31.2001 Diaspididae Diaspis echinocacti HEMC01217 Willowmore Eastern Cape -29.8 30.85 Diaspididae Diaspis echinocacti HEMC01653 Wolmaransstad Northern Cape -27.20.87 25.9778 Diaspididae Diaspis echinocacti HEMC05786 Pretoria Gauteng -25.73857 28.20733 Diaspididae Diaspis echinocacti HEMC06391 Pretoria Gauteng -25.73857 28.20733 Diaspididae Diaspis echinocacti SB55_1 Castle George Western Cape -33.7821 22.5491 Diaspididae Diaspis echinocacti SB55_2 Castle George Western Cape -33.7821 22.5491 Diaspididae Diaspis echinocacti SB55_3 Castle George Western Cape -33.7821 22.5491 Diaspididae Diaspis echinocacti SB55_4 Castle George Western Cape -33.7821 22.5491 Diaspididae Diaspis echinocacti SB55_5 Castle George Western Cape -33.7821 22.5491 Diaspididae Diaspis echinocacti SB55_7 Castle George Western Cape -33.7821 22.5491 Diaspididae Entaspidiotus lounsburyi HEMC00221 Bloemfontein Free state -33.93197 18.47081 Diaspididae Entaspidiotus lounsburyi HEMC00554 Middelburg Mpumalanga -25.7047 29.3143 Diaspididae Entaspidiotus lounsburyi HEMC00634 Stellenbosch Western Cape -33.936557 18.86755 Diaspididae Entaspidiotus lounsburyi HEMC01106 Eendekuil Western Cape -27.1731 27.179 Diaspididae Entaspidiotus lounsburyi HEMC01513 Pofadder Northern Cape -29.1508 19.4075 Diaspididae Entaspidiotus lounsburyi HEMC01580 Namaqualand Northern Cape -31.6096 187361 Diaspididae Entaspidiotus lounsburyi HEMC01772 Kroonstad Free state -27.553 27.6728 Diaspididae Entaspidiotus lounsburyi HEMC01921 Pretoria Gauteng -25.73857 28.20733 Diaspididae Entaspidiotus lounsburyi HEMC02122 Pretoria Gauteng -25.73857 28.20733 Diaspididae Entaspidiotus lounsburyi HEMC02123 Oudtshoorn Western Cape -33.3922 22.2150 Diaspididae Entaspidiotus lounsburyi HEMC02301 Durban KwaZulu-Natal -29.8458 31.0083 Diaspididae Entaspidiotus lounsburyi HEMC02648 Springbok Northen Cape -30.6353 22.0836 Diaspididae Entaspidiotus lounsburyi HEMC03161 Polokwane Limpopo -23.874485 29.464295 Diaspididae Entaspidiotus lounsburyi HEMC03212 Pretoria Gauteng -25.73857 28.20733 Diaspididae Entaspidiotus lounsburyi HEMC03343 De Doorns Western Cape -33.695866 19.495344 Diaspididae Entaspidiotus lounsburyi HEMC03688 Port Nolloth Northen Cape -29.2375 16.9031 Diaspididae Entaspidiotus lounsburyi HEMC03690 Bitterfontein Northen Cape -28.9112 17.7016 Diaspididae Entaspidiotus lounsburyi HEMC03691 Vredendal Western Cape 31.6658 18.5066 Diaspididae Entaspidiotus lounsburyi HEMC03694 Springbok Northen Cape -28.9112 17.7016

187

Current family Species AccessionNumber Locality Province Latitude Longitude classification Diaspididae Entaspidiotus lounsburyi HEMC03696 Bitterfontein Northen Cape -28.9112 17.7016 Diaspididae Entaspidiotus lounsburyi HEMC04744 Port Noloth 30km E Northen Cape -29.2375 16.9031 Diaspididae Entaspidiotus lounsburyi HEMC04856 Nababeep Northern Cape -29.618 17.7082 Diaspididae Entaspidiotus lounsburyi HEMC05462 Pretoria Gauteng -25.73857 28.20733 Diaspididae Entaspidiotus lounsburyi HEMC05530 Pretoria Gauteng -25.73857 28.20733 Diaspididae Entaspidiotus lounsburyi HEMC05636 Stellenbosch Western Cape -33.936557 18.86755 Diaspididae Entaspidiotus lounsburyi HEMC05638 Strandfontein Western Cape -31.7164 18.2512 Diaspididae Entaspidiotus lounsburyi HEMC05654 Clanwilliam Western Cape -32.119946 19.059219 Diaspididae Entaspidiotus lounsburyi HEMC05932 Pretoria Gauteng -25.73857 28.20733 Diaspididae Entaspidiotus lounsburyi HEMC06252 Pretoria Gauteng -25.73857 28.20733 Diaspididae Entaspidiotus lounsburyi SB250_2 Johannesburg Gauteng -26.179311 28.042399 Diaspididae Pseudaulacaspis pentagona (-) Pretoria Gauteng -25.73857 28.20733 Diaspididae Pseudaulacaspis pentagona HEMC00165 Rosebank Gauteng -26.143708 28.042513 Diaspididae Pseudaulacaspis pentagona HEMC00401 Pretoria Gauteng -25.73857 28.20733 Diaspididae Pseudaulacaspis pentagona HEMC00430 Durban KwaZulu-Natal -29.8458 31.0083 Diaspididae Pseudaulacaspis pentagona HEMC00563 Pretoria Gauteng -25.73857 28.20733 Diaspididae Pseudaulacaspis pentagona HEMC00626 Pretoria Gauteng -25.73857 28.20733 Diaspididae Pseudaulacaspis pentagona HEMC01160 Pretoria Gauteng -25.73857 28.20733 Diaspididae Pseudaulacaspis pentagona HEMC01307 Pretoria Gauteng -25.73857 28.20733 Diaspididae Pseudaulacaspis pentagona HEMC01339 Durban KwaZulu-Natal -29.8458 31.0083 Diaspididae Pseudaulacaspis pentagona HEMC01342 Pretoria Gauteng -25.73857 28.20733 Diaspididae Pseudaulacaspis pentagona HEMC01345 Pretoria Gauteng -25.73857 28.20733 Diaspididae Pseudaulacaspis pentagona HEMC01413 Tzaneen Limpopo -23.816924 30.303665 Diaspididae Pseudaulacaspis pentagona HEMC01455 Pretoria Gauteng -25.73857 28.20733 Diaspididae Pseudaulacaspis pentagona HEMC01528 Pretoria Gauteng -25.73857 28.20733 Diaspididae Pseudaulacaspis pentagona HEMC01587 Durban KwaZulu-Natal -29.8458 31.0083 Diaspididae Pseudaulacaspis pentagona HEMC01623 Pretoria Gauteng -25.73857 28.20733 Diaspididae Pseudaulacaspis pentagona HEMC01645 Durban KwaZulu-Natal -29.8458 31.0083 Diaspididae Pseudaulacaspis pentagona HEMC01663 Durban KwaZulu-Natal -29.8458 31.0083 Diaspididae Pseudaulacaspis pentagona HEMC01664 Umkomaas KwaZulu-Natal -30.206082 30.795128 Diaspididae Pseudaulacaspis pentagona HEMC01667 Pretoria Gauteng -25.73857 28.20733 Diaspididae Pseudaulacaspis pentagona HEMC02145 Pretoria Gauteng -25.73857 28.20733

188

Current family Species AccessionNumber Locality Province Latitude Longitude classification Diaspididae Pseudaulacaspis pentagona HEMC02304 Pretoria Gauteng -25.73857 28.20733 Diaspididae Pseudaulacaspis pentagona HEMC03771 Pretoria Gauteng -25.73857 28.20733 Diaspididae Pseudaulacaspis pentagona HEMC04533 Pretoria Gauteng -25.73857 28.20733 Diaspididae Pseudaulacaspis pentagona HEMC04637 Pietermaritzburg KwaZulu-Natal -29.6009 30.3681 Diaspididae Pseudaulacaspis pentagona HEMC04651 Emalahleni Mpumalanga -25.4684 31.04401 Diaspididae Pseudaulacaspis pentagona HEMC04782 Pretoria Gauteng -25.73857 28.20733 Diaspididae Pseudaulacaspis pentagona HEMC04791 Emalahleni Mpumalanga -25.4684 31.04401 Diaspididae Pseudaulacaspis pentagona HEMC04792 Pretoria Gauteng -25.73857 28.20733 Diaspididae Pseudaulacaspis pentagona HEMC05358 Emalahleni Mpumalanga -25.4684 31.04401 Diaspididae Pseudaulacaspis pentagona HEMC05561 Emalahleni Mpumalanga -25.4684 31.04401 Diaspididae Pseudaulacaspis pentagona HEMC05566 Emalahleni Mpumalanga -25.4684 31.04401 Diaspididae Pseudaulacaspis pentagona HEMC05583 Tzaneen Limpopo -23.816924 30.303665 Diaspididae Pseudaulacaspis pentagona HEMC05764 De Hoek Forest Reserve Western Cape -32.0175 18.8458 Diaspididae Pseudaulacaspis pentagona HEMC05815 Pretoria Gauteng -25.73857 28.20733 Diaspididae Pseudaulacaspis pentagona HEMC05826 Rietondale Gauteng -25.73286 28.30494 Diaspididae Pseudaulacaspis pentagona HEMC05844 Pietermaritzburg KwaZulu-Natal -29.6009 30.3681 Diaspididae Pseudaulacaspis pentagona HEMC05963 Emalahleni Mpumalanga -25.4684 31.04401 Diaspididae Pseudaulacaspis pentagona HEMC06238 Schoemanskloof Mpumalanga -25.3828 30.5084 Diaspididae Pseudaulacaspis pentagona HEMC06259 Emalahleni Mpumalanga -25.4684 31.04401 Diaspididae Pseudaulacaspis pentagona HEMC06292 Roodeplaat Gauteng -25.60398 28.35429 Diaspididae Pseudaulacaspis pentagona HEMC06388 Stellenbosch Western Cape -33.936557 18.86755 Diaspididae Pseudaulacaspis pentagona HEMC06637 Pretoria Gauteng -25.73857 28.20733 Diaspididae Pseudaulacaspis pentagona HEMC07067 Roodeplaat Gauteng -25.60398 28.35429 Diaspididae Pseudaulacaspis pentagona SB152_3 Rietondale Gauteng -25.73286 28.30494 Diaspididae Separaspis capensis (-) Namaqualand Northern Cape -31.6096 187361 Diaspididae Separaspis capensis HEMC00235 Polokwane Limpopo -23.874485 29.464295 Diaspididae Separaspis capensis HEMC00436 Polokwane Limpopo -23.874485 29.464295 Diaspididae Separaspis capensis HEMC00600 Zebediela Limpopo -24.2899 29.3238 Diaspididae Separaspis capensis HEMC00621 Tzaneen Limpopo -23.816924 30.303665 Diaspididae Separaspis capensis HEMC01425 Addo Eastern Cape -33.5685 25.69216 Diaspididae Separaspis capensis HEMC01434 Addo Eastern Cape -33.5685 25.69216 Diaspididae Separaspis capensis HEMC01451 Haenertsburg Mpumalanga -22.0371 28.093

189

Current family Species AccessionNumber Locality Province Latitude Longitude classification Diaspididae Separaspis capensis HEMC01507 Citrusdal Western Cape -32.5946 19.0286 Diaspididae Separaspis capensis HEMC01594 Worcester Western Cape -33.633122 19.431285 Diaspididae Separaspis capensis HEMC01735 Uitenhage Eastern Cape -33.763741 25.393641 Diaspididae Separaspis capensis HEMC01771 Worcester Western Cape -33.633122 19.431285 Diaspididae Separaspis capensis HEMC01930 Umkomaas KwaZulu-Natal -30.206082 30.795128 Diaspididae Separaspis capensis HEMC03600 Ladismith Western Cape -33.5551 21.1821 Diaspididae Separaspis capensis HEMC03608 Ladismith Western Cape -33.5551 21.1821 Diaspididae Separaspis capensis HEMC03741 De Doorns Western Cape -33.695866 19.495344 Diaspididae Separaspis capensis HEMC03765 Graaff Reinet Eastern Cape -32.2179 24.5829 Diaspididae Separaspis capensis HEMC03770 Potgietersrus Limpopo -24.1324 29.0339 Diaspididae Separaspis capensis HEMC03777 Citrusdal Waterfalls Western Cape -32.5946 19.0286 Diaspididae Separaspis capensis HEMC03778 De Doorns Western Cape -33.695866 19.495344 Diaspididae Separaspis capensis HEMC03798 Grahamstown Eastern Cape -33,30271 26.52383 Diaspididae Separaspis capensis HEMC03806 Somerset East Western Cape -32.6637 25.5502 Diaspididae Separaspis capensis HEMC03901 Magaliesburg North West -26.0082 27.5314 Diaspididae Separaspis capensis SB289_1 Magaliesburg North West -26.0082 27.5314 Diaspididae Separaspis proteae (-) Buffelspoort North West -25.826260 27.42036 Diaspididae Separaspis proteae HEMC00241 Buffelspoort North West -25.826260 27.42036 Diaspididae Separaspis proteae HEMC00495 Johannesburg Gauteng -26.179311 28.042399 Diaspididae Separaspis proteae HEMC01479 Runtenburg North West -25.7239 27.2912 Diaspididae Separaspis proteae HEMC02146 Pretoria Gauteng -25.73857 28.20733 Diaspididae Separaspis proteae HEMC03395 Pretoria Gauteng -25.73857 28.20733 Diaspididae Separaspis proteae HEMC05600 Rustenburg North West -25.7239 27.2912 Diaspididae Separaspis proteae HEMC05619 Krugersdorp North West -26.106771 27.724278 Diaspididae Separaspis proteae HEMC05845 Magaliesberg North West -26.0082 27.5314 Diaspididae Separaspis proteae HEMC06263 Magaliesberg North West -26.0082 27.5314 Diaspididae Separaspis proteae HEMC06264 Ferie Glen Gauteng -25.79878 28.32704 Kirriidae Tachardina africana (-) Pretoria Gauteng -25.73857 28.20733 Kirriidae Tachardina africana HEMC00550 Addo Eastern Cape -33.5685 25.69216 Kirriidae Tachardina africana HEMC01386 Rustenburg North West -25.7239 27.2912 Kirriidae Tachardina africana HEMC04079 Citrusdal area Western Cape -32.5946 19.0286 Kirriidae Tachardina africana HEMC06775 Roodeplaat Gauteng -25.60398 28.35429

190

Current family Species AccessionNumber Locality Province Latitude Longitude classification Kirriidae Tachardina africana SB227_11 Roodeplaat Gauteng -25.60398 28.35429 Kirriidae Tachardina africana SB227_12 Roodeplaat Gauteng -25.60398 28.35429 Kirriidae Tachardina africana SB227_13 Roodeplaat Gauteng -25.60398 28.35429 Kirriidae Tachardina africana SB227_14 Roodeplaat Gauteng -25.60398 28.35429 Margarodidae Icerya purchasi HEMC06405 Emalahleni Mpumalanga -25.4684 31.04401 Margarodidae Icerya purchasi HEMC07069 Grahamstown Eastern Cape -33,30271 26.52383 Margarodidae Icerya purchasi SB27_1 Rietondale Gauteng -25.73286 28.30494 Margarodidae Icerya purchasi SB27_2 Rietondale Gauteng -25.73286 28.30494 Margarodidae Icerya purchasi SB27_3 Rietondale Gauteng -25.73286 28.30494 Margarodidae Icerya purchasi SB27_4 Rietondale Gauteng -25.73286 28.30494 Margarodidae Icerya purchasi SB27_5 Rietondale Gauteng -25.73286 28.30494 Margarodidae Icerya syechellarum HEMC03248 Pietermaritzburg KwaZulu-Natal -29.6009 30.3681 Margarodidae Icerya syechellarum HEMC06413 Letsitele Limpopo -23.9056 30.3633 Margarodidae Icerya syechellarum HEMC06560 Komatipoort Mpumalanga -25.4223 31.9441 Margarodidae Icerya syechellarum HEMC06717 Komatipoort Mpumalanga -25.4223 31.9441 Margarodidae Icerya syechellarum SB238_1 Buffelspoort North West -25.826260 27.42036 Margarodidae Icerya syechellarum SB238_2 Buffelspoort North West -25.826260 27.42036 Margarodidae Icerya syechellarum SB238_3 Buffelspoort North West -25.826260 27.42036 Margarodidae Icerya syechellarum SB238_4 Buffelspoort North West -25.826260 27.42036 Margarodidae Icerya syechellarum SB238_5 Buffelspoort North West -25.826260 27.42036 Ortheziidae Orthezia insignis (-) Duban Botanical Gardens KwaZulu-Natal -29.7525 30.822 Ortheziidae Orthezia insignis HEMC00012 Stellenbosch Western Cape -33.936557 18.86755 Ortheziidae Orthezia insignis HEMC05282 Pretoria Gauteng -25.73857 28.20733 Ortheziidae Orthezia insignis HEMC05841 Amanzimtoti KwaZulu-Natal -30.0522 30.855 Ortheziidae Orthezia insignis HEMC05942 Cape Town Western Cape -34.0289 18.41937 Ortheziidae Orthezia insignis HEMC06380 Rietondale Gauteng -25.73286 28.30494 Ortheziidae Orthezia insignis SB7_1 Rietondale Gauteng -25.73286 28.30494 Ortheziidae Orthezia insignis SB7_2 Rietondale Gauteng -25.73286 28.30494 Ortheziidae Orthezia insignis SB7_3 Rietondale Gauteng -25.73286 28.30494 Ortheziidae Orthezia insignis SB7_4 Rietondale Gauteng -25.73286 28.30494 Pseudococcidae Delottococcus aberiae HEMC00740 Emalahleni Mpumalanga -25.4684 31.04401 Pseudococcidae Delottococcus aberiae HEMC00743 Emalahleni Mpumalanga -25.4684 31.04401

191

Current family Species AccessionNumber Locality Province Latitude Longitude classification Pseudococcidae Delottococcus aberiae HEMC00752 Emalahleni Mpumalanga -25.4684 31.04401 Pseudococcidae Delottococcus aberiae HEMC00753 Middelburg Mpumalanga -25.7047 29.3143 Pseudococcidae Delottococcus aberiae HEMC00786 Silverton Gauteng -25.713 28.2891 Pseudococcidae Delottococcus aberiae HEMC00850 Tzaneen Limpopo -23.816924 30.303665 Pseudococcidae Delottococcus aberiae HEMC00886 Durban KwaZulu-Natal -29.8458 31.0083 Pseudococcidae Delottococcus aberiae HEMC00892 Umkomaas KwaZulu-Natal -30.206082 30.795128 Pseudococcidae Delottococcus aberiae HEMC00914 Durban KwaZulu-Natal -29.8458 31.0083 Pseudococcidae Delottococcus aberiae HEMC00915 Durban KwaZulu-Natal -29.8458 31.0083 Pseudococcidae Delottococcus aberiae HEMC00946 Hout Bay Western Cape -34.0460 18.3652 Pseudococcidae Delottococcus aberiae HEMC00952 Cape Town Western Cape -34.0289 18.41937 Pseudococcidae Delottococcus aberiae HEMC01049 Emalahleni Mpumalanga -25.4684 31.04401 Pseudococcidae Delottococcus aberiae HEMC01198 Addo Eastern Cape -33.5685 25.69216 Pseudococcidae Delottococcus aberiae HEMC02978 Louis Trichardt Limpopo -23.0468 29.9196 Pseudococcidae Delottococcus aberiae HEMC03030 Amanzimtoti KwaZulu-Natal -30.0522 30.855 Pseudococcidae Delottococcus aberiae HEMC03289 Umkomaas KwaZulu-Natal -30.206082 30.795128 Pseudococcidae Delottococcus aberiae HEMC03575 Addo Eastern Cape -33.5685 25.69216 Pseudococcidae Delottococcus aberiae HEMC04151 Humansdorp Eastern Cape -33.9371 24.941 Pseudococcidae Delottococcus aberiae HEMC04310 Park Rynie KwaZulu-Natal -30.2901 30.6834 Pseudococcidae Delottococcus aberiae HEMC04340 Kranskop Limpopo -23.5763 29.9354 Pseudococcidae Delottococcus aberiae HEMC04357 Grahamstown Eastern Cape -33,30271 26.52383 Pseudococcidae Delottococcus aberiae HEMC04401 Zebediela Limpopo -24.2899 29.3238 Pseudococcidae Delottococcus aberiae HEMC04404 Zebediela Limpopo -24.2899 29.3238 Pseudococcidae Delottococcus aberiae HEMC04405 Zebediela Limpopo -24.2899 29.3238 Pseudococcidae Delottococcus aberiae HEMC04462 Hermanus Western Cape -34.42041 19.24101 Pseudococcidae Delottococcus aberiae HEMC04584 Oribi Gorge KwaZulu-Natal -30.6998 30.2733 Pseudococcidae Delottococcus aberiae HEMC04734 Graskop Mpumalanga -27.0966 29.8745 Pseudococcidae Delottococcus aberiae HEMC04835 Emalahleni Mpumalanga -25.4684 31.04401 Pseudococcidae Delottococcus aberiae HEMC05094 Emalahleni Mpumalanga -25.4684 31.04401 Pseudococcidae Delottococcus aberiae HEMC05103 Emalahleni Mpumalanga -25.4684 31.04401 Pseudococcidae Delottococcus aberiae HEMC05284 Cape Town Western Cape -34.0289 18.41937 Pseudococcidae Delottococcus aberiae HEMC05318 Emalahleni Mpumalanga -25.4684 31.04401 Pseudococcidae Delottococcus aberiae HEMC05564 Elandsbaai Western Cape -32.3155 18.3966

192

Current family Species AccessionNumber Locality Province Latitude Longitude classification Pseudococcidae Delottococcus aberiae HEMC05570 Cape Town Western Cape -34.0289 18.41937 Pseudococcidae Delottococcus aberiae HEMC05588 Port Elizabeth Eastern Cape -33.93264 25.56995 Pseudococcidae Delottococcus aberiae HEMC05616 Kareedouw Eastern Cape -33.9906 24.2134 Pseudococcidae Delottococcus aberiae HEMC05666 Gansbaai Western Cape -34.579085 19.443410 Pseudococcidae Delottococcus aberiae HEMC05673 Stellenbosch Western Cape -33.936557 18.86755 Pseudococcidae Delottococcus aberiae HEMC05697 Cape Town Western Cape -34.0289 18.41937 Pseudococcidae Delottococcus aberiae HEMC05709 Kareedouw Eastern Cape -33.9906 24.2134 Pseudococcidae Delottococcus aberiae HEMC06546 Nkwalini Valley KwaZulu-Natal -28.726180 31.525150 Pseudococcidae Delottococcus aberiae HEMC06547 Melmoth KwaZulu-Natal -28.5388 31.3075 Pseudococcidae Delottococcus aberiae HEMC06562 Nkwalini Valley KwaZulu-Natal -28.726180 31.525150 Pseudococcidae Delottococcus aberiae HEMC06594 Emalahleni Mpumalanga -25.4684 31.04401 Pseudococcidae Delottococcus aberiae HEMC06614 Nkwalini KwaZulu-Natal -28.726180 31.525150 Pseudococcidae Delottococcus aberiae SB259_1 Rietondale Gauteng -25.73286 28.30494 Pseudococcidae Dysmicoccus brevipes HEMC00561 Hectorspruit Mpumalanga -25.4321 31.6825 Pseudococcidae Dysmicoccus brevipes HEMC02049 Empangeni KwaZulu-Natal -28.7144 31.8625 Pseudococcidae Dysmicoccus brevipes HEMC02987 Louis Trichardt Limpopo -23.0468 29.9196 Pseudococcidae Dysmicoccus brevipes HEMC03307 East London Eastern Cape -33.93197 18.4708 Pseudococcidae Dysmicoccus brevipes HEMC04959 Port Elizabeth Eastern Cape -33.93264 25.56995 Pseudococcidae Dysmicoccus brevipes HEMC05026 Pretoria Gauteng -25.73857 28.20733 Pseudococcidae Dysmicoccus brevipes HEMC05106 Port Elizabeth Eastern Cape -33.93264 25.56995 Pseudococcidae Dysmicoccus brevipes HEMC05177 Hluhluwe KwaZulu-Natal -28.0817 32.252 Pseudococcidae Dysmicoccus brevipes HEMC05551 East London Eastern Cape -33.93197 18.4708 Pseudococcidae Dysmicoccus brevipes HEMC06373 Emalahleni Mpumalanga -25.4684 31.04401 Pseudococcidae Dysmicoccus brevipes HEMC06930 Hluhluwe KwaZulu-Natal -28.0817 32.252 Pseudococcidae Ferrisia malvastra HEMC06417 Emalahleni Mpumalanga -25.4684 31.04401 Pseudococcidae Ferrisia malvastra HEMC06822 Stellenbosch Western Cape -33.936557 18.86755 Pseudococcidae Ferrisia malvastra HEMC06836 Roodeplaat Gauteng -25.60398 28.35429 Pseudococcidae Ferrisia malvastra HEMC06633 Brits North West -25.6622 27.796 Pseudococcidae Ferrisia malvastra HEMC06994 Merwespont Western Cape -33.9702 20.1538 Pseudococcidae Ferrisia malvastra HEMC07085 Rietondale Gauteng -25.73286 28.30494 Pseudococcidae Ferrisia malvastra SB11_1 Rietondale Gauteng -25.73286 28.30494 Pseudococcidae Ferrisia malvastra SB11_2 Rietondale Gauteng -25.73286 28.30494

193

Current family Species AccessionNumber Locality Province Latitude Longitude classification Pseudococcidae Ferrisia malvastra SB11_3 Rietondale Gauteng -25.73286 28.30494 Pseudococcidae Ferrisia malvastra SB11_4 Rietondale Gauteng -25.73286 28.30494 Pseudococcidae Ferrisia malvastra SB11_5 Rietondale Gauteng -25.73286 28.30494 Pseudococcidae Hypogeococcus pungens HEMC06670 Pretoria Gauteng -25.73857 28.20733 Pseudococcidae Hypogeococcus pungens HEMC06671 Pretoria Gauteng -25.73857 28.20733 Pseudococcidae Hypogeococcus pungens HEMC06672 Pretoria Gauteng -25.73857 28.20733 Pseudococcidae Hypogeococcus pungens HEMC06673 Pretoria Gauteng -25.73857 28.20733 Pseudococcidae Hypogeococcus pungens HEMC06674 Pretoria Gauteng -25.73857 28.20733 Pseudococcidae Hypogeococcus pungens HEMC06675 Pretoria Gauteng -25.73857 28.20733 Pseudococcidae Hypogeococcus pungens HEMC07078 Rietondale Gauteng -25.73286 28.30494 Pseudococcidae Hypogeococcus pungens HEMC07080 Rietondale Gauteng -25.73286 28.30494 Pseudococcidae Hypogeococcus pungens SB71_1 Rietondale Gauteng -25.73286 28.30494 Pseudococcidae Hypogeococcus pungens SB71_2 Rietondale Gauteng -25.73286 28.30494 Pseudococcidae Hypogeococcus pungens SB71_3 Rietondale Gauteng -25.73286 28.30494 Pseudococcidae Hypogeococcus pungens SB71_4 Rietondale Gauteng -25.73286 28.30494 Pseudococcidae Hypogeococcus pungens SB71_5 Rietondale Gauteng -25.73286 28.30494 Pseudococcidae Nairobia bifrons HEMC06414 Tzaneen Limpopo -23.816924 30.303665 Pseudococcidae Nairobia bifrons HEMC06852 Silverton Gauteng -25.713 28.2891 Pseudococcidae Nairobia bifrons SB202_1 Roodeplaat Gauteng -25.60398 28.35429 Pseudococcidae Nairobia bifrons SB202_2 Roodeplaat Gauteng -25.60398 28.35429 Pseudococcidae Nairobia bifrons SB202_3 Roodeplaat Gauteng -25.60398 28.35429 Pseudococcidae Nairobia bifrons SB202_4 Roodeplaat Gauteng -25.60398 28.35429 Pseudococcidae Nairobia bifrons SB202_5 Roodeplaat Gauteng -25.60398 28.35429 Pseudococcidae Nipaecoccus graminis HEMC00064 Tongaat KwaZulu-Natal -29.3644 31.1297 Pseudococcidae Nipaecoccus graminis HEMC00800 Pretoria Gauteng -25.73857 28.20733 Pseudococcidae Nipaecoccus graminis HEMC02046 Mookgopong Limpopo -24.4275 28.59419 Pseudococcidae Nipaecoccus graminis SB286_1 Tswaing Nature Researve Gauteng -25.4187 28.0815 Pseudococcidae Nipaecoccus graminis SB286_2 Tswaing Nature Researve Gauteng -25.4187 28.0815 Pseudococcidae Nipaecoccus graminis SB286_3 Tswaing Nature Researve Gauteng -25.4187 28.0815 Pseudococcidae Nipaecoccus graminis SB286_4 Tswaing Nature Researve Gauteng -25.4187 28.0815 Pseudococcidae Nipaecoccus graminis SB286_5 Tswaing Nature Researve Gauteng -25.4187 28.0815 Pseudococcidae Nipaecoccus nipae HEMC00741 Durban KwaZulu-Natal -29.8458 31.0083

194

Current family Species AccessionNumber Locality Province Latitude Longitude classification Pseudococcidae Nipaecoccus nipae HEMC02948 Shakaskraal KwaZulu-Natal -29.4401 31.2029 Pseudococcidae Nipaecoccus nipae HEMC05400 Park Rynie KwaZulu-Natal -30.2901 30.6834 Pseudococcidae Nipaecoccus nipae HEMC06832 Malelane Mpumalanga -25.2905 31.3136 Pseudococcidae Nipaecoccus nipae HEMC06873 Pretoria Gauteng -25.73857 28.20733 Pseudococcidae Nipaecoccus nipae HEMC07026 Levubu Limpopo -23.0542 30.2864 Pseudococcidae Nipaecoccus nipae HEMC07042 Levubu Limpopo -23.0542 30.2864 Pseudococcidae Nipaecoccus nipae SB239_1 Pretoria central Gauteng -25.73857 28.20733 Pseudococcidae Nipaecoccus nipae SB239_2 Pretoria central Gauteng -25.73857 28.20733 Pseudococcidae Nipaecoccus nipae SB239_3 Pretoria central Gauteng -25.73857 28.20733 Pseudococcidae Nipaecoccus nipae SB239_4 Pretoria central Gauteng -25.73857 28.20733 Pseudococcidae Nipaecoccus nipae SB239_5 Pretoria central Gauteng -25.73857 28.20733 Pseudococcidae Nipaecoccus viridis (-) Pretoria Gauteng -25.73857 28.20733 Pseudococcidae Nipaecoccus viridis HEMC00057 Pretoria Gauteng -25.73857 28.20733 Pseudococcidae Nipaecoccus viridis HEMC00674 Pretoria Gauteng -25.73857 28.20733 Pseudococcidae Nipaecoccus viridis HEMC00689 Pretoriuskop Mpumalanga -25.2093 31.2354 Pseudococcidae Nipaecoccus viridis HEMC00809 Rustenburg North West -25.7239 27.2912 Pseudococcidae Nipaecoccus viridis HEMC00942 Rustenburg North West -25.7239 27.2912 Pseudococcidae Nipaecoccus viridis HEMC01207 Mkuze KwaZulu-Natal -27.6402 32.2638 Pseudococcidae Nipaecoccus viridis HEMC02082 Letsitele Limpopo -23.9056 30.3633 Pseudococcidae Nipaecoccus viridis HEMC05359 Zebediela Limpopo -24.2899 29.3238 Pseudococcidae Nipaecoccus viridis HEMC05483 Letaba Mpumalanga -23.8602 30.29855 Pseudococcidae Nipaecoccus viridis HEMC05497 Emalahleni Mpumalanga -25.4684 31.04401 Pseudococcidae Nipaecoccus viridis HEMC05508 Letaba Mpumalanga -23.8602 30.29855 Pseudococcidae Nipaecoccus viridis HEMC05519 Emalahleni Mpumalanga -25.4684 31.04401 Pseudococcidae Nipaecoccus viridis HEMC06408 Emalahleni Mpumalanga -25.4684 31.04401 Pseudococcidae Nipaecoccus viridis HEMC06592 Groblersdal area Limpopo -25.134 29.4221 Pseudococcidae Nipaecoccus viridis HEMC06964 Ferie Glen Gauteng -25.79878 28.32704 Pseudococcidae Paracoccus burnerae (-) Pretoria Gauteng -25.73857 28.20733 Pseudococcidae Paracoccus burnerae HEMC00047 Emalahleni Mpumalanga -25.4684 31.04401 Pseudococcidae Paracoccus burnerae HEMC00742 Witrivier Mpumalanga -25.3622 31.2341 Pseudococcidae Paracoccus burnerae HEMC00745 Pretoria Gauteng -25.73857 28.20733 Pseudococcidae Paracoccus burnerae HEMC00758 Pretoria Gauteng -25.73857 28.20733

195

Current family Species AccessionNumber Locality Province Latitude Longitude classification Pseudococcidae Paracoccus burnerae HEMC00759 Pretoria Gauteng -25.73857 28.20733 Pseudococcidae Paracoccus burnerae HEMC00791 Skukuza Mpumalanga -24.9898 31.5926 Pseudococcidae Paracoccus burnerae HEMC00811 Pretoria Gauteng -25.73857 28.20733 Pseudococcidae Paracoccus burnerae HEMC00815 Pretoria Gauteng -25.73857 28.20733 Pseudococcidae Paracoccus burnerae HEMC00840 Pretoria Gauteng -25.73857 28.20733 Pseudococcidae Paracoccus burnerae HEMC00864 Zebediela Limpopo -24.2899 29.3238 Pseudococcidae Paracoccus burnerae HEMC00865 Durban KwaZulu-Natal -29.8458 31.0083 Pseudococcidae Paracoccus burnerae HEMC00899 Pretoria Gauteng -25.73857 28.20733 Pseudococcidae Paracoccus burnerae HEMC00940 Zebediela Limpopo -24.2899 29.3238 Pseudococcidae Paracoccus burnerae HEMC02011 Zebediela Limpopo -24.2899 29.3238 Pseudococcidae Paracoccus burnerae HEMC02013 Pretoria Gauteng -25.73857 28.20733 Pseudococcidae Paracoccus burnerae HEMC02047 Zebediela Limpopo -24.2899 29.3238 Pseudococcidae Paracoccus burnerae HEMC02067 Bloemfontein Free state -33.93197 18.47081 Pseudococcidae Paracoccus burnerae HEMC02314 Kuruman Northern Cape -27.473730 24.434667 Pseudococcidae Paracoccus burnerae HEMC02613 Mafikeng North West -25.8554 25.6414 Pseudococcidae Paracoccus burnerae HEMC02636 Pretoria Gauteng -25.73857 28.20733 Pseudococcidae Paracoccus burnerae HEMC02984 Pretoria Gauteng -25.73857 28.20733 Pseudococcidae Paracoccus burnerae HEMC03001 Pretoria Gauteng -25.73857 28.20733 Pseudococcidae Paracoccus burnerae HEMC03243 Irene Gauteng -25.87907 28.20743 Pseudococcidae Paracoccus burnerae HEMC03633 Irene Gauteng -25.87907 28.20743 Pseudococcidae Paracoccus burnerae HEMC03647 Addo Eastern Cape -33.5685 25.69216 Pseudococcidae Paracoccus burnerae HEMC04147 Addo Eastern Cape -33.5685 25.69216 Pseudococcidae Paracoccus burnerae HEMC04185 Addo Eastern Cape -33.5685 25.69216 Pseudococcidae Paracoccus burnerae HEMC04192 Zebediela Limpopo -24.2899 29.3238 Pseudococcidae Paracoccus burnerae HEMC04204 Addo Eastern Cape -33.5685 25.69216 Pseudococcidae Paracoccus burnerae HEMC04207 Zebediela Limpopo -24.2899 29.3238 Pseudococcidae Paracoccus burnerae HEMC04400 Zebediela Limpopo -24.2899 29.3238 Pseudococcidae Paracoccus burnerae HEMC04403 Pretoria Gauteng -25.73857 28.20733 Pseudococcidae Paracoccus burnerae HEMC04599 Stellenbosch Western Cape -33.936557 18.86755 Pseudococcidae Paracoccus burnerae HEMC04848 Stellenbosch Western Cape -33.936557 18.86755 Pseudococcidae Paracoccus burnerae HEMC05015 Stellenbosch Western Cape -33.936557 18.86755 Pseudococcidae Paracoccus burnerae HEMC05032 Stellenbosch Western Cape -33.936557 18.86755

196

Current family Species AccessionNumber Locality Province Latitude Longitude classification Pseudococcidae Paracoccus burnerae HEMC05036 Stellenbosch Western Cape -33.936557 18.86755 Pseudococcidae Paracoccus burnerae HEMC05175 Stellenbosch Western Cape -33.936557 18.86755 Pseudococcidae Paracoccus burnerae HEMC05186 Stellenbosch Western Cape -33.936557 18.86755 Pseudococcidae Paracoccus burnerae HEMC05190 Tzaneen Limpopo -23.816924 30.303665 Pseudococcidae Paracoccus burnerae HEMC05364 Stellenbosch Western Cape -33.936557 18.86755 Pseudococcidae Paracoccus burnerae HEMC05368 Zebediela Limpopo -24.2899 29.3238 Pseudococcidae Paracoccus burnerae HEMC05487 Roodeplaat Gauteng -25.60398 28.35429 Pseudococcidae Paracoccus burnerae HEMC05514 WITBANK Mpumalanga -26.5174 28.8848 Pseudococcidae Paracoccus burnerae HEMC05553 Emalahleni Mpumalanga -25.4684 31.04401 Pseudococcidae Paracoccus burnerae HEMC05613 Johannesburg Gauteng -26.179311 28.042399 Pseudococcidae Paracoccus burnerae HEMC06329 Nkwalini KwaZulu-Natal -28.726180 31.525150 Pseudococcidae Paracoccus burnerae HEMC06346 Marble Hall Mpumalanga -24.9637 29.2992 Pseudococcidae Paracoccus burnerae HEMC06387 Emalahleni Mpumalanga -25.4684 31.04401 Pseudococcidae Paracoccus burnerae HEMC06429 Brits North West -25.6622 27.796 Pseudococcidae Paracoccus burnerae HEMC06551 Emalahleni Mpumalanga -25.4684 31.04401 Pseudococcidae Paracoccus burnerae HEMC06555 Emalahleni Mpumalanga -25.4684 31.04401 Pseudococcidae Paracoccus burnerae HEMC06589 Emalahleni Mpumalanga -25.4684 31.04401 Pseudococcidae Paracoccus burnerae HEMC06595 Malelane Mpumalanga -25.2905 31.3136 Pseudococcidae Paracoccus burnerae HEMC06598 Emalahleni Mpumalanga -25.4684 31.04401 Pseudococcidae Paracoccus burnerae HEMC06612 Emalahleni Mpumalanga -25.4684 31.04401 Pseudococcidae Paracoccus burnerae HEMC06613 Roodeplaat Gauteng -25.60398 28.35429 Pseudococcidae Paracoccus burnerae HEMC06840 Port Elizabeth Eastern Cape -33.93264 25.56995 Pseudococcidae Paracoccus burnerae HEMC06858 Gouda Western Cape -33.2466 19.0227 Pseudococcidae Paracoccus burnerae HEMC06895 Agterland Citrusdal Western Cape -32.5946 19.0286 Pseudococcidae Paracoccus burnerae HEMC06924 Somerset West Western Cape -34.1009 18.8427 Pseudococcidae Paracoccus burnerae HEMC06944 Stellenbosch Western Cape -33.936557 18.86755 Pseudococcidae Paracoccus burnerae HEMC07099 Rietondale Gauteng -25.73286 28.30494 Pseudococcidae Phenacoccus madeirensis HEMC06343 Halfway House Gauteng -25.9998 28.1137 Pseudococcidae Phenacoccus madeirensis HEMC06909 Halfway House Gauteng -25.9998 28.1137 Pseudococcidae Phenacoccus madeirensis HEMC06910 Brits North West -25.6622 27.796 Pseudococcidae Phenacoccus madeirensis HEMC06986 Cedara KwaZulu-Natal -29.5238 30.2616 Pseudococcidae Phenacoccus madeirensis HEMC07030 Cedara KwaZulu-Natal -29.5238 30.2616

197

Current family Species AccessionNumber Locality Province Latitude Longitude classification Pseudococcidae Phenacoccus madeirensis HEMC07031 Grabouw Western Cape -34.0488 19.0886 Pseudococcidae Phenacoccus madeirensis HEMC07035 Roodeplaat Gauteng -25.60398 28.35429 Pseudococcidae Phenacoccus madeirensis HEMC07038 Rietondale Gauteng -25.73286 28.30494 Pseudococcidae Phenacoccus madeirensis SB132_2 Vredehuis Gauteng -25.71 28.1917 Pseudococcidae Phenacoccus manihoti (-) Mzinti Mpumalanga -25.71 28.1917 Pseudococcidae Phenacoccus manihoti HEMC06309 Phalaborwa Limpopo -23.9309 31.1364 Pseudococcidae Phenacoccus manihoti HEMC06345 Pretoria Gauteng -25.73857 28.20733 Pseudococcidae Phenacoccus manihoti HEMC06499 Roodeplaat Gauteng -25.60398 28.35429 Makhathini Research -27.4864 32.1809 Pseudococcidae Phenacoccus manihoti HEMC06841 Station KwaZulu-Natal Pseudococcidae Phenacoccus manihoti HEMC07077 Roodeplaat Gauteng -25.60398 28.35429 Pseudococcidae Phenacoccus manihoti HEMC07096 Roodeplaat Gauteng -25.60398 28.35429 Pseudococcidae Phenacoccus manihoti SB215_1 Roodeplaat Gauteng -25.60398 28.35429 Pseudococcidae Phenacoccus manihoti SB215_2 Roodeplaat Gauteng -25.60398 28.35429 Pseudococcidae Phenacoccus manihoti SB215_3 Roodeplaat Gauteng -25.60398 28.35429 Pseudococcidae Phenacoccus manihoti SB215_4 Roodeplaat Gauteng -25.60398 28.35429 Pseudococcidae Phenacoccus manihoti SB215_5 Roodeplaat Gauteng -25.60398 28.35429 Pseudococcidae Planococcus citri (-) Durban KwaZulu-Natal -29.8458 31.0083 Pseudococcidae Planococcus citri HEMC00048 Cape Town Western Cape -34.0289 18.41937 Pseudococcidae Planococcus citri HEMC00480 Vredendal Western Cape 31.6658 18.5066 Pseudococcidae Planococcus citri HEMC00669 Pretoria Gauteng -25.73857 28.20733 Pseudococcidae Planococcus citri HEMC00721 Witrivier Mpumalanga -25.3622 31.2341 Pseudococcidae Planococcus citri HEMC00744 Emalahleni Mpumalanga -25.4684 31.04401 Pseudococcidae Planococcus citri HEMC00749 Emalahleni Mpumalanga -25.4684 31.04401 Pseudococcidae Planococcus citri HEMC00754 Pretoria Gauteng -25.73857 28.20733 Pseudococcidae Planococcus citri HEMC00764 Addo Eastern Cape -33.5685 25.69216 Pseudococcidae Planococcus citri HEMC00774 Addo Eastern Cape -33.5685 25.69216 Pseudococcidae Planococcus citri HEMC00787 Barberton Mpumalanga -25.7399 30.8566 Pseudococcidae Planococcus citri HEMC00788 Louis Trichardt Limpopo -23.0468 29.9196 Pseudococcidae Planococcus citri HEMC00790 Tzaneen Limpopo -23.816924 30.303665 Pseudococcidae Planococcus citri HEMC00808 Durban KwaZulu-Natal -29.8458 31.0083 Pseudococcidae Planococcus citri HEMC00845 Umkomaas KwaZulu-Natal -30.206082 30.795128 Pseudococcidae Planococcus citri HEMC00846 Umkomaas KwaZulu-Natal -30.206082 30.795128

198

Current family Species AccessionNumber Locality Province Latitude Longitude classification Pseudococcidae Planococcus citri HEMC00920 Rustenburg North West -25.7239 27.2912 Pseudococcidae Planococcus citri HEMC00941 Rustenburg North West -25.7239 27.2912 Pseudococcidae Planococcus citri HEMC01208 Pretoria Gauteng -25.73857 28.20733 Pseudococcidae Planococcus citri HEMC01452 Addo Eastern Cape -33.5685 25.69216 Pseudococcidae Planococcus citri HEMC02065 Zebediela Limpopo -24.2899 29.3238 Pseudococcidae Planococcus citri HEMC02069 Pretoria Gauteng -25.73857 28.20733 Pseudococcidae Planococcus citri HEMC02106 Pretoria Gauteng -25.73857 28.20733 Pseudococcidae Planococcus citri HEMC02883 Potgietersrus Limpopo -24.1324 29.0339 Pseudococcidae Planococcus citri HEMC03080 Louis Trichardt Limpopo -23.0468 29.9196 Pseudococcidae Planococcus citri HEMC03270 Addo Eastern Cape -33.5685 25.69216 Pseudococcidae Planococcus citri HEMC03560 Citrusdal Western Cape -32.5946 19.0286 Pseudococcidae Planococcus citri HEMC03568 Citrusdal Western Cape -32.5946 19.0286 Pseudococcidae Planococcus citri HEMC03571 Citrusdal Western Cape -32.5946 19.0286 Pseudococcidae Planococcus citri HEMC03612 Citrusdal Western Cape -32.5946 19.0286 Pseudococcidae Planococcus citri HEMC03623 Citrusdal Western Cape -32.5946 19.0286 Pseudococcidae Planococcus citri HEMC04206 Pretoria Gauteng -25.73857 28.20733 Pseudococcidae Planococcus citri HEMC04272 Zebediela Limpopo -24.2899 29.3238 Pseudococcidae Planococcus citri HEMC04399 Emalahleni Mpumalanga -25.4684 31.04401 Pseudococcidae Planococcus citri HEMC04432 Emalahleni Mpumalanga -25.4684 31.04401 Pseudococcidae Planococcus citri HEMC04770 Malelane Mpumalanga -25.2905 31.3136 Pseudococcidae Planococcus citri HEMC05014 Pretoria Gauteng -25.73857 28.20733 Pseudococcidae Planococcus citri HEMC05067 Emalahleni Mpumalanga -25.4684 31.04401 Pseudococcidae Planococcus citri HEMC05095 Emalahleni Mpumalanga -25.4684 31.04401 Pseudococcidae Planococcus citri HEMC05099 Mookgopong Limpopo -24.4275 28.59419 Pseudococcidae Planococcus citri HEMC05131 Roodeplaat Gauteng -25.60398 28.35429 Pseudococcidae Planococcus citri HEMC05139 Roodeplaat Gauteng -25.60398 28.35429 Pseudococcidae Planococcus citri HEMC05141 Emalahleni Mpumalanga -25.4684 31.04401 Pseudococcidae Planococcus citri HEMC05149 Emalahleni Mpumalanga -25.4684 31.04401 Pseudococcidae Planococcus citri HEMC05150 Pretoria Gauteng -25.73857 28.20733 Pseudococcidae Planococcus citri HEMC05156 Letsitele Limpopo -23.9056 30.3633 Pseudococcidae Planococcus citri HEMC05212 Letaba Mpumalanga -23.8602 30.29855 Pseudococcidae Planococcus citri HEMC05312 Letaba Mpumalanga -23.8602 30.29855

199

Current family Species AccessionNumber Locality Province Latitude Longitude classification Pseudococcidae Planococcus citri HEMC05350 Letaba Mpumalanga -23.8602 30.29855 Pseudococcidae Planococcus citri HEMC05352 Letaba Mpumalanga -23.8602 30.29855 Pseudococcidae Planococcus citri HEMC05354 Emalahleni Mpumalanga -25.4684 31.04401 Pseudococcidae Planococcus citri HEMC05363 Stellenbosch Western Cape -33.936557 18.86755 Pseudococcidae Planococcus citri HEMC05468 Zebediela Limpopo -24.2899 29.3238 Pseudococcidae Planococcus citri HEMC05490 Emalahleni Mpumalanga -25.4684 31.04401 Pseudococcidae Planococcus citri HEMC05494 Roodeplaat Gauteng -25.60398 28.35429 Pseudococcidae Planococcus citri HEMC05505 Roodeplaat Gauteng -25.60398 28.35429 Pseudococcidae Planococcus citri HEMC05507 Gordons Bay Western Cape -34.1745 18.8925 Pseudococcidae Planococcus citri HEMC05582 Roodeplaat Gauteng -25.60398 28.35429 Pseudococcidae Planococcus citri HEMC05623 Roodeplaat Gauteng -25.60398 28.35429 Pseudococcidae Planococcus citri HEMC05626 Pretoria Gauteng -25.73857 28.20733 Pseudococcidae Planococcus citri HEMC05628 Somerset West Western Cape -34.1009 18.8427 Pseudococcidae Planococcus citri HEMC05644 Emalahleni Mpumalanga -25.4684 31.04401 Pseudococcidae Planococcus citri HEMC05976 Rustenburg North West -25.7239 27.2912 Pseudococcidae Planococcus citri HEMC05979 Emalahleni Mpumalanga -25.4684 31.04401 Pseudococcidae Planococcus citri HEMC06406 Citrusdal Western Cape -32.5946 19.0286 Pseudococcidae Planococcus citri HEMC06471 Melmoth KwaZulu-Natal -28.5388 31.3075 Pseudococcidae Planococcus citri HEMC06548 Nkwalini Valley KwaZulu-Natal -28.726180 31.525150 Pseudococcidae Planococcus citri HEMC06549 Letsitele Limpopo -23.9056 30.3633 Pseudococcidae Planococcus citri HEMC06552 Citrusdal Western Cape -32.5946 19.0286 Pseudococcidae Planococcus citri HEMC06557 Emalahleni Mpumalanga -25.4684 31.04401 Pseudococcidae Planococcus citri HEMC06591 Stellenbosch Western Cape -33.936557 18.86755 Pseudococcidae Planococcus citri HEMC06627 Uitenhage area Eastern Cape -33.763741 25.393641 Pseudococcidae Planococcus citri HEMC06888 Ouwerf Citrusdal Western Cape -32.5946 19.0286 Pseudococcidae Planococcus citri HEMC06923 Pretoria Gauteng -25.73857 28.20733 Pseudococcidae Planococcus citri HEMC06998 Bonnievale Western Cape -33.9064 20.0089 Pseudococcidae Planococcus citri HEMC07023 Roodeplaat Gauteng -25.60398 28.35429 Pseudococcidae Planococcus citri HEMC07036 Roodeplaat Gauteng -25.60398 28.35429 Pseudococcidae Planococcus citri HEMC07037 Rietondale Gauteng -25.73286 28.30494 Pseudococcidae Planococcus citri SB18_2 Roodeplaat Gauteng -25.60398 28.35429 Pseudococcidae Planococcus ficus (-) Worcester Western Cape -33.633122 19.431285

200

Current family Species AccessionNumber Locality Province Latitude Longitude classification Pseudococcidae Planococcus ficus HEMC01064 Stellenbosch Western Cape -33.936557 18.86755 Pseudococcidae Planococcus ficus HEMC04968 Stellenbosch Western Cape -33.936557 18.86755 Pseudococcidae Planococcus ficus HEMC04973 Wellington Western Cape -33.5607 19.005 Pseudococcidae Planococcus ficus HEMC04976 Robertson Western Cape -25.336579 27.178802 Pseudococcidae Planococcus ficus HEMC04978 Robertson Western Cape -25.336579 27.178802 Pseudococcidae Planococcus ficus HEMC04980 Stellenbosch Western Cape -33.936557 18.86755 Pseudococcidae Planococcus ficus HEMC04981 Stellenbosch Western Cape -33.936557 18.86755 Pseudococcidae Planococcus ficus HEMC04986 Stellenbosch Western Cape -33.936557 18.86755 Pseudococcidae Planococcus ficus HEMC04988 Robertson Western Cape -25.336579 27.178802 Pseudococcidae Planococcus ficus HEMC04991 Stellenbosch Western Cape -33.936557 18.86755 Pseudococcidae Planococcus ficus HEMC04992 Robertson Western Cape -25.336579 27.178802 Pseudococcidae Planococcus ficus HEMC04994 Robertson Western Cape -25.336579 27.178802 Pseudococcidae Planococcus ficus HEMC04995 Stellenbosch Western Cape -33.936557 18.86755 Pseudococcidae Planococcus ficus HEMC04996 Stellenbosch Western Cape -33.936557 18.86755 Pseudococcidae Planococcus ficus HEMC04997 Robertson Western Cape -25.336579 27.178802 Pseudococcidae Planococcus ficus HEMC04999 Hex River Valley Western Cape -33.4541 19.609 Pseudococcidae Planococcus ficus HEMC05002 Hex River Valley Western Cape -33.4541 19.609 Pseudococcidae Planococcus ficus HEMC05005 Hex River Valley Western Cape -33.4541 19.609 Pseudococcidae Planococcus ficus HEMC05009 Hex River Valley Western Cape -33.4541 19.609 Pseudococcidae Planococcus ficus HEMC05013 Stellenbosch Western Cape -33.936557 18.86755 Pseudococcidae Planococcus ficus HEMC05018 Hex River Valley Western Cape -33.4541 19.609 Pseudococcidae Planococcus ficus HEMC05020 Stellenbosch Western Cape -33.936557 18.86755 Pseudococcidae Planococcus ficus HEMC05064 Wellington Western Cape -33.5607 19.005 Pseudococcidae Planococcus ficus HEMC05068 George Western Cape -33.7821 22.5491 Pseudococcidae Planococcus ficus HEMC05091 Stellenbosch Western Cape -33.936557 18.86755 Pseudococcidae Planococcus ficus HEMC05147 Hex River Western Cape -33.4541 19.609 Pseudococcidae Planococcus ficus HEMC05303 Hex River Western Cape -33.4541 19.609 Pseudococcidae Planococcus ficus HEMC05307 Hex River Western Cape -33.4541 19.609 Pseudococcidae Planococcus ficus HEMC05594 De Doorns Western Cape -33.695866 19.495344 Pseudococcidae Planococcus ficus HEMC05596 De Doorns Western Cape -33.695866 19.495344 Pseudococcidae Planococcus ficus HEMC05599 Stellenbosch Western Cape -33.936557 18.86755 Pseudococcidae Planococcus ficus HEMC06430 Robertson Western Cape -25.336579 27.178802

201

Current family Species AccessionNumber Locality Province Latitude Longitude classification Pseudococcidae Planococcus ficus HEMC06657 Ladismith Western Cape -33.5551 21.1821 Pseudococcidae Planococcus ficus HEMC06659 Stellenbosch Western Cape -33.936557 18.86755 Pseudococcidae Planococcus ficus HEMC06660 Stellenbosch Western Cape -33.936557 18.86755 Pseudococcidae Planococcus ficus HEMC06661 Stellenbosch Western Cape -33.936557 18.86755 Pseudococcidae Planococcus ficus HEMC06676 Stellenbosch Western Cape -33.936557 18.86755 Pseudococcidae Planococcus ficus HEMC06679 Porterville, Cape Western Cape -32.9675 19.0425 Pseudococcidae Planococcus ficus HEMC06683 Porterville, Cape Western Cape -32.9675 19.0425 Pseudococcidae Planococcus ficus HEMC06684 Robertson Western Cape -25.336579 27.178802 Pseudococcidae Planococcus ficus HEMC06685 Stellenbosch Western Cape -33.936557 18.86755 Pseudococcidae Planococcus ficus HEMC06692 Robertson Western Cape -25.336579 27.178802 Pseudococcidae Planococcus ficus HEMC06751 Stellenbosch Western Cape -33.936557 18.86755 Pseudococcidae Planococcus ficus HEMC06759 Worcester Western Cape -33.633122 19.431285 Pseudococcidae Planococcus ficus HEMC06823 Roodeplaat Gauteng -25.60398 28.35429 Pseudococcidae Planococcus ficus HEMC06839 Paarl Western Cape -33.731986 18.977615 Pseudococcidae Planococcus ficus HEMC06845 Stellenbosch Western Cape -33.936557 18.86755 Pseudococcidae Planococcus ficus HEMC06880 Marble Hall Mpumalanga -24.9637 29.2992 Pseudococcidae Planococcus ficus HEMC06882 Hex River Valley Western Cape -33.4541 19.609 Pseudococcidae Planococcus ficus HEMC06939 Groblersdal area Limpopo -25.134 29.4221 Pseudococcidae Planococcus ficus HEMC06965 Marble Hall Mpumalanga -24.9637 29.2992 Pseudococcidae Planococcus ficus HEMC06983 Ladismith Western Cape -33.5551 21.1821 Pseudococcidae Planococcus ficus HEMC07012 Paarl area Western Cape -33.731986 18.977615 Pseudococcidae Planococcus ficus HEMC07045 Pretoria central Gauteng -25.73857 28.20733 Pseudococcidae Pseudococcus longispinus (-) Pretoria Gauteng -25.73857 28.20733 Pseudococcidae Pseudococcus longispinus HEMC00519 Durban KwaZulu-Natal -29.8458 31.0083 Pseudococcidae Pseudococcus longispinus HEMC00655 Pretoria Gauteng -25.73857 28.20733 Pseudococcidae Pseudococcus longispinus HEMC00666 Stellenbosch Western Cape -33.936557 18.86755 Pseudococcidae Pseudococcus longispinus HEMC00673 Durban KwaZulu-Natal -29.8458 31.0083 Pseudococcidae Pseudococcus longispinus HEMC00724 Pretoria Gauteng -25.73857 28.20733 Pseudococcidae Pseudococcus longispinus HEMC00730 Emalahleni Mpumalanga -25.4684 31.04401 Pseudococcidae Pseudococcus longispinus HEMC00738 Pretoria Gauteng -25.73857 28.20733 Pseudococcidae Pseudococcus longispinus HEMC00418 Durban KwaZulu-Natal -29.8458 31.0083 Pseudococcidae Pseudococcus longispinus HEMC00828 Durban KwaZulu-Natal -29.8458 31.0083

202

Current family Species AccessionNumber Locality Province Latitude Longitude classification Pseudococcidae Pseudococcus longispinus HEMC00874 Durban KwaZulu-Natal -29.8458 31.0083 Pseudococcidae Pseudococcus longispinus HEMC00893 Durban KwaZulu-Natal -29.8458 31.0083 Pseudococcidae Pseudococcus longispinus HEMC00903 Pretoria Gauteng -25.73857 28.20733 Pseudococcidae Pseudococcus longispinus HEMC00908 Vryburg North West -26.958429 24.72976 Pseudococcidae Pseudococcus longispinus HEMC00913 Kuruman Northern Cape -27.473730 24.434667 Pseudococcidae Pseudococcus longispinus HEMC00916 Umkomaas KwaZulu-Natal -30.206082 30.795128 Pseudococcidae Pseudococcus longispinus HEMC00925 Stellenbosch Western Cape -33.936557 18.86755 Pseudococcidae Pseudococcus longispinus HEMC00973 Pretoria Gauteng -25.73857 28.20733 Pseudococcidae Pseudococcus longispinus HEMC01025 Stellenbosch Western Cape -33.936557 18.86755 Pseudococcidae Pseudococcus longispinus HEMC01042 Stellenbosch Western Cape -33.936557 18.86755 Pseudococcidae Pseudococcus longispinus HEMC01046 Stellenbosch Western Cape -33.936557 18.86755 Pseudococcidae Pseudococcus longispinus HEMC01060 Hout Bay Western Cape -34.0460 18.3652 Pseudococcidae Pseudococcus longispinus HEMC02120 Eshowe KwaZulu-Natal -28.7592 31.3845 Pseudococcidae Pseudococcus longispinus HEMC02181 Pretoria Gauteng -25.73857 28.20733 Pseudococcidae Pseudococcus longispinus HEMC02546 Mafikeng North West -25.8554 25.6414 Pseudococcidae Pseudococcus longispinus HEMC02620 Vryburg North West -26.958429 24.72976 Pseudococcidae Pseudococcus longispinus HEMC02635 Port Elizabeth Eastern Cape -33.93264 25.56995 Pseudococcidae Pseudococcus longispinus HEMC03257 Pretoria Gauteng -25.73857 28.20733 Pseudococcidae Pseudococcus longispinus HEMC03384 Pretoria Gauteng -25.73857 28.20733 Pseudococcidae Pseudococcus longispinus HEMC03417 Addo Eastern Cape -33.5685 25.69216 Pseudococcidae Pseudococcus longispinus HEMC03557 Pretoria Gauteng -25.73857 28.20733 Pseudococcidae Pseudococcus longispinus HEMC04266 Pretoria Gauteng -25.73857 28.20733 Pseudococcidae Pseudococcus longispinus HEMC04928 Pretoria Gauteng -25.73857 28.20733 Pseudococcidae Pseudococcus longispinus HEMC04989 Roodeplaat Gauteng -25.60398 28.35429 Pseudococcidae Pseudococcus longispinus HEMC04998 Emalahleni Mpumalanga -25.4684 31.04401 Pseudococcidae Pseudococcus longispinus HEMC05010 East London Eastern Cape -33.93197 18.4708 Pseudococcidae Pseudococcus longispinus HEMC05055 East London Eastern Cape -33.93197 18.4708 Pseudococcidae Pseudococcus longispinus HEMC05138 Roodeplaat Gauteng -25.60398 28.35429 Pseudococcidae Pseudococcus longispinus HEMC05140 Pretoria Gauteng -25.73857 28.20733 Pseudococcidae Pseudococcus longispinus HEMC05145 Emalahleni Mpumalanga -25.4684 31.04401 Pseudococcidae Pseudococcus longispinus HEMC05183 Stellenbosch Western Cape -33.936557 18.86755 Pseudococcidae Pseudococcus longispinus HEMC05188 Stellenbosch Western Cape -33.936557 18.86755

203

Current family Species AccessionNumber Locality Province Latitude Longitude classification Pseudococcidae Pseudococcus longispinus HEMC05199 Stellenbosch Western Cape -33.936557 18.86755 Pseudococcidae Pseudococcus longispinus HEMC05341 East London Eastern Cape -33.93197 18.4708 Pseudococcidae Pseudococcus longispinus HEMC05344 Letaba Mpumalanga -23.8602 30.29855 Pseudococcidae Pseudococcus longispinus HEMC05348 Tzaneen Limpopo -23.816924 30.303665 Pseudococcidae Pseudococcus longispinus HEMC05357 Stellenbosch Western Cape -33.936557 18.86755 Pseudococcidae Pseudococcus longispinus HEMC05407 Paarl Western Cape -33.731986 18.977615 Pseudococcidae Pseudococcus longispinus HEMC05460 Tzaneen Limpopo -23.816924 30.303665 Pseudococcidae Pseudococcus longispinus HEMC05466 Roodeplaat Gauteng -25.60398 28.35429 Pseudococcidae Pseudococcus longispinus HEMC05503 Somerset West Western Cape -34.1009 18.8427 Pseudococcidae Pseudococcus longispinus HEMC05642 Pretoria Gauteng -25.73857 28.20733 Pseudococcidae Pseudococcus longispinus HEMC05705 Pretoria Gauteng -25.73857 28.20733 Pseudococcidae Pseudococcus longispinus HEMC05981 Pretoria Gauteng -25.73857 28.20733 Pseudococcidae Pseudococcus longispinus HEMC05996 Emalahleni Mpumalanga -25.4684 31.04401 Pseudococcidae Pseudococcus longispinus HEMC06137 Emalahleni Mpumalanga -25.4684 31.04401 Pseudococcidae Pseudococcus longispinus HEMC06336 Hazyview Mpumalanga -25.0366 31.2001 Pseudococcidae Pseudococcus longispinus HEMC06358 Hazyview Mpumalanga -25.0366 31.2001 Pseudococcidae Pseudococcus longispinus HEMC06359 Pretoria Gauteng -25.73857 28.20733 Pseudococcidae Pseudococcus longispinus HEMC06411 Duiwelskloof Limpopo -23.6198 30.1142 Pseudococcidae Pseudococcus longispinus HEMC06481 Duiwelskloof Limpopo -23.6198 30.1142 Pseudococcidae Pseudococcus longispinus HEMC06559 Emalahleni Mpumalanga -25.4684 31.04401 Pseudococcidae Pseudococcus longispinus HEMC06590 Pretoria Gauteng -25.73857 28.20733 Pseudococcidae Pseudococcus longispinus HEMC06630 Brits North West -25.6622 27.796 Pseudococcidae Pseudococcus longispinus HEMC06632 Stellenbosch Western Cape -33.936557 18.86755 Pseudococcidae Pseudococcus longispinus HEMC06678 Swellendam Western Cape -34.024263 20.449523 Pseudococcidae Pseudococcus longispinus HEMC06695 Tsitsikama Western Cape -33.82676 23.71549 Pseudococcidae Pseudococcus longispinus HEMC06857 Robertson area Western Cape -25.336579 27.178802 Pseudococcidae Pseudococcus longispinus HEMC06897 Greyton Western Cape -34.0471 19.6077 Pseudococcidae Pseudococcus longispinus HEMC06899 Merwespont Western Cape -33.9702 20.1538 Pseudococcidae Pseudococcus longispinus HEMC06900 Buffeljagsrivier Western Cape -34.023871 20.460685 Pseudococcidae Pseudococcus longispinus HEMC06991 Gouda Western Cape -33.2466 19.0227 Pseudococcidae Pseudococcus longispinus HEMC06992 Merwespont Western Cape -33.9702 20.1538 Pseudococcidae Pseudococcus longispinus HEMC06993 Merwespont Western Cape -33.9702 20.1538

204

Current family Species AccessionNumber Locality Province Latitude Longitude classification Pseudococcidae Pseudococcus longispinus HEMC07019 Buffeljagsrivier Western Cape -34.023871 20.460685 Pseudococcidae Pseudococcus longispinus HEMC07021 Hoedspruit Limpopo -24.35452 30.95959 Pseudococcidae Pseudococcus longispinus HEMC07059 Pretoria Gauteng -25.73857 28.20733 Pseudococcidae Pseudococcus longispinus HEMC07101 Vredehuis Gauteng -25.71 28.1917 Pseudococcidae Pseudococcus viburni (-) Hex River Valley Western Cape -33.4541 19.609 Pseudococcidae Pseudococcus viburni HEMC06658 Stellenbosch Western Cape -33.936557 18.86755 Pseudococcidae Pseudococcus viburni HEMC06663 Hex River Valley Western Cape -33.4541 19.609 Pseudococcidae Pseudococcus viburni HEMC06666 Stellenbosch Western Cape -33.936557 18.86755 Pseudococcidae Pseudococcus viburni HEMC06677 Stellenbosch Western Cape -33.936557 18.86755 Pseudococcidae Pseudococcus viburni HEMC06690 Villiersdorp Western Cape -33.684696 18.933101 Pseudococcidae Pseudococcus viburni HEMC06694 Elgin Western Cape -34.0983 18.9879 Pseudococcidae Pseudococcus viburni HEMC06696 Elgin Western Cape -34.0983 18.9879 Pseudococcidae Pseudococcus viburni HEMC06697 Grabouw Western Cape -34.0488 19.0886 Pseudococcidae Pseudococcus viburni HEMC06760 Pretoria Gauteng -25.73857 28.20733 Pseudococcidae Pseudococcus viburni HEMC06851 Pretoria Gauteng -25.73857 28.20733 Pseudococcidae Pseudococcus viburni HEMC06864 Pretoria Gauteng -25.73857 28.20733 Pseudococcidae Pseudococcus viburni HEMC06865 Pretoria Gauteng -25.73857 28.20733 Pseudococcidae Pseudococcus viburni HEMC06997 Hermanus Western Cape -34.42041 19.24101 Pseudococcidae Pseudococcus viburni HEMC07090 Hermanus Western Cape -34.42041 19.24101 Pseudococcidae Vryburgia transvaalensis (-) Settlers Limpopo -24.9231 28.5196 Pseudococcidae Vryburgia transvaalensis HEMC00571 Louis Trichardt Limpopo -23.0468 29.9196 Pseudococcidae Vryburgia transvaalensis HEMC00667 Louis Trichardt Limpopo -23.0468 29.9196 Pseudococcidae Vryburgia transvaalensis HEMC00671 Pretoria Gauteng -25.73857 28.20733 Pseudococcidae Vryburgia transvaalensis HEMC00693 Emalahleni Mpumalanga -25.4684 31.04401 Pseudococcidae Vryburgia transvaalensis HEMC00757 Pretoria Gauteng -25.73857 28.20733 Pseudococcidae Vryburgia transvaalensis HEMC01002 Modderrivier Northern cape -28.9736 24.4734 Pseudococcidae Vryburgia transvaalensis HEMC01199 Citrusdal Western Cape -32.5946 19.0286 Pseudococcidae Vryburgia transvaalensis HEMC02068 Cullinan Gauteng -25.6725 28.5258 Pseudococcidae Vryburgia transvaalensis HEMC02095 Stellenbosch Western Cape -33.936557 18.86755 Pseudococcidae Vryburgia transvaalensis HEMC02592 Pretoria Gauteng -25.73857 28.20733 Pseudococcidae Vryburgia transvaalensis HEMC04186 Pretoria Gauteng -25.73857 28.20733 Pseudococcidae Vryburgia transvaalensis HEMC04258 Cape Town Western Cape -34.0289 18.41937

205

Current family Species AccessionNumber Locality Province Latitude Longitude classification Pseudococcidae Vryburgia transvaalensis HEMC05472 Pretoria Gauteng -25.73857 28.20733 Pseudococcidae Vryburgia transvaalensis HEMC06554 Paarl Western Cape -33.731986 18.977615 Pseudococcidae Vryburgia transvaalensis HEMC06571 Pretoria Gauteng -25.73857 28.20733 Pseudococcidae Vryburgia transvaalensis HEMC06601 Pretoria Gauteng -25.73857 28.20733 Pseudococcidae Vryburgia transvaalensis HEMC06609 Stellenbosch Western Cape -33.936557 18.86755 Pseudococcidae Vryburgia transvaalensis HEMC06691 Hex River Valley Western Cape -33.4541 19.609 Pseudococcidae Vryburgia transvaalensis HEMC06871 Wellington Western Cape -33.5607 19.005 Pseudococcidae Vryburgia transvaalensis HEMC06879 Somerset West Western Cape -34.1009 18.8427 Pseudococcidae Vryburgia transvaalensis HEMC06943 Paarl Western Cape -33.731986 18.977615 Pseudococcidae Vryburgia transvaalensis HEMC07033 Paarl Western Cape -33.731986 18.977615 Pseudococcidae Vryburgia transvaalensis HEMC07105 Paarl Western Cape -33.731986 18.977615

206

Appendix 4.2: Model performance results

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