A Molecular Phylogenetic Study and the Use of Dna Barcoding
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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 by MAMADI THERESA SETHUSA THESIS submitted in fulfillment of the requirements for the degree PHILOSOPHIAE DOCTOR in 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) i 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 ii 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 iv 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. v 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. vi 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.