An investigation of patterns of connectivity among populations of the Australian mosquito (Aedes vigilax) using mitochondrial sequences and microsatellite loci

Author Shibani, Naema S.

Published 2010

Thesis Type Thesis (PhD Doctorate)

School Griffith School of Environment

DOI https://doi.org/10.25904/1912/945

Copyright Statement The author owns the copyright in this thesis, unless stated otherwise.

Downloaded from http://hdl.handle.net/10072/366811

Griffith Research Online https://research-repository.griffith.edu.au An investigation of patterns of connectivity among populations of the Australian mosquito (Aedes vigilax) using mitochondrial sequences and microsatellite loci

Naema S. Shibani B. Sc. (M. Sc.)

Griffith School of Environment Science, Environment, Engineering and Technology Griffith University And Australian Rivers Institute

Aedes vigilax (Photo courtesy QLD Museum, photographer Jeffrey Wright)

Submitted in fulfilment of the requirements of the degree of Doctor of Philosophy October 2009 Summary

The fluctuating changes in climate during the Pleistocene period played an important role in population genetic structure of many species in northern and southern Australia. I investigated the population structure and history of a widespread Ross River Virus vector, the Australian saltmarsh mosquito (Aedes vigilax) across the continent. The main aim of this study was to determine levels of connectivity among sites across the continent. This information is important for disease control, because it can inform about the likely spread of pesticide resistance, as well as the scale at which control measures should be undertaken.

In this study, the genetic structure of 379 individuals of this vector (Ae. vigilax) (Skuse) was examined and a comparative approach was taken in analysing patterns of genetic variation. The methods were mitochondrial COI sequence variation and nuclear variation at three microsatellite loci. Analysis of mitochondrial DNA data detected 119 haplotypes of which more than 50% were unique. Bayesian analysis revealed two distinctly divergent clades. Clade-I was only found in eastern Australia (Queensland and New South Wales), whereas Clade-II was found throughout the sampling area (Northern Territory, Western and eastern Australia). A molecular clock calibration was used to estimate the divergence time between the two clades to be 0.924 million years. The Nested Clade Analysis (NCA) indicated that there was significant geographical structure and suggested divergence between clade-I and clade-II by allopatric fragmentation. Increasing aridity during the Pleistocene was suggested as a factor in structuring Ae. vigilax populations. The Carpentarian barrier in the north was hypothesized to be associated with the divergence between the eastern and western clades. In addition, the Murchison arid barrier in the west could explain the low gene flow between south-western population (Perth site) and other western sites.

A recent demographic expansion in the late Pleistocene (6 000 – 13 000 years ago) was inferred from haplotype mismatch distribution analysis. Fu’s Fs tests also indicated evidence of significant population expansion at most sites.

II Nested clade analysis demonstrated a range expansion in the western clade, suggesting movement from west to east and from north-western to south-western Australia; while the range expansion in the eastern clade populations was proposed to be from south-eastern to north-eastern. An analysis of molecular variance (AMOVA) indicated contemporary structure at different geographic scales. Overall the mitochondrial gene showed significant levels of population structuring for the subdivision of populations into eastern and western regions. The analysis indicated that individuals of clade-I showed very little genetic structure, whereas clade-II did have significant structure, mostly explained by samples from south-western Australia. The AMOVA analysis of nuclear genes showed some spatial genetic structuring indicating limited gene flow among populations. Microsatellite data supported the geographic structure between east and west, and a significant isolation by distance effect.

I hypothesise that the two clades may have diverged on opposite sides of the continent during the Pleistocene about one million years ago. More recently there has been subsequent secondary contact caused by clade-II individuals expanding their range from west to east. Clade-II populations also showed expansion from the north-west to the south-west; whereas clade-I expanded but only along the east coast. The overlap of eastern clade haplotypes in the eastern region suggested secondary contact among eastern and western clades in eastern Australia. Based on microsatellite data, there was no evidence that the divergence had led to speciation, tests for Hardy Weinberg Equilibrium, Linkage Disequilibrium and Assignment analyses provided no evidence for reproductive isolation between these two clades. This suggests that the Australian Ae. vigilax is not a cryptic species complex.

In summary, this study demonstrated the importance of contemporary and historical processes in determining the population genetic structure of Australian Ae. vigilax. It suggests that the arid barriers may have had an impact on geographic variation of widespread populations of this species to form east-west fragmentation during the Pleistocene period. The data reveal patterns of recent population expansion of Ae. vigilax populations that in the west expanded from west to east. Then subsequently secondary contact occurs with populations in the east. Studying the ability of Ae.

III vigilax to disperse is important and may have relevance to control programs to prevent the spread of arboviruses carried by this species.

IV Acknowledgements

I would like first to thank my supervisors Professor Jane Hughes and Professor Pat Dale. To Jane for directing me to the world of genetics to explore and better understand genetic concepts and theories in related to molecular biology. Thanks for her helpful discussion and encouraging me during this research. In turn to Pat for her advice, support, guidance and patience, which are much appreciated. Special thanks to both for comments, feedback and editing my drafts. Without their suggestions and support, this project would not have been possible.

Particular thanks go to Dr. Daniel Schmidt, who was enormously helpful with statistical support and specific software for data analyses, and some suggestions throughout this project.

I’d also like to sincerely acknowledge the effort of all the staff that provided samples from different states and territories around Australia, including Richard Russell, John Clancy, John Haniotis, Roy Durre, Dave Allaway, Mike Muller, Scott Ritchie, Andrew Van Den Hurk, Jonathan Lee, Peter Whelan, Nina Kurucz, Huy Nguyen, Michael Lindsay, Cheryl Johansen, Craig Williams and Samantha Williams. Thanks to Leon Hugo for your information and thank you, Ian Myles (Brisbane City Council) for your company in the field and provide the traps. Thanks also to J. Wright (Queensland Museum) for his photo of Ae. vigilax, and grateful to Leila Eslami- Endargoli and Siti Amri for willing assistance in GIS.

I have been fortunate to work in Professor Hughes’s lab; thanks to Jane and the Geeks who added to my knowledge and skills through discussions in group meetings. I am very grateful to all the people that taught me lab techniques, especially Jing Ma, Mia Hillyer, Jemma Somerville and Simon Song, and thanks to all lab geeks for advice and learning experience in the lab. Dan, Rod, Joel, Jimmy, Alison, Issa, Ben, Tim, Steve, Ryan, Kate, Giovanella, Arlene, Tayner, Abraham, Jodie, Ana, Courtenay, Kathryn, Andrew, Sofie, Bob, Olivier, Rachel, Suman, Risto. Thanks also to Jacinta Zalucki, Alicia Toon and Mark Ponniah and for their comments.

V

Thanks also to Professor Stuart Bunn, Gina, Tanya, Lacey, and Sally in Australian Rivers Institute for their co-operation, for always being there to answer questions, and for providing conference funding. I would also like to thank Petney Dickson for her administrative assistance and all the support staff at Griffith School of Environment and Griffith University.

I am indebted to my colleagues, Shahla, Leila, Mahdi, Patricia, Eman, Rawaa, Siti, Paula, Peter and Mirriam for their friendship and support during the course of my project.

Thanks to my family, brothers and sisters faraway and who have helped probably more than they realise; they were behind me all the way!

This thesis is dedicated to the memory of my parents, for their spiritual support, who sadly passed away before my thesis was completed. My father S. Shibani and my mother S. Turki appreciated the value of knowledge.

Lastly, but definitely not least, special thanks to my husband Bashir for his sacrifices and encouragement to pursue my dreams, and to my children Amira, Amel, Amani and Khalid for their patience, support and love during my endeavours.

This research was funded by Libyan Government and an additional scholarship was provided from Australia, Griffith School of Environment; and also the lab funding was provided by the Griffith School of Environment.

VI

Declaration

This work has not previously been submitted for a degree or diploma in any university. To the best of my knowledge and belief, the thesis contains no material previously published or written by another person except where due reference is made in the thesis itself.

……………………….

Naema Shibani

VII

Table of Contents

(a) Summary………………………………………………………………… II (b) Acknowledgments……………………………….………………………..V (c) Declaration………………………………………………………………VII (d) Table of Contents………………………………..……………………...VIII (e) List of Figures……………………………….…………………………...XII (f) List of Tables…………………………………..………………………..XIV

Chapter 1: Introduction...... 1 1.1 Genetic Structure of Populations...... 4 1.2 Techniques applied to measure gene flow in natural populations...... 5 1.2.1 Mitochondrial DNA...... 6 1.2.2 Microsatellites...... 7 1.2.3 Phylogeography...... 8 1.2.4 Speciation...... 12 1.3 Climate and Barriers in Australia...... 14 1.4 The Species...... 15 1.5 Classification...... 16 1.6 Life cycle...... 17 1.7 Ecology and Distribution...... 19 1.8 Dispersal...... 22 1.9 Vector-borne disease...... 23 1.9.1 Ross River Virus (RRV)…...... 26 1.9.2 Barmah Forest Virus (BFV)...... 27 1.9.3 Japanese Encephalitis Virus (JEV)...... 28 1.9.4 Edge Hill virus (EHV) ...... 29 1.9.5 Stratford virus (STRV)...... 30 1.10 Study aims and hypotheses...... 30

Chapter 2: Material and methods...... 32 2.1 Mosquito Sampling...... 32 2.2 Study Area...... 34 2.3 Sampling & Methodology...... 36

VIII

2.3.1 Field Work (sampling)...... 36 2.3.2 Lab Work (Methodology) ...... 37 2.3.2.1 Identification...... 37 2.3.2.2 DNA extraction and amplification...... 38 2.3.2.3 Purification, sequencing and alignment...... 38 2.4 Microsatellite loci...... 39 2.5 Data analysis...... 41 2.5.1 Phylogenetic trees...... 41 2.5.2 Nested clade analysis...... 42 2.5.3 Molecular clock and divergence...... 43 2.5.4 Neutrality tests...... 43 2.5.5 Mismatch distributions...... 44 2.5.6 Analysis of Molecular Variance...... 45 2.5.7 Mantel tests...... 45 2.5.8 Hardy-Weinberg Equilibrium...... 46 2.5.9 Linkage Disequilibrium...... 46 2.5.10 Assignment tests...... 47

Chapter 3: Analysis of genetic structure in Aedes vigilax, using a fragment of the mitochondrial COI gene...... 48 3.1 Introduction...... 48 3.2 Material and methods...... 49 3.3 Data analysis...... 50 3.4 Results...... 51 3.4.1 Bayesian analysis...... 51 3.4.2 Divergence time and mismatch distribution analysis...... 53 3.4.3 Genetic diversity...... 54 3.4.4 Neutrality tests...... 54 3.4.5 AMOVA...... 58 3.4.6 F-statistics...... 58 3.4.7 Isolation by Distance...... 61 3.4.8 Network and Nested Clade Analysis...... 61 3.5 Discussion ...... 66

IX

3.5.1 Clade – I...... 68 3.5.2 Clade – II...... 70 3.6 Conclusion...... 71

Chapter 4: Is there reproductive isolation between two mitochondrial clades of the Ross River Virus Vector (Ae. vigilax)?...... 73 4.1 Introduction...... 73 4.2 Methods...... 76 4.2.1 Identification of microsatellite loci...... 76 4.2.2 Amplification and screening of microsatellite loci...... 77 4.3 Data analysis...... 78 4.3.1 Hardy Weinberg Equilibrium...... 78 4.3.2 Linkage Disequilibrium...... 79 4.3.3 Assignment tests...... 79 4.4 Results...... 80 4.5 Discussion...... 85 4.6 Conclusion...... 87

Chapter 5: Microsatellite variation at the continental scale in Ae. vigilax…88 5.1 Introduction...... 88 5.2 Methodology...... 90 5.3 Data analysis...... 90 5.4 Results...... 90 5.4.1 Allele frequencies...... 90 5.4.2 Hierarchical analysis...... 93

5.4.3 Pairwise FST comparisons...... 95 5.4.4 Mantel tests...... 97 5.5 Discussion...... 97 5.6 Conclusion...... 99

Chapter 6: General Discussion...... 100 6.1 Phylogeographic pattern and evolutionary history...... 100 6.2 AMOVA, expansion and gene flow as evidence for contemporary structure in

X

Australian Ae. vigilax species...... 105 6.3 Further Research...... 107 6.4 Implications for vector control...... 108 References...... 109 Appendix 1: Ae. vigilax sampling sites and the geographic locations for all sequences used in chapter 3...... 126 Appendix 2: Haplotypes sequence of mitochondrial DNA for Ae. vigilax from chapter 3...... 127

XI

List of Figures

Figure 1.1: Development of phylogenetic structuring of alleles between two populations (Moritz 1994) ...... 9 Figure 1.2: Haplotype network show relationship among DNA sequences...... 10 Figure 1.3: Five categories of phylogeographic structure (Avise et al 1987) ...... 12 Figure 1.4: Map of Australia showing locations of the Carpentarian Barrier, Murchison Barrier and the historical interrelationships of eight areas of endemism in Australia with postulated isolating barriers (A-H) as adapted from Cracraft (1986); Ford (1987)...... 15

Figure 1.5: Ae. vigilax (by S. Doggett)...... 16 Figure1.6: Taxonomic characteristic features of Ae. vigilax (Russell 1993)...... 17 Figure 1.7: The typical lifecycle of a mosquito (Webb & Russell 2007)...... 18

Figure 1.8: The geographic distribution of saltmarsh mosquito in Australia...... 20

Figure 1.9: Mangrove and salt marsh area (photo by Pat Dale, Griffith University)...... 20

Figure 2.1: Map showing sampling locations for Ae. vigilax in Australia...... 33

Figure 2.2: Map showing sampling sites for Ae. vigilax in eastern Australia (QLD &NSW)...... 35 Figure 2.3: Map showing sampling sites for Ae. vigilax in Northern Territory and Western Australia...... 36 Figure 2.4: Mosquito sampling trap...... 37

Figure 3.1: Phylogenetic relationships among 119 mtDNA haplotypes of Ae. vigilax individuals constructed using MrBayes...... 52 Figure 3.2: Mismatch distribution of Ae. vigilax for clade-I & clade-II...... 53

Figure 3.3: Scatter diagram of geographic distance and Slatkin’s linearized FST between eastern and western populations for mtDNA data...... 61 Figure 3.4: Network showing the relationships between 119 haplotypes found in 318 individuals and the arrangement of nested clade for Ae. vigilax. Haplotypes in clade-I and clade-II (approximately 2.1% divergent). The lines between haplotypes indicate a single mutation. Missing haplotypes are indicated by black circles...... 64 Figure 4.1: Assignment test using multilocus genotype, showing the percentage of Ae. vigilax clades correctly or wrongly assigned to the mtDNA clades. Brisbane sites (Hem, Ban & Bri), Gold Coast sites (Hel & Ruc), Cairns site (Sma) and Sydney sites (New & Sta)...... 84

XII

Figure 5.1a: Pie charts representing allele frequencies at the AP5 locus for Ae. vigilax at 13 sites...... 91 Figure 5.1b: Pie charts representing allele frequencies at the JO338 locus for Ae. vigilax at 13 sites...... 92 Figure 5.1c: Pie charts representing allele frequencies at the GT48 locus for Ae. vigilax at 13 sites...... 92 Figure 5.2: Scatter diagram of geographic distance and genetic distances between eastern and western populations for three loci...... 97 Figure 6.1: Hydrologic zonation of Australia at present and at the glacial maximum using the climatic index (Ford1987)...... 102 Figure 6.2: Hypothesis of population divergence and isolation in the Australian Ae. vigilax...... 103 Figure 6.3: Scenario range expansion of Ae. vigilax in both clades with secondary contact by clade-II individuals during expanding their range from west to east...... 103

XIII

List of Tables

Table 1.1: Numbers of vector-borne disease infections reported in Australia from 1991 to 2009 (Australian Government Department of Health and Ageing)...... 25 Table 2.1: Collection localities and sample sizes for all Ae. vigilax examined...... 33 Table 2.2: Primers for 47 microsatellite loci screened for Ae. vigilax that had been developed for other mosquito species...... 40 Table 3.1: Mismatch distribution results for clade I and clade II for Ae. vigilax...... 54

Table 3.2 (a) Diversity measures for populations of clade-I using 433 bp of COI. Numbers in parenthesis are standard deviations...... 55 Table 3.2 (b) Diversity measures for populations of clade-II...... 56 Table 3.3 Results of Tajima’s D and Fu’s Fs tests for Ae. vigilax populations; shows clade-I and clade-II...... 57 Table 3.4 Results of Overall AMOVA analysis for Ae. vigilax from all sites...... 59 Table 3.5 Results of hierarchical AMOVA for Ae. vigilax based on 433bp of COI sequence...... 59

Table 3.6 Pairwise FST values based on mtDNA differentiation among 13 sites for Ae. vigilax...... 60

Table 3.7 NCA results showing only clades with significant geographical relationships based on the X2 test...... 65 Table 4.1: Characteristics of three microsatellite loci analysed for Ae. vigilax...... 77

Table 4.2: Hardy Weinberg equilibrium tests (FIS-values) for mosquito populations from 13 sites at three microsatellite loci...... 80 Table 4.3: Three microsatellite loci analysed for Ae. vigilax populations in Australia...... 81 Table 4.4: Multilocus Linkage Disequilibrium in thirteen Ae. vigilax populations ...... 82 Table 4.5: Percentage of Ae. vigilax individuals correctly and wrongly assigned to their clades, using the Rannala & Mountain 1997 method with 90% level of confidence...... 85

Table 5.1: Microsatellite allele frequencies for each locus at 13 locations ...... 93 Table 5.2: AMOVA results based on three microsatellite loci...... 94

Table 5.3: Overall FST values for each microsatellite locus among Ae. vigilax samples ...... 94

Table 5.4: Pairwise FST values based on three microsatellite loci among Ae. vigilax sites. Significant values in bold. Squares indicate comparisons between sites in the same region...... 96

XIV Chapter 1 ______

Chapter 1

General Introduction

Mosquitoes are responsible for the transmission of viral infections that cause considerable morbidity and mortality. The diseases are known as Arboviruses (Arthropod-borne viruses) and they are transmitted via mosquito bites. Generally, mosquitoes are distributed across Australia, occupying a diverse array of habitats. There are approximately 300 species of mosquito recorded in Australia (Russell 1993).

The mosquito Aedes vigilax (Skuse) (Diptera: Culicidae) is one of the main vectors of arboviral disease in coastal regions of Australia. Ae. vigilax is responsible for transmitting many diseases but the most important and widespread is Ross River Virus. Determining the extent of dispersal and gene flow amongst the Ae. vigilax subpopulations is relevant to vector control and disease prevention.

Dry climatic and environmental conditions will make habitat less hospitable for dispersing across and have an effect on the genetic structure of a species. The genetic differentiation of populations occurs as a result of dispersal or vicariance. Dispersal is the result of the active or passive movement of the organism from an ancestral centre of origin into new areas. Vicariance is where past geological or environmental events split the continuous range of an organism (Avise 2000; Hedrick 2005).

Evolution occurs by natural selection in the presence of genetic variation in a population over time. Population genetics is the study of how this variation changes, from parents to offspring, generation after generation under the effect of both systematic and random evolutionary forces, such as natural selection and mutation (Snustad & Simmons 1997). Although mutation is the primary cause of variation, natural selection plays the main role in creating new species (Nei 1987). Population

1 Chapter 1 ______genetics and phylogeography are used to estimate natural population differentiation and the evolutionary history of populations.

More specifically, phylogeography is used to determine current and historical genetic structure in populations based on their geographical distribution and can be used to deduce the evolutionary processes that result in current distributions (Avise 2000). For example, when the dispersal of organisms is restricted by barriers or environmental events, the populations may become genetically divergent, due to natural selection and/ or random genetic drift. Conversely, gene flow between two populations results in a shared genetic structure between geographically separate populations with slight differentiation (Slatkin 1985). Even a small amount of movement is sufficient to prevent any significant genetic differentiation (Nei 1975; Hartl & Clark 1997). The migration of breeding animals to or from a population can also cause changes in gene frequencies (Pirchner & Krosigk 1969).

Dispersal is important for insects to feed, find mates, find oviposition sites and detect suitable habitats. Many studies have shown that habitat fragmentation and the life history of a species affect levels of dispersal. Fragmented habitat could well select for increased flight ability to enable long distance flight between fragments (Taylor & Merriam 1995). On the other hand, evolutionary changes in dispersal as a response of habitat change and fragmentation can lead to expanded dispersal when insects are struggling with spatial and environmental heterogeneity (Hill et al. 1999; Thomas 1999). For example, many flying adult aquatic insect species easily disperse through the surrounding environment, leading to high gene flow between populations (Hughes 2007). In contrast, terrestrial species lacking an adult flying stage are likely to exhibit less gene flow between populations.

Flight of insects from one place to another is usually termed dispersal or migration, which are important determinants in the genetic structure of populations (Roff 1974; Spieth 1974, 1979). Migration usually refers to the movement of insects where flight seems to be in a specific direction, and is usually used to describe movements of a whole population of animals, as well as often being seasonal, with the individuals or their offspring frequently returning to their parent’s place of origin (Williams 1961; Silver 2008). Migratory flight is often considered to be linked to the biology and

2 Chapter 1 ______survival of the species and is relatively controlled and persistent. Dispersal is sometimes thought to represent random flight of insects. It is commonly held to describe more or less random flights, with wind often playing an important role (Williams 1961; Pielou 1977). Also, dispersal is an important factor in mosquito ecology (Lee et al. 1984). Some species have a clear migratory behaviour that is part of their normal biology (Russell 1993).

Environmental factors also play an important role in determining host seeking behaviour. Adult mosquito activity may be absent in south-eastern Australia when temperatures are below 8ºC; high humidity and even a little rain can significantly increase activity at optimal temperatures (Russell 1993). For example Barmah Forest Virus outbreaks appear to be associated with several factors such as rainfall, when coupled with high tides which result in very large populations of Ae. vigilax (Doggett et al. 1999).

An important factor in the ecology of mosquito-borne disease is the dispersal capacity of its mosquito vectors. Knowledge of genetic variation, gene flow and dispersal of mosquitoes is important for understanding vector transmission and to inform disease control (Tabachnick & Black 1995). From an ecological perspective, Ae. vigilax may disperse for many kilometres (Russell 1993). For example, Marks (1969) found Ae. vigilax on Heron Island (Great Barrier Reef), even though no suitable breeding sites existed there. In addition, Ae. vigilax has desiccation resistant eggs that can survive in the dry season for long periods of time (Lindsay et al. 1993b). This means that Ae. vigilax possibly has the ability to disperse even to arid regions of Australia. This is important for three reasons. First, knowledge of the dispersal abilities of a species allows prediction of the rate of recolonization following local extinctions caused for example by pesticide application. Second, it allows some prediction of the rate of spread of any new virus infections. Third, it will indicate the potential spread of resistance to a particular pesticide.

Molecular research has been useful in other mosquito-borne diseases. Since 1991 there have been many molecular studies on malaria vectors, mostly directed toward the development of malaria control strategies by identification, detection and

3 Chapter 1 ______monitoring of insecticide susceptibility, and resistance and the determination of the genetic structure and gene flow among populations (Collins et al. 2000; Ranson et al. 2000; Santolamazza et al. 2008). Genetic techniques and molecular tools have also been used in the control of populations of the dengue virus vector Ae. aegypti (Higgs et al. 1998; Adelman et al. 2007). To date, only a single genetic study has been conducted on Ae. vigilax (Chapman et al. 1999). That study used six allozyme loci to identify genetic differentiation between samples from ponds in different localities in south-east Queensland. In this study, genetic techniques, mtDNA and microsatellite markers are used to examine genetic variation, gene flow and dispersal of Australian Ae. vigilax populations, which is essential to realize vector transmission and may assist in control programs to prevent the spread of disease.

1.1 Genetic Structure of Populations

Population genetics is a branch of genetic research focused on identifying genetic structure and the distribution of genes within and between populations and understanding the evolutionary processes that affect these populations. The different patterns of the distribution and composition of alleles within and between populations at differing temporal and geographic scales are created by evolutionary, ecological and molecular processes (Avise 2000).

Natural populations contain a large amount of variability; some part of this variability is evidently environmental, but a large part is genetically determined (Nei 1975). The structure of natural populations consists of two parts: demographic structure affected by births, deaths and migration of individuals across the entire distribution of the population, and the genetic structure influenced by processes such as mutation, genetic drift, selection and gene flow (Slatkin 1994).

In brief, genetic variation within and between populations of a single species is maintained by: (1) mutation; (2) genetic drift; (3) selection (Endler 1986); (4) dispersal (Hartl & Clark 1997) and (5) recombination; although their relative effects may be different (Hedrick 2005). Gene flow results in the dispersal of genes or gametes from one population to another (Slatkin 1985). Gene flow contributes towards the maintenance of genetic similarity among conspecific populations. In large

4 Chapter 1 ______populations the amount of variation within and among populations depends on gene flow and selection, while mutation and genetic drift are less important. In the absence of selection, limited gene flow will lead to genetic differentiation among populations due to genetic drift (Crow & Aoki 1984; Slatkin 1985). As gene flow among populations increases, the genetic population structure among populations becomes more homogeneous (Allendrof 1983), because there is a close relationship between gene flow and genetic differentiation. Although the biology of the organism affects gene flow, historical processes also strongly influence observed patterns of gene flow in natural populations (Avise 1994). Genetic drift caused by the random sampling of genes at the time of zygote formation results in local genetic differentiation unless it is opposed by gene flow. The historical biogeographic and demographic events during the Pleistocene have had significant evolutionary effects, and demonstrate that genetic drift plays an important role in species divergence, even in the absence of long- standing geographic barriers (Knowles & Richards 2005). If there are low levels of gene flow, well-adapted combinations of genes can be fixed in one local population through the combined action of genetic drift and natural selection and then spread to other local populations by gene flow (Slatkin 1985). Genetic variation also occurs when populations have been isolated from each other by barriers for long periods of time. This may give rise to new species that are at least partially reproductively isolated from each other; when the new species become mixed due to secondary contact, there may be no interbreeding between them (Avise 2004; Hedrick 2005).

1.2 Techniques applied to measure gene flow in natural populations

The first molecular technique used to study gene flow in natural populations was allozymes electrophoresis (Richardson et al. 1986). This technique allowed the analysis of the gene frequency differences within and between species using statistical methods (Nei 1987).

Important recent advances in molecular genetics have increased the power of population genetics in biology including the development of the polymerase chain reaction (PCR); application of evolutionarily conserved sets of PCR primers

5 Chapter 1 ______(mtDNA); initiation of hypervariable microsatellite loci; and initiation of routine DNA sequencing in biology laboratories (Sunnucks 2000).

These advances combined with recent powerful statistical analysis and computer programs have enhanced the power of molecular genetic analysis (Sunnucks 2000; Jones & Ardren 2003). Mitochondrial DNA and microsatellites are the most commonly used methods in population genetics in recent times (Jones & Ardren 2003).

1.2.1 Mitochondrial DNA

Mitochondrial DNA is a double stranded genome, separate from the nuclear chromatin which occurs in mitochondria (Boore 1999). It is very commonly used in studies of molecular phylogenetics and phylogeography (Moritz et al. 1987). Its molecules generally contain 37 genes. It ranges in size between 14 and 20 kilobases (kb) in length with only a few exceptions known. MtDNA exists as a single, circular molecule within each mitochondrion (Wolstenholme 1992; Boore 1999). With the single copy of each nuclear gene that is inherited from each egg cell, large numbers of mitochondria coexist within an egg and are passed to the next generation through maternal inheritance (Avise 1998), that is it shows non-mendelian inheritance. Due to their uniparental maternal inheritance and their haploid nature, mtDNA genes have a

fourfold smaller effective population size (Ne) than nuclear genes (Birky-Jr et al. 1989). This difference in transmission and some major differences in patterns of evolution cause mtDNA and nuclear DNA (nDNA) gene genealogies to reflect different aspects of population biology and history (Sunnucks 2000). Several attributes of mtDNA have led to its wide popularity and utility for analysis of genetic variation within a species. Due to its smaller Ne, the effect of genetic drift and limited gene flow should be more visible in mtDNA than in nDNA, thereby enhancing the usefulness of mtDNA as a population genetic marker. Moreover, its high mutation rate provides a large number of alleles for genetic drift and gene flow (Moritz et al. 1987). These characteristics make a gender specific marker such as mtDNA a powerful locus for distinguishing models of demographic structure in natural systems (Wollenberg & Avise 1998).

6 Chapter 1 ______Genetic diversity may reflect phylogenetic relationships at the population or species level, or may summarize relationships among alleles as in mtDNA gene trees (Avise 1994). Where the molecular divergence rate is known, a molecular clock can be applied to estimate the time to the most recent common ancestor (TMRCA) of a sample of genes within a population or between two divergent lineages (Arbogast et al. 2002).

1.2.2 Microsatellites

Microsatellites are tandem repeats of short segments of a DNA sequence, from 1-6 nucleotides long that are mostly non-functional, and tend to occur in non-coding DNA. Microsatellites have been proven to be useful and have been used extensively as genetic markers, as they are widely distributed in eukaryotic genomes (Bachtrog et al. 2000; Harr et al. 2000; Zane et al. 2002). In addition, they are assumed to be selectively neutral, and they have been shown to evolve relatively rapidly for nuclear markers, resulting in a high amount of genetic variability (Kashi et al. 1997).

Microsatellite analysis has been used in evolutionary and population genetic analysis (Goldstein & Schlötterer 1999), and phylogenetic reconstruction (Harr et al. 2000). Microsatellites are in nuclear DNA and reflect migration of both parents (they are usually inherited biparentally). They tend to show high levels of variation in length within a population and can be useful for testing contemporary genetic differentiation on a fine scale and recent population history.

Microsatellites are easily developed into PCR-based molecular markers that are particularly useful for small organisms with limited DNA. The most common way to detect microsatellites is to design two PCR primers (forward and reverse) to flank the microsatellite region. The PCR products are then separated by gel electrophoresis. In microsatellites, the repeated sequence consists of di-, tri-, or tetra nucleotide repeats, and can be repeated six to 100 times. Large repeats are often more polymorphic. The number of repeats at a locus is highly variable between individuals of the same species. For this reason, microsatellite sequences can be used for genetic fingerprinting (Gardener et al. 1996; Brachet et al. 1999; Rozen & Skaletsky 2000). A

7 Chapter 1 ______high level of variation in the number of repeat units reflects a high mutation rate (Rienzo et al. 1998). Microsatellites, due to being mostly non-coding, are less likely to be affected by selection, so the distribution of variation is affected only by mutation, migration (gene flow) and genetic drift (Slatkin 1995; Ellegren 2004).

Microsatellites have been successfully used for population genetic studies in insect vectors such as Anopheles (Lanzaro et al. 1995; Michel et al. 2005) and Aedes aegypti (Raval et al. 2002; Huber et al. 2004). Microsatellite markers will be useful for identifying various populations of Ae. vigilax on a microgeographic scale and to assess genetic variation among mosquito populations.

1.2.3 Phylogeography

The word phylogeography was invented in 1987 (Avise et al. 1987). Phylogeography is the study of spatial genetic patterns that deal with historical components of the distribution of genetic lineages; phylogeography combines population genetics, phylogenetics and biogeography. It is the reflection of DNA lineages in natural populations (gene and species) and shows the distinct geographic orientations that connect with current and historical genetic evolution (Avise 2000; 2008). The associated population dynamics vary with life history and geography, and the present genetic constitution of the populations and species reflect their previous history (Hewitt 2004). Phylogeography has connections to molecular and evolutionary genetics, natural history, population biology, historical geography, and speciation analysis. Phylogeography is the initial and dominant bridge between micro- evolutionary processes operating within species to explain macro-evolutionary processes among species (Avise et al. 1987; Bermingham & Moritz 1998).

The analyses of DNA sequence data from an evolutionary point of view is focused on the reconstruction of evolutionary relationships among both genes and species. The similarity and differences in DNA sequences among different organisms may be used to understand evolutionary relationships. The sample of alleles is examined to determine DNA variation in a population. By examining the genome’s non- recombining part, such as mtDNA in most organisms, Hedrick (2005) has shown that

8 Chapter 1 ______there are three general patterns. Polyphyly, paraphyly and reciprocal monophyly describe different phylogenetic relationships among extant lineages. These three patterns are illustrated in Figure 1.1. A and B are two geographically distinct sites, the block arrow show where the geographical separation occurs (Moritz 1994). The lower letters a, b, c and d show the sequences within the populations and their similarity is shown in a simple tree. Polyphyly occurs when each of the populations may have sequences for which some sequences in the other population are more similar than some of the sequences in its own population (Hedrick 2005). For example, b in population A and c in population B are more similar to each other than other sequence pairs within the same population (Avise 2008). This situation could result soon after two populations are separated; because there has not been enough evolutionary time for the lineages to sort into two distinct lineages. Paraphyly is considered as the intermediary stage between polyphyly and reciprocal monophyly, due to incomplete lineage sorting. Reciprocal monophyly is when the two different populations have lineages that are more closely related to other lineages within the population than to any lineages in the other population, for example, a and b in population A are closer to each other than they are to any sequence in population B. The rate at which populations proceed from polyphyly, through paraphyly to reciprocal monophyly depends on effective population size, usually taking at least 4Ne (effective population size) generations (Moritz 1994). It is also influenced by mutation rate, population demography and the phylogeographic distribution of alleles before the separation of the two populations (Hedrick 2005).

Polyphyly Paraphyly Reciprocal monophyly

a b c d a b c d a b c d a b c d

A B A B A B A B

Figure 1.1: Development of phylogenetic structuring of alleles between two populations (Moritz 1994).

9 Chapter 1 ______

Haplotype networks are used to show relationships between sequences and how many mutations have occurred between sequences as in Figure 1.2. Each circle represents a distinct DNA sequence or haplotypes. The network shows that haplotypes A, C and D each arose from haplotype B by a single mutation. Haplotype E arose from haplotype D. The ancestral haplotypes are B and D and the recent haplotypes A, C and E.

A

B C

D E

Figure 1.2: Haplotype network show relationship among DNA sequences.

Many factors including species interactions, abiotic factors, habitat selection, dispersal, gene drift, mutation and historical processes play an important role in extant populations (Wright 1931; Avise 2000; Krebs 2001). Avise et al.(1987) and Avise (2000) recognized five distinct categories of phylogeographic patterns in nature that illustrate the possible relationships between phylogeny and geography (see also Figure 1.3).

Categories І and ІI described discontinuous patterns of genetic divergence and deep gene trees.

Category I: This category shows prominent genetic gaps with extrinsic barriers to gene flow which distinguish deep allopatric lineages in a gene tree. Haplogroups are separated by large mutational distance. They reflect intraspecific phylogenetic discontinuities within species that have geographic distributions extending across zoogeographic regions.

10 Chapter 1 ______Category II: This category is characterized by phylogenetic gaps between some branches in a gene tree, with principal lineages codistributed over a broad area with lack of geographical separation. This pattern reflects past intrinsic barriers among populations, followed by recent secondary admixture zones. This pattern may be indicative of the presence of sympatric sibling species.

Categories ІІІ, ІV and V describe continuous patterns, and they depend on historical levels of gene flow among conspecific populations that have not been subdivided by long-term biogeographic barriers.

Category III: Most or all haplotypes are related closely and localized geographically. Contemporary gene flow has been low enough in relation to population size to have permitted lineage sorting and random drift to raise genetic divergence among populations that nevertheless were in historical contact recently.

Category ІV: All or most haplotypes are widely distributed throughout the species range. This category is expected for very extensive gene flow in a species of modest or small effective size. The populations of species that show this category of intraspecific phylogeography have had relatively high and recent historical interconnections through gene flow.

Category V: In this category, some mtDNA genotypes are geographically widespread, while allied genotypes are localized, such that the overall pattern is one of a nested series of phylogeographic relationships (Avise et al. 1987). This pattern is intermediate between categories ІІІ & ІV. Common lineages that are widespread and closely related lineages that are more restricted geographically. This phylogeographic results from low contemporary gene flow between populations which have been connected tightly in history (Avise 2000).

11 Chapter 1 ______

Phylogeographic Gene tree Geographic representation Category Pattern region1 region 2 region 3

a – b -c l – m - n x – y - z I large genetic gaps

a- b -c a- b - c a- b - c

II large genetic gaps l -m -n l -m -n l -mn

x- y - z x- y - z x- yz

III No large genetic gaps a b c

a a a

IV No Large genetic gaps b b b

c c c

b - a a a - c V No Large genetic gaps

Figure 1.3: Five categories of phylogeographic structure (Avise et al. 1987, p.137).

1.2.4 Speciation

Speciation is the process that results in reproductive isolation between populations. It usually occurs where two populations diverge from one another while they geographically isolated (Dobzhansky 1935; Avise 2000). Ecological differences between populations may enhance reproductive isolation between them (Coyne & Orr 2004).

Over the past 40 years, it has become clear that many species of mosquitoes described actually consist of a complex of closely related, morphologically similar ‘cryptic species’ (Paterson et al. 1963; Green et al. 1985; Booth & Bryan 1986). The An. maculipennis complex was discovered in the 1930s and the An. gambiae complex in the 1960s. The recognized Anopheles vector genus has been identified and currently contains 66 Anopheline species. Many, but not all, species are important vectors of

12 Chapter 1 ______human malaria; and at least 55% belong to a closely related complex of “Cryptic species”. The rate of discovery of new cryptic species has increased through genome analysis and DNA sequencing (Besansky et al. 1992).

Several DNA methods have been developed to distinguish among the members of a cryptic species complex. Molecular investigations have provided a motivation for vector systematics in the discovery and definition of cryptic species complexes (Munstermann & Conn 1997). In the genus Anopheles, Ayala and Coluzzi (2005) have shown that the geographic distribution of seven species of the An. gambiae complex and An. arabiensis overlap. Seven species are siblings that are morphologically nearly indistinguishable, but they differ in genetic and ecological attributes, including breeding sites. Molecular technologies for identifying 12 cryptic species in the An. punctulatus group were developed by Nigel Beebe. He worked with Bob Cooper from the Australian Army Malaria Institute (AMI). They collected samples from northern Australia, Papua New Guinea, and on Islands in the southwest Pacific to determine the distribution of this species group (Beebe & Cooper 2002).

In addition, Hemmerter et al. (2007) used Likelihood and Bayesian analyses for the mosquito Culex palpalis to reveal that three divergent lineages were geographically restricted to southern Australia, northern Australia and Papua New Guinea; and Culex annulirostris was restricted geographically with five divergent lineages, one in the Solomon Islands and two identified mainly within Australia while two other lineages showed distributions in PNG and the Torres Strait Islands with a southern limit at the north of Australia's Cape York Peninsula. Dev (1987) studied the mechanisms of speciation in the Aedes scutellaris group. Genetic evidence was provided in support of the hybrid origin of species in the south Pacific. It showed that hybridization has been a strong factor in speciation together with geographical isolation and cytoplasmic differentiation. A migration from east to west is suggested.

The existence of cryptic sibling species creates problems in vector control because different members of a complex are likely to differ in their involvement in disease transmission and their response to various control methods. It is therefore important to assess differences between species, as these data are essential to selection and monitoring of vector control strategies (Coluzzi 1984; 1992).

13 Chapter 1 ______

1.3 Climate and Barriers in Australia

Northern Australia has been subject to large-scale repetitive movements of habitats resulting from the development of arid barriers and fluctuating sea levels (Ford & Blair 2005) (Figure 1.4). It is a complicated region to investigate the evolutionary history, using the geographical distribution of genetic lineages, especially within and among closely related species (Brown & Lomolino 1998). There are analytical approaches to aid in distinguishing historical from ecological influences in the distribution of mtDNA haplotypes (Avise 2008). For example, Cracraft (1982) indicated that the avifauna genus Poephila showed patterns of concordance between phenotypic and genotypic characteristics during geographic differentiation; and suggesting that vicariance had resulted in speciation. The supposed Carpentarian arid Barrier also appears to be associated with the divergence between east and west mainland Australian magpie populations (Toon et al. 2007). A review on savannah refuges suggested that the Carpentarian Barrier had been a major barrier (Ford & Blair 2005). Furthermore, Lee and Edwards (2008) showed that the Red-Backed Fairy Wren diverged in northern and eastern Australia across the Carpentarian Barrier approximately 0.27 million years ago.

In addition, the Murchison barrier (which was founded during the Pleistocene) is assumed to be an arid barrier to dispersal for many species (such as Australian birds) between the Pilbara and the south-west (Ford 1987) (Figure 1.4).

14 Chapter 1 ______

Figure 1.4: Map of Australia showing locations of the Carpentarian Barrier, Murchison Barrier and the historical interrelationships of eight areas of endemism in Australia with postulated isolating barriers (A-H) as adapted from Cracraft (1982); Ford (1987).

1.4 The Species

Mosquitoes are common worldwide and widely distributed except in frozen areas. They are well-known insects that belong to the order Diptera, Family Culicidae, and there are about three and a half thousand species currently recognized. Most of them live in the humid tropics and subtropics, where the warm and moist climate is suitable for survival and ensures rapid development (Clements 1992). They are classified into three subfamilies:

1- Toxorhynchitinae 2- Anophelinae 3- Culicinae.

Mosquitoes are a nuisance and also may transmit dangerous diseases. Ae. vigilax is a vector of arboviral disease in Australia (Figure 1.5). Ae. vigilax has been shown to be

15 Chapter 1 ______able to transmit Ross River virus (RRV) (Doherty et al. 1963; Russell et al. 1991; Ritchie et al. 1997; Harley et al. 2000), Barmah Forest Virus (BFV) (Merianos et al. 1992; Doggett et al. 1999; Boyd & Kay 2002), Japanese Encephalitis Virus (JEV) (Van den Hurk et al. 2003), Edge Hill virus (EHV) (Aaskov et al. 1993; Doggett et al. 2008), Stratford virus (STRV) (Nisbet et al. 2005; Doggett et al. 2008), Murray Valley encephalitis Virus (MVEV) and Kunjin virus (KUNV). Although it is unlikely to be of concern for MVE and Kunjin, it is accepted as the major vector of RRV, BFV, JEV, EHV and STRV. All of these viruses have been responsible for human infections and each has specific ecological and epidemiological characteristics. An understanding of mosquito classification, distinguishing features, life cycle, ecology and gene flow in vector species will lead to a greater understanding of disease epidemiology. This is essential for disease surveillance, risk evaluation and the development of strategies to improve success in control programs to prevent the spread of arboviruses carried by this species. To know how long distances the disease can transmit and that is important for disease control. By prevent the spread of arboviruses carried by this species, not only for disease transmission, but also might inform the detection and control that vector-borne disease outbreak.

Figure 1.5: Ae. Vigilax

1.5 Classification

Marks (1982) and Russell (1993) have described Ae. vigilax. The adult female is a medium sized mosquito of dark appearance with banded legs. The mouthparts are characterised by pale scaling on the basal two-third underside of the proboscis (Figure

16 Chapter 1 ______1.6.a). The thorax has dark bronzy scales and some golden scales admixed on the scutum (Figure 1.6.b); wings are dark scaled but with sparse mottling of narrow white scales mainly along front veins (Figure 1.6.c); hind legs have mottled femur and tibia, tarsi all with basal bands (Figure 1.6.d). The abdomen is distinguished by dark abdominal tergites with pale basal bands (Figure 1.6.e); sternites are pale scaled with dark lateral apical or sub-apical patches (occasionally bands) (Figure 1.6.f).

Adult females are mostly likely to be confused with other species with mottled proboscis and banded legs, such as Aedes theobaldi (pale wing scales are broader than dark scales), Aedes flavifrons (dark blotch on wing membrane), and Aedes alboannulatus, Aedes camptorhynchus, Aedes procax (all with dark scaled wings) (Russell 1993).

Figure1.6: Taxonomic characteristic features of Ae. vigilax (Russell 1993, p. 10.98)

1.6 Life cycle Ae. vigilax undergoes complete metamorphosis (eggs, larvae, pupae and adults) (Figure 1.7). The larval habitat is selected by the female when she deposits her eggs. The eggs are laid as single units on a moist substrate (Clements 1992), for example in small cracks in soil on vegetation (Turner & Streever 1999) or on sloping irregularly

17 Chapter 1 ______flooded banks (Dale et al. 1986) or higher marsh areas (Dale et al. 2008). Some eggs are also laid on stems of saltmarsh plants and on mangroves (Gislason & Russell 1997). The time required to complete the development of eggs depends on temperature, but the minimum is 2 days. The other factor that is thought to be important from analysis of eggshell distribution is sea level, which when increased may lead to changes to land cover and relative location on the marsh (Dale et al. 2008).

Eggs hatch into aquatic larvae which then swim actively. They come to the surface to breathe through siphon tubes at the end of the body, which are also used to suspend the larvae from the water surface. They grow through four instars in 4-5 days during summer depending on water temperature and tides. Larvae feed on algae and microscopic organisms which live in the water (Clement 1992; Russell 1993).

The pupae are also aquatic. They breathe through a trumpet tube at the head end of the comma shaped body. The duration of the pupal stage (2-3 days) is also dependent on temperature (Russell 1993).

Finally, transformed into winged aerial adults, individuals rest on the surface of the water for a short time to allow the wings to dry before flying off to start the next stage of the life cycle.

Figure 1.7: The typical lifecycle of a mosquito (Webb & Russell 2007, p.6).

18 Chapter 1 ______

The time taken for development from egg to adult varies; it depends on environmental factors, but under optimum conditions takes 1-2 weeks. Their resting habit is to stand with the body parallel to the resting surface (Russell 1993). Females attack during daylight hours (diurnal) but also at night (nocturnal) and prefer to bite humans, although the viruses have been found in Kangaroos, Wallabies, birds, bats, rabbits and horses (Ryan et al. 1997; Russell & Dwyer 2000). The blood seeking behaviour starts from 20 to 24 hours after emergence of adult. The females are driven to their host by a combination of different stimuli, including carbon dioxide, body odours, and body heat/humidity. Adults are most abundant in summer months and, in New South Wales for example, are active from mid-spring through to autumn. They are strong fliers and have been shown to disperse long distances from their breeding site at least 50 km (Russell 1993) and more than 10 km from larval habitats (Webb & Russell 2007).

1.7 Ecology and Distribution

In Australia, Ae. vigilax is the most common saltmarsh mosquito in coastal regions of New South Wales, and in the more northern areas of Queensland and the Northern Territory. It also occurs in parts of South Australia and Western Australia (Belkin 1962; Dobrotworsky 1965; Marks 1973; Russell 1990), and has been reported in Victorian coastal areas (Lee et al. 1984; Dale 1993; Russell 1993) (Figure 1.8).

19 Chapter 1 ______

Figure 1.8: The geographic distribution of saltmarsh mosquito in Australia (http://www.152.98.200.7/ento/Mosquito/html/Oc_vigilax.htm).

Ae. vigilax is associated with estuarine environments, mangroves, and coastal saltmarshes (Russell 1993). It does not usually breed in freely flushed areas but prefers to breed in mangrove and salt marsh areas where water remains after flooding (Dale et al. 1998) (Figure 1.9).

Figure 1.9: Mangrove and salt marsh area (photo by Pat Dale, Griffith University).

20 Chapter 1 ______The virus (RRV or BFV or JEV) is spread when it is transmitted to people by the bite of Ae. vigilax individual that is infected with a virus. The mosquito becomes infected with virus when it bites an intermediate host which has the virus (Russell 1993). For RRV and BFV disease to be transmitted to humans, there must be interactions between an infected vector and human, so human risk for disease depends on both the abundance and distribution of infected mosquitoes and the degree of human-mosquito contact (Dale et al. 1998). Many studies (McMichael et al. 1996; Dale et al. 1998; Maelzer et al., 1999; Hales et al. 2002; Tong et al. 2004b; Gatton et al. 2005; Tong et al. 2005; Doggett et al. 2008) have examined the relationship between climate variation and vector-borne diseases. They showed a strong relationship between disease and some environmental factors including temperature, humidity, rainfall, and tide level.

Tong et al. (2004) and Gatton et al. (2005) through their studies in Townsville and Queensland respectively across 10 years, showed positive correlations between RRV disease outbreaks and increased rainfall, temperature, humidity and sea tide. Gatton et al. (2005) showed that rainfall and temperature variables were good predictors of RRV disease outbreaks for certain areas within Queensland.

Dale et al. (1998) identified the factors which specifically influence the risk of disease using Remote Sensing (RS) and Geographic Information System (GIS) as powerful tools for mosquito vector and risk management. Remote Sensing is a tool that can be used to identify larval habitats. It is used in mosquito control programs as discussed by Wagner et al. (1979). GIS has been used recently to manage disease and particularly vector-borne disease (Lourenco-de-Oliveira et al. 1989; Kitron et al. 1997), and aerial photographs have also been used to map vector habitats (Kline 1991; Dale et al. 1998). Color infrared (CIR) aerial photography was used to identify specific parts of oviposition habitats (where larvae and eggs were found) of the saltmarsh mosquito (Ae. vigilax). Such tools have been used to identify an expanded geographic distribution of the notified Barmah Forest Virus infections in Queensland over the last decade (Tong et al. 2005).

Recently, studies using data to identify vector habitats, such as Thomson et al. (1996) reviewed the use of satellite data in malaria ecology to show spatial and temporal

21 Chapter 1 ______variation. Also Szalanski et al. (2006) used mtDNA haplotypes from Aedes species as evidence for geographical boundaries and habitat differences acting as barriers to dispersal and gene flow. However, only one genetic study performed on Ae. vigilax has been undertaken so far. This study used allozymes to test for genetic differentiation between samples from nearby localities in south-east Queensland. They found significant larval variation between some breeding sites, and non- significant genetic differentiation was found among collections of adult mosquitoes; indicating widespread dispersal (Chapman et al. 1999).

Additionally, a large level of gene flow between populations may result from human assisted dispersal. Introduction of Ae. vigilax was thought to be caused by human transportation because their eggs are desiccation resistant and could travel. Ae. vigilax have been caught in aircraft arriving in Christchurch from Australia on two occasions ( in 2001 and 2003) (Lourenco-de-Oliveira et al. 1989).

1.8 Dispersal

Dispersal and gene flow refer to the movement of individuals and gametes among populations, which is closely associated with the structure of populations.

Service (1997) provided a comprehensive review of the different types of dispersal seen in mosquitoes, including accidental dispersal on human transportation, wind assisted long distance dispersal and shorter, daily flights in search of hosts, mates, nectar, oviposition, and resting sites. Southwood and Henderson (2000) refer to two types of dispersal: neighbourhood dispersal, which describes the process of movements of individuals into adjacent areas; and jump dispersal, which describes longer-distance movements (Silver 2008). For example, Hamlyn-Harris (1933) found migration takes place, between one and sixty miles, during the high temperature, barometric pressure and humidity being important factors.

22 Chapter 1 ______1.9 Vector-borne disease

A current worldwide emphasis on vector-borne disease research relates to a requirement for more intensive studies of the vector species, due to many aspects of future climate changes are still unpredictable. As the spread of exotic species and altered climate have the potential to change the dynamics and geographical distribution of species (Sutherst et al. 1998).

Australia has more than 70 arboviruses, but relatively few cause human disease. The most common arboviruses which cause significant human disease in Australia are the flaviviruses Murray Valley Encephalitis, Kunjin, Japanese Encephalitis, and Dengue Virus; and the alphaviruses Ross River and Barmah Forest Virus (Flexman et al. 1998).

Ross River Virus (RRV) became widely recognized as a major nationwide problem in the 1980s (Russell 1994). During the 1990s Barmah Forest Virus (BFV) emerged with some relatively major outbreaks (Merianos et al. 1992; Lindsay et al. 1995; Doggett et al. 1999). Japanese Encephalitis Virus (JEV) also emerged annually in the Torres Strait Islands following its introduction from Papua New Guinea (PNG); (Hanna et al. 1996; Ritchie et al. 1997; Hanna et al. 1999). Recently, other two viruses Edge Hill virus (EHV) and Stratford virus (STRV) have been isolated from Ae. vigilax from few cases (Doggett et al. 2008).

The number of vector-borne diseases notified from state and Territory health authorities between 1 July 2006 and 30 June 2007 decreased by (55%) RRV, (29%) BFV and no human cases of JEV during 2006-2007 (Liu et al. 2007). The last reported was only one JEV infection in February 2008. Between 1 July and 30 September 2008 were reported 768 notifications of RRV infections, representing rate of 14.6 per 100,000 populations (Australian Government Department of Health and Ageing 2008). Doggett et al. (2008) reported that the number of laboratory notifications of human RRV and BFV disease from NSW, for the period July 2007 to June 2008 was 537 BFV and 1,196 RRV. However, overall there were 54 mosquito species with 104,797 trapped in Sydney in that season, which was lower than the

23 Chapter 1 ______pervious season due to lower Ae. vigilax numbers; even though Ae. vigilax was the most common species collected from the coastal region in NSW during that period. The reports from Western Australia in 2006 provided information suggesting that Ross River and Barmah Forest virus diseases were endemic in the Pilbara and the Shire of Roebourne had approximately 5-40 cases reported every year (Mosquitoes Environmental Service Information Sheet 2006).

In summary, Table 1.1 shows the number of notifications from vector-borne disease from states and Territories in Australia from 1991 to up to date (September) 2009 (Australian Government Department of Health and Ageing 2009).

24 Chapter 1 ______

Table 1.1: Numbers of vector-borne disease infections reported in Australia from 1991 to 2009 (Australian Government Department of Health and Ageing).

Diseases 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Years to Sep. Ross River NN NN 5252 3848 2633 7776 6600 3157 4376 4225 3226 1455 3844 4209 2540 5545 4207 5651 3836 Virus infection Barmah Forest NN NN NN NN 761 876 690 526 639 646 1143 910 1367 1103 1323 2140 1716 2101 1138 Virus infection Japanese NN NN NN NN NN NN NN NN NN NN 0 0 1 1 0 0 0 1 0 Encephalitis Virus infection

NN: Not Notifiable

25 Chapter 1 ______1.9.1 Ross River Virus (RRV)

Ross River Virus is an important arbovirus disease that is endemic and enzootic in Australia. It is known as an epidemic polyarthritis (Selden & Cameron 1996). An average of 5000 cases of RRV disease is notified yearly in Australia (Harley et al. 2001; 2005), the last report in 2009 there were 3836 infections (Table 1.1) (Australian Government Department of Health and Ageing 2009); and around 700 cases per season in NSW (Doggett et al. 2008). A positive correlation has been found between RRV disease outbreaks and increases in some environmental factors (Tong et al. 2004; Gatton et al. 2005) reflecting suitable conditions for the mosquito vector to flourish.

Ross River Virus was isolated in 1963 from Ae. vigilax mosquitoes trapped near the Ross River Townsville, North Queensland and then from an epidemic polyarthritis patient in Queensland in 1964 (Doherty et al. 1963). The single largest reported outbreak of RRV occurred in the south Pacific islands in 1979-1980, when more than 50000 people were infected (Aaskov et al. 1993). Russell (2002) described in detail the ecology and epidemiology of RRV isolated from over 40 species of mosquitoes belonging to seven genera, 10 of which have been able to transmit the disease in the laboratory.

The infection is not fatal, but there is substantial morbidity associated with acute polyarthritis, which is the main symptom of the disease (Russell 2002), accompanied by fever, skin rash, headache and depression which may be followed by acute aching in the muscles and joints, and then extensive polyarthritis with all joints affected. Some people recover quickly, while others may have symptoms for months or years (Selden & Cameron 1996; Flexman et al. 1998; Harley et al. 2001; 2002).

Ae. vigilax in the laboratory has presented evidence of transovarial transmission (TOT) of Ross River Virus (Kay 1982; Russell 1993), that is the passage of the virus from an infected female into her eggs and through the hatching larvae to the next generation of emerging adults. This could be a key mechanism for RRV survival in tropical and temperate regions if TOT occurs under natural conditions (Russell 1993).

26 Chapter 1 ______

The disease has become more common in recent years, as an increasing level of infection has been reported in all states throughout Australia (Russell & Kay 2004). For example, from 1991 to 2009, there were 72380 laboratory diagnosed cases of RRV infection reported from all Australian states and territories (Australian Government Department of Health and Ageing 2009). In coastal areas, the saltmarsh mosquitoes Ae. vigilax in the northern region and Ae. camptorhynchus (Thomson) in the southern region are the most important vectors. Muhar et al. (2000) indicated that RRV infection risk is significantly high relatively close to the major vector species Ae. vigilax habitats.

To design preventive programs, better understanding is thus needed of transmission and behavioural and environmental risks. Much more research is needed to define the vectors of RRV, in explaining the complex ecologies of the virus and developing plans for management, real time observation, and prioritised mosquito control (Russell & Kay 2004).

1.9.2 Barmah Forest Virus (BFV)

Barmah Forest virus (BFV) infections have increased in recent years, although they are generally less widespread than RRV (Table 1.1). Their average is approximately 1000 human cases of BFV per year across Australia; this virus has been recorded from all states and regional outbreaks have increased in most parts of Australia (Merianos et al. 1992; Lindsay et al. 1995; Doggett et al. 1999; Webb & Russell 2007; Australian Government Department of Health and Ageing 2009).

Barmah Forest virus was first isolated from mosquitoes in Victoria in 1974, but was not associated with human infection until 1986 or with human disease until 1988 (Marshall et al. 1982; Flexman et al. 1998; Daley & Dwyer 2002). Daley & Dwyer (2002) emphasised that BFV was first associated with human infection in 1988 and distributed in the east coast of Australia; then it extended to Western Australia and the Northern Territory. The first outbreak of 47 cases of BFV disease in Victoria occurred between January and May 2002 (Passmore et al. 2002). In Western Australia, BFV

27 Chapter 1 ______was first isolated from mosquitoes in 1989, and the first outbreak of human disease occurred in the south-western region of the state during the spring and summer 1993- 94 (Lindsay et al. 1995).

BFV is believed to be of shorter duration than RRV. Symptoms may include fever, tiredness, painful joints, joint swelling, muscle tenderness, skin rash and headache. Some people, especially children, may become infected without showing any symptoms (Beard et al. 1997; Flexman et al. 1998; Harley et al. 2001). Although the symptoms of Barmah Forest virus are similar to Ross River virus, the disease has not yet been as intensively studied.

BFV appears associated more with coastal than inland regions even though it was first isolated from the inland, and the largest outbreaks have been in NSW in 1994/95 even though most cases are recorded in Queensland (Doggett et al. 1995; Russell & Kay 2004). Ae. vigilax was considered to be one of the major vectors of BFV in the largest recorded outbreak of that disease in 1995 that occurred in NSW (Doggett et al. 1999). Almost 7100 cases of BFV were reported nationally for the 4 years 2006-09, nearly twice the number for the previous four years (Table 1.1). Barmah Forest Virus is not fatal, but it merits serious consideration as a cause of mosquito-borne morbidity (Russell & Kay 2004).

1.9.3 Japanese Encephalitis Virus (JEV)

Japanese Encephalitis Virus was first isolated in Japan in 1935, and was then found in Russia and most of East and south East Asia. Recently, it has spread to the Indian subcontinent and Nepal. It was clinically diagnosed for the first time in India in 1955. Fifty thousand cases with 15000 deaths were notified annually from a wide geographic range (Solomon et al. 2000) with major outbreaks occurring with heavy rainfall and/or floods.

Ae. vigilax also has the potential to transmit Japanese encephalitis virus. JEV was first recorded in 1995 on an island in the Torres Strait, between mainland Queensland and PNG, with three human cases and two deaths (Russell & Kay 2004). JEV in Australia

28 Chapter 1 ______is not transmitted in the mainland. The emergence of JEV in the Torres Strait was unexpected, occurring over 3,000 kilometres from the nearest human infections in Bali and Lombok (Flexman et al. 1998). Antibodies were found in local pigs and the virus was isolated from mosquitoes (Culex annulirostris) (Skuse) (Hanna et al. 1996; Ritchie et al. 1997). The weather and wind prior to the outbreak indicated that the virus was probably introduced with wind-blown mosquitoes and/or by migrating birds from PNG (Hanna et al. 1999).

Apart from 1999, JEV activity has been monitored annually through sentinel pigs. There were two further human cases in 1998, but there have been none since (Russell & Kay 2004). Then in 2003, 2004 and 2008, there was just one case each (Australian Government Department of Health and Ageing 2009).

Feeding of JEV to colonised and wild-caught mosquitoes demonstrated that Cx. annulirostris was a highly efficient vector, and that Culex gelidus (Theobald), Culex sitiens (Wiedemann), Culex quinquefasciatus (Say), Aedes notoscriptus (Skuse) and Ae. vigilax were competent, but that Aedes aegypti (Linnaeus), Aedes kochi (Donitz) and Verrallina funerea (Theobald) were not (Van den Hurk et al. 2003).

Encephalitis typically refers to an inflammation of the brain which may be fatal, or severe leading to recovery with or without lasting consequences. Symptoms always include high fever and headache, vomiting, nausea, diarrhea and dizziness. The brain may become dysfunctional after a few days with lethargy, irritability, convulsions and neck stiffness; and finally, coma and death may result (Whitley & Gnann 2002; Chanama 2005). The disease is fatal in 30-50% of cases; more than half of the patients that develop symptoms either do not survive or suffer severe brain damage and /or paralysis.

1.9.4 Edge Hill virus (EHV)

Only a few cases of assumptive infection with EHV have been described, with symptoms including myalgia, arthralgia and muscle fatigue (Webb & Russell 2007). Ae. vigilax has yielded most of the EHV isolates in south east Australia, although it

29 Chapter 1 ______has been isolated from many other mosquito species. The vertebrate hosts may be wallabies and bandicoots, however studies are limited (Aaskov et al. 1993; Doggett et al. 2008).

1.9.5 Stratford virus (STRV)

Stratford virus is closely related to Kokobera virus (KOKV). Both were originally classified as separate species in the Japanese encephalitis virus (JEV) complex (Nisbet et al. 2005; Webb & Russell 2007). There have been very few documented symptomatic patients infected with (STRV), only three described to date and symptoms included fever, arthritis and lethargy. The virus has mostly been isolated from coastal NSW, particularly from the saltmarsh mosquito, Ae. vigilax, although recent isolates from the Sydney metropolitan area include Aedes notoscriptus and Aedes procax (Doggett et al. 2008).

To understand more why, where and when there are outbreaks of mosquito-borne diseases virus, it is critical to know the dispersal abilities of the mosquito vector species in question. One way to do this is to use genetic markers, which can be used to assess levels and patterns of dispersal at a range of spatial and temporal scales.

1.10 Study aims and hypotheses

Given the scarcity of data, and the importance of understanding the dispersal of Ae. vigilax with regard to Ross River virus, the goal of this project was to study the genetic diversity of Ae. vigilax populations and to estimate the level of genetic variability and levels of genetic exchange among populations using mitochondrial sequence data and microsatellite loci. In this study I analyse for the first time the genetic variability of Ae. vigilax in populations from different locations across the Australian distribution using mtDNA and microsatellites markers.

The purpose of this study was to apply phylogeographic and population genetic techniques to populations throughout the range of Ae. vigilax to investigate the relationship between historical environmental changes and contemporary processes of

30 Chapter 1 ______genetic differentiation in the populations of this species. This thesis includes six chapters. Chapter 2 details the general methods of sampling, laboratory techniques and data analysis used in this research. In chapter 3, I describe the phylogeographic patterns of the species across the Australian distribution, using sequence data from the mitochondrial cytochrome oxidase gene I. Nested clade analysis was used to illustrate the patterns of variation, and different statistical methods were used also to test recent demographic changes and to estimate the divergence time among populations. The hypothesis is that patterns of genetic structure will show high levels of gene flow among populations of Ae. vigilax.

Based on microsatellite loci, in chapter 4, I assess the level of allelic variation of Ae. vigilax populations to emphasize the mtDNA results that the extent of divergence between the populations of Ross River Virus vector. I used three microsatellite loci to test the hypothesis that two lineages identified in chapter 3 are reproductively isolated, i.e. represent distinct biological species.

In chapter 5, I also used the three microsatellite loci as independent assessment for gene flow between eastern and western regions of the Australian continent. The geographic distribution of variation at these three loci were estimated and compared with that variation of mitochondrial haplotypes in chapter 3. The aim was to test further hypothesis of dispersal and gene flow of Australian Ae. vigilax.

Chapter 6 presents a general discussion of the phylogeographic patterns of Ae. vigilax and presents an overview of the hypothesis of recent evolutionary history of this species, and summarizes the findings in the previous three chapters comparing with the other studies. Finally, it concludes with further research and the implications of my findings for vector control.

31 Chapter 2 ______

Chapter 2 Material and methods

In this chapter, the general methods of sampling, molecular analysis and data analysis carried out in this research are described with some reference to the literature. Methods that are unique to a specific part of the study are described in the relevant chapter.

2.1 Mosquito Sampling

Adult females of Ae. vigilax were collected by local collectors using light traps during the summer of 2005 and 2006 from nine regions distributed throughout Australia; Sydney from New South Wales, Gold Coast, Brisbane and Cairns from Queensland, Darwin from Northern Territory, Wyndham, Willie Creek, Onslow and Perth from Western Australia (Figure 2.1). In each location, I obtained between one to three sites (a total of 13 sites) and from each site between 16 and 31 individuals were collected (see Table 2.1). A total of 379 adult female mosquitoes morphologically identified as Ae. vigilax were preserved in 70-100% ethanol for shipping and kept until genetic analysis. All individuals were used for microsatellite analysis. For mtDNA analysis only 318 adults were used.

32 Chapter 2 ______

Figure 2.1: Map showing sampling locations for Ae. vigilax in Australia.

Table 2.1: Collection localities and sample sizes for all Ae. vigilax examined.

States & Sample Sample Sample Territories Locations numbers Sample sites size Eastern Australia NSW Sydney 1 Newington (NEW) 30 2 Salt Ash "Port Stephens Shire" (STA) 30 QLD Gold Coast 3 Rusty Cars (RUC) 30 4 Helensvale Rd. (HEL) 28 Brisbane 5 Hemmant (HEM) 30 6 Banyo (BAN) 30 7 Brighton (BRI) 30 Cairns 8 Smith Field (SMA) 30 NT Darwin 9 Palm Creek (PLM) 30 Western Australia Wyndham 10 Wyndham (WYN) 29 Willie Creek 11 Willie Creek (WIL) 22 Onslow 12 Onslow (ONS) 31 Perth 13 Leschenault Peninsula "Belvidere" (LSP) 29 QLD: Queensland; NSW: New South Wales; NT: Northern Territory.

33 Chapter 2 ______

2.2 Study Area

Australia has a wide range of environments including tropical, temperate, rainforests, plains, desert and mountains. In this study a number of these have been chosen as the study areas. This area covers 9 major regions. The sampling was from multiple sites within broad regions that are at least 50 km apart, two sites from the Sydney region (Newington & Port Stephens Shire), two sites from the Gold Coast region (Rusty Cars & Helensvale Rd), three sites from the Brisbane region (Hemmant, Banyo & Brighton), and one site at Cairns (Smith Field), Darwin (Palm Creek), Wyndham, Willie Creek, Onslow and Perth (Leschenault Peninsula) respectively (Figures 2.2 & 2.3). Samples were provided or collected from Sydney by R. Russell, J. Clancy & J. Haniotis; from Gold Coast by R. Durre & D. Allaway; from Brisbane by M. Muller, I. Myles & N. Shibani; from Cairns by S. Ritchie; from Darwin by P. Whelan & H. Nguyen and from Western Australia by M. Lindsay & C. Johansen.

34 Chapter 2 ______

Figure 2.2: Map showing sampling sites for Ae. vigilax in eastern Australia (QLD &NSW)

35 Chapter 2 ______

Figure 2.3: Map showing sampling sites for Ae. vigilax in Northern Territory and Western Australia

2.3 Sampling & Methodology

2.3.1 Field Work (sampling)

Adult female Ae. vigilax were collected during summer using light traps and samples were obtained twice during this study. Carbon dioxide-baited light traps were used to collect adult mosquitoes from different locations at each site (one light trap at each).

The attraction of a light trap is increased by placing a source of carbon dioxide (such as dry-ice) above the trap (Figure 2.4). Carbon dioxide attracts mosquitoes because it represents the respiratory product of humans. Body odour and carbon dioxide, carried on the wind stimulate sense receptors on the antennae and palps of female mosquitoes, alerting them to the presence of a host (Clements 1992). The traps were

36 Chapter 2 ______located in open areas, away from buildings and other lights, at a height of about 1.5 meters above ground level. Dry-ice baited traps were placed out for collections at least 1-2 hours before sunset. Mosquitoes were collected between dusk and dawn, and retrieved approximately 1-2 hours after sunrise or as soon as practicable after sunrise (Reinert 1989; Butts 2002; Butts 2002). The collections were transported to the laboratory for sorting, identification and molecular analyses.

Figure 2.4: Mosquito sampling trap (Connecticut Mosquito Management Program 2009).

Adult mosquitoes were stored in 70% ethanol for long term storage or shipping to maintain the quality of the DNA. Exposure to conditions of high humidity and high temperature for more than 2h can seriously reduce the quality of the DNA (Nei & Li 1973; Owens & Szalanski 2005).

2.3.2 Lab Work (Methodology)

2.3.2.1 Identification

As far as pest problems and the transmission of disease causing pathogens are concerned, it is only the adult female mosquito that is important because the male does not suck blood nor bite in any way. The female Ae. vigilax were initially identified by the collectors. They were then all checked in the laboratory using

37 Chapter 2 ______taxonomic keys. They were stored in 70-100% ethanol before being used for molecular analysis.

2.3.2.2 DNA extraction and amplification

DNA was extracted from 379 individual mosquitoes (from the whole mosquito body or just the legs) using the CTAB/phenol–chloroform DNA extraction protocol (Doyle & Doyle 1987). The digestion of mosquito muscle was ground in 700μl of CTAB solution and 5μl of 20 mg/ml Proteinase K overnightºC. at 350 65μl each of chloroform-isoamyl and phenol were added to tubes, then they were gently inverted for 30 minutes and centrifuged at 13 500 rpm for 5 minutes. Supernatant was removed to clean 1.5 ml eppendorf tubes; the same step was repeated by adding 600 ml of chloroform-isoamyl. The DNA was precipitated with addition of 600 ml of cold isopropanol, mixed gently and left for at least 1 hour at -80ºC. Pellets of DNA were washed with 70% ethanol and dried in a vacuum. Then, the extracted DNA was

resuspended in 50μl of ddH2O and stored at -20ºC.

A polymerase Chain Reaction (PCR) was conducted using the primers LCO1490 and HCO2198 (Folmer et al. 1994) to amplify a fragment of the mitochondrial Cytochrome Oxidase I (COI) gene. The markers AP5 (Behbahani et al. 2004), JO338 (Widdel et al. 2005) and GT48 (Keyghobadi et al. 2004) were used to amplify three microsatellite loci. Polymerase Chain Reaction was performed in 12.5μl final reactions. The cycling program was at 94ºC for an initial denaturation step for 5min, followed by an annealing steps at different time and cycles, then extension at 72ºC for the final elongation step. PCR products were run on a 1.6% agarose gel to estimate the concentration of purified DNA or 5% polyacrylamide gel for short fragments.

2.3.2.3 Purification, sequencing and alignment

Sequencing reactions were cleaned and prepared using the EXO SAP method (Nordström et al. 2000), by placing 5μl of PCR product into a PCR tube with 0.5μl of Exonuclease I (EXO) and 2μl of Shrimp Alkaline Phosphatase (SAP). They were then incubated in the following PCR thermo cycling conditions: 37ºC for 35 min, 80ºC for 20 min and held at 4ºC. A sequencing reaction was conducted with 0.5μl of EXO SAP

38 Chapter 2 ______DNA containing 2μl Big Dye Terminator reaction mixture, 2μl of Terminator Buffer,

0.32μl of primer (forward or reverse), and ddH2O to take the volume to 10μl which was subjected to 30 cycles of the following PCR thermocycling conditions: 96ºC for 1 min, 50ºC for 5 seconds and 60ºC for 4 min. Finally, samples were cleaned withμl 5

of 125mM EDTA, 15μl of ddH2O and 60μl of 100% ethanol, mixed gently, and incubated at room temperature for 15 minutes. They were centrifuged at 13 500 rpm for 40 min at 4ºC, and dried in a vacuum. Sequencing was performed on an ABI 3130 XL automated sequencer at the Griffith University (Nathan) sequencing facility. The sequence signals of A, T, C and G nucleotide positions were recorded by ABI software and aligned using the SequencherTM 4.1 (Gene Code Corporation). The peaks were translated into chromatograms of the nucleotide base code for editing the disturbances in the chromatogram by correct the initial and terminal regions of overlapping sequences.

2.4 Microsatellite loci

In this study, I screened 47 primer pairs to identify loci that could be used for Ae. vigilax (Table 2.2), developed for other species of mosquito. Useful microsatellite loci have been difficult to identify for mosquitoes in the genus Aedes. Only three loci were found that were variable and gave clearly interpretable banding patterns. These three microsatellite markers are AP5, CxpGT46 and OJ338B (Behbahani et al. 2004; Keyghobadi et al. 2004; Widdel et al. 2005) as shown in chapter 4 (Table 4.1), which amplified, screened and analysed for Ae vigilax individuals.

39 Chapter 2 ______Table 2.2: Primers for 47 microsatellite loci screened for Ae. vigilax that had been developed for other mosquito species. *Indicate the microsatellite loci which used in the study. References Species Locus Repeat motif name

Huber et al. Aedes aegypti 23/15 (GTT)3GTC(GTT)18GT (2001)

C2A8 (GCC)3 And CCAAACTACCCACCCCCACCACCGA

AGCCATACCACGCTCCACCCCCA(CCA)2CACCAC

GCTCAC(CCA)8GC CCACTACGGACACCA

T3A7 (TTTA)7(T)14

AED19 GGAC(GGA)5

9A89 (TA)2(GTT)4

Behbahani et al. Aedes AP1 (AC)14N3(CG)5 (2004) polynesiensis

AP2 (TGC)12N6(TGA)4

AP3 (TGC)5

AP4 (TCA)6

*AP5 (TGG)4N30(TGC)4(TGT)3N12(TGA)3

AP6 (CT)3N26(CAC)3N36(CAC)3(CAG)4(CA)3

Keyghobadi et al. Culex pipiens CxpGT4 (GT)5(GTTT)2GC(GT)2CT(GT)5 (2004)

CxpGT9 (GT)13

CxpGT12 (TG)14

CxpGT20 (TG)15

CxpGT40 (GT)15

*CxpGT46 (TG)15

CxpGT51 (TG)4CG(TG)15

CxpGT53 (TG)22 A Widdel et al. Aedes japonicus OJ5 (GTT)6(GCT)3(GTT) (2005) A OJ10 (GTT)(GTG)(GTT)8 A OJ85 (CAG)6 B OJ100 (GT)5 A OJ187 (CGA)11 B *OJ338 (CAA)10

Chambers et al. Aedes aegypti B07 GA15 (2007)

B19 CAT7

H08 TCG7

G11 TTA16

F06 TAGA8

M201 ATA36

40 Chapter 2 ______

M106 ATG5 Slotman et al. Aedes aegypti AT1 - (2007) AG7 - AG2 - AC7 - AG1 -

Venkatesan et al. Culex tarsalis CUTC102 (ATG) n CTG(ATG) n (2007)

CUTC201 (ATG) n

CUTC203 (ATG) n

CUTD5#7 (CAG) n

CUTD10 (CAG) n

CUTD102 (CAG) n CAACAGCAA(CAG) n

CUTD203 (CAG) n CAC(CAG) n CAA(CAG) n

CUTD206 (CAG) n

CUTD211 (CAG) n

CUTD213 (CAG) n CAA(CAG) n

2.5 Data analysis

2.5.1 Phylogenetic trees

Phylogeographic analysis attempts to reconstruct the evolutionary relationships (historical and contemporary) among DNA sequences of different individuals. Bayesian analysis was performed using MrBayes version 3.1.2 (Huelsenbeck & Ronquist 2001). NEXUS format was used for a Bayesian inference of phylogeny to define phylogenetic relationships within the species. I used MrBayes with parameters, 2 million generations, trees sampled every 100 cycles, 25% burn in, as suggested in the Method Manual. If the standard deviation of split frequencies is below 0.01 after 100,000 generations, the run was stopped. Otherwise, generations were added until the value fell below 0.01 (Ronquist et al. 2005).

41 Chapter 2 ______

2.5.2 Nested clade analysis

Relationship among COI haplotypes were visualized in a network produced using TCS (Clement et al. 2000). All ambiguous connections in the network were resolved using the criteria described by Crandall & Templeton (1993), and by Posada & Crandall (2001a). Under coalescent theory, rare haplotypes with low frequency should occur at tips of the cladograms, whereas the high frequency haplotypes occur in the interior, because they have been present in population for a long time. Therefore, the older haplotypes with high frequency in the population are likely to have a greater number of mutational connections than younger haplotypes (Excoffier & Langaney 1989). The older haplotypes will be more broadly distributed geographically, and singletons are more likely to be connected to haplotypes from the same population than to haplotypes from different populations. Then the network is illustrated by nesting arrangement of the haplotypes in a group of clades in a hierarchy related to their age from youngest to oldest.

Nested clade analysis (NCA) was used to infer population history from the geographic distribution of haplotypes and to distinguish it from contemporary population structure. I used NCA to test the evolutionary relationships of haplotypes and clades and their association with geographical location. This allows the inference of historical processes that may have affected the populations, and allows inferences of restricted gene flow, past fragmentation and range expansion (Templeton et al. 1995). The haplotypes were grouped from the tips to the interior into a series of one step clades. This procedure was repeated for 2-step clades, 3-step clades etc. until the whole network was combined into a single clade. The two main statistics calculated

were clade distance (Dc) which measures the geographical extent of clades and

haplotypes; and nested clade distance (Dn) which measures the distance of each haplotype or clade from the geographical centre of the nesting clade, then the average

distance between tip and interior clades within each nested group (I-TDc) and the tip to interior distance for the nesting clade (I-TDn) was estimated (Posada et al. 2006). The statistical significance of variables was calculated against a null hypothesis of no geographic association of the haplotypes and clades. GeoDis version 2.5 was used for

42 Chapter 2 ______the calculation of statistics and the associated p-value for the NCA (Posada et al. 2000). Following this, the Posada and Templeton November 2005 inference key was used (available at: Darwin.uvigo.ed/download/geodisKey_11Nov05.pdf). NCA has recently been criticised because it has been shown to give some false indications from simulated data (Panchal & Beaumont 2007; Petit 2008). However, in this study it was used in conjunction with other methods to examine the history of the species across the continent.

2.5.3 Molecular clock and divergence

A molecular clock has been used to explore phylogenetic and biogeographic relationships (Gaunt & Miles 2002; Galtier et al. 2009). It assumes that a measure of genetic distance between populations can be converted to a time estimate for their separation from a common ancestor by using a calibration factor applied to the number of genetic changes expected in a given time (Arbogast et al. 2002). Molecular clock calibration was used to estimate the divergence time between the clades (chapter 3). If the number of nucleotide differences between two populations were plotted against the time since their divergence from a common ancestor, the result should be a straight line equal to the mutation rate. That is, evolution should proceed according to the molecular clock (Griffiths et al. 2008).

2.5.4 Neutrality tests

Neutrality tests can be useful for identifying specific regions or specific sites targeted by selection and for identifying evidence of demographic changes (Nielsen 2001). Most polymorphisms are selectively neutral; testing the neutral hypothesis has been one of the prime objectives of molecular population genetics, which makes general conclusions about the cause of molecular evolution (Kimura 1968). There has been evidence for positive selection and selective sweeps. Positive selection occurs when a new selectively advantageous mutation is segregating in a population. Selective sweeps refer to the removal of variation at neutral sites that are linked to positively selected alleles that go to fixation in a population (Nielsen 2001); in this type of selection the rate of recombination is correlated with the level of polymorphism in

43 Chapter 2 ______organisms (Begun & Aquadro 1992). Selective sweeps can reduce genetic diversity in the target genes or genomic regions. Population bottlenecks can also cause a genome- wide loss of diversity that could be misidentified as the signature of selection (Vigouroux et al. 2002). The effect of population bottlenecks on genetic variation has become important in population genetics, speciation theory and conservation biology. Populations that have recently suffered a severe reduction in size are especially important to identify for conservation because they are most likely to suffer increased risk of extinction (Ralls et al. 1988; Cornuet & Luikart 1996). Bottlenecks may be important in some modes of speciation, such as, colonization of a new area by a few individuals or a single mated female may result in extensive genetic changes which lead to reproductive isolation (Carson 1971; Cornuet & Luikart 1996). There is a biological problem in that a large number of functional genes occur in the mitochondrion and there is complete linkage between them, so a selective sweep for any one of the genes may lead to low genetic diversity (Hedrick & Miller 1992).

Tajima’s D (Tajima 1989) and Fu’s Fs (Fu 1996) are the most commonly used tests for neutrality. Tajima’s D is based on the probability of the observed number of haplotypes that give the level of genetic diversity by estimating the number of nucleotide differences (π) and the average number of segregating sites (θ) from pairwise comparisons in a sample of nucleotide sequences (Tajima 1989). Fu’s Fs test is based on the relationship between the observed number of haplotypes in a sample and the expected number at equilibrium (Ewens 1972). Tajima (1989) suggested that a sudden expansion leads to negative D values, and Fu’s Fs is especially sensitive to population expansions (Fu 1996).

2.5.5 Mismatch distributions

Mismatch distributions were calculated to examine evidence of any past population growth from mtDNA data. When the neutrality test was significantly negative, mismatch distributions were constructed to characterize the expansion. This shows the distribution of the number of pairwise differences between haplotypes. Time since expansion in mutational units was estimated by Tau (τ=2ut) using the equation t= τ /2u, in which t is time since expansion and u = µk, where k is the number of base pairs

44 Chapter 2 ______and µ is the mutation rate per site per generation (Slatkin & Hudson 1991). The generation time was taken for development (life cycle) depends on environmental factors mainly temperature. It is ~ 1 week in summer, or ~ 2-3 weeks in winter, so between 32-36 generation yearly.

To further examine evidence for demographic expansion, the mismatch distribution was tested with the “raggedness” (r) statistic, which measures the smoothness of the mismatch distribution. Expected data from expanded and stationary populations can then be compared (Harpending et al. 1993; Harpending 1994). A unimodal distribution is expected after a sudden and large demographic expansion (Slatkin & Hudson 1991; Rogers & Harpending 1992).

2.5.6 Analysis of Molecular Variance

To estimate population differentiation I used analyses of molecular variance

(AMOVA) for both mtDNA and microsatellite data. FST was introduced by Wright (1951), and is a suitable means of summarizing population structure by calculating population differentiation as a result of the combined effect of genetic drift and gene flow. FST estimates the proportion of the total variation that is between populations and is equal to zero when populations share the same alleles in equal frequencies and one when alternative alleles are fixed in different populations. Φ-statistics were calculated based on haplotype frequency and pairwise differences between haplotypes or alleles. This determined the genetic variation among different geographical regions. It was used to partition genetic variation among specified hierarchical groups, eastern and western populations (ΦCT), among populations within these groups (Φsc), and within all populations (Excoffier et al. 1992).

2.5.7 Mantel tests

Mantel tests were used to test for isolation by distance (IBD). This tests for a statistically significant relationship between genetic distance and geographic distance (Bohonak 2002). IBD is expected in situations of restricted gene flow where individual dispersal distances are less than the total distribution and the populations

45 Chapter 2 ______have reached equilibrium between gene flow, mutation and genetic drift (Slatkin 1993). The Mantel test computes a correlation between these two distance matrices

where the values of the two matrices are genetic distances (FST), which is obtained from Slatkin’s distance by using Arlequin version 3.11 (Excoffier et al. 2005) against geographic distances (km2), which was calculated by measuring the distances between pairs of sites across the continent and also along the coastline. Both microsatellite and mtDNA data were analysed using Mantel tests.

2.5.8 Hardy-Weinberg Equilibrium

Each microsatellite locus was tested for deviations from Hardy-Weinberg Equilibrium. Deviations from Hardy-Weinberg Equilibrium predictions can be caused by non-random mating (for example the presence of multiple species or populations in a sample), selection, migration, genetic drift, the presence of null alleles or mis- scoring of gels. Tests for deviations from Hardy-Weinberg Equilibrium proportions were done for each locus/population combination in GENEPOP version 4.0.10 (Raymond & Rousset 1995) using exact tests with Fisher’s method.

2.5.9 Linkage Disequilibrium

Linkage Disequilibrium is deviation in the frequency of genotypes in a population from the frequency expected if the alleles at different loci are associated at random. Recombination between genes on the same chromosome will not produce linkage equilibrium in a single generation if the alleles at the different genes began in non- random association with each other. This process of decay in gametic phase disequilibrium is inhibited when loci are physically linked on a chromosome because intergenic recombination is then restricted (Avise 2004; Griffiths et al. 2008). Non- random associations between polymorphisms at different loci are measured by the degree of linkage disequilibrium (LD). The level of linkage disequilibrium is influenced by a number of factors including genetic linkage, the rate of recombination, the rate of mutation, genetic drift, non-random mating, and population structure. The presence of multiple species in a sample will clearly result in linkage

46 Chapter 2 ______disequilibrium. The Genepop version 4.0.10 program also was used to test for deviation from linkage equilibrium.

2.5.10 Assignment tests

Recently, assignment tests have been used to provide a measure of population differentiation by calculating the proportion of individuals assigned to the population in which they were sampled (Paetkau et al. 1995; Rannala & Mountain 1997; Pearse et al. 2006). The assignment tests were used to determine the probability of individuals originating from their mitochondrial lineage on the basis of their multi- locus nuclear genotype.

47

Chapter 3

Analysis of genetic structure in Aedes vigilax, using a fragment of the mitochondrial COI gene

3.1 Introduction

The mitochondrial cytochrome oxidase I (COI) gene has been used successfully to examine population genetic structure, population history and evolutionary history of a wide range of insect species (Folmer et al. 1994; Lunt et al. 1996; Franck et al. 2001; Hughes et al. 2003; Eastwood et al. 2006; Hemmerter et al. 2007; Schmidt & Hughes 2007; Thomsen et al. 2009). Mitochondrial DNA was selected for this study to reconstruct phylogeographic histories, because it is maternally inherited, has a high mutation rate and small effective population size. Therefore, it has more linear or clonal evolution than nuclear DNA. Also, its coding genes have a more rapid rate of evolution, making it a useful marker for studying intraspecific population genetic variation (Avise 2004; Ballard & Whitlock 2004; Lee & Edwards 2008). In addition, the COI gene has been used successfully for intraspecific and interspecific studies of Anopheles and of Aedes mosquitoes (Walton et al. 2000; Cook et al. 2005).

Molecular genetic studies on other species of the genus Aedes have found evidence for good dispersal abilities and high levels of gene flow. Beebe et al. (2005) used the COI gene to identify genetic diversity and spread of the dengue vector Aedes aegypti between the Northern Territory, Townsville and Torres Strait in Australia. They found a separate Townsville population and another population on Thursday Island in the Torres Strait that was genetically divergent from the mainland populations. Szalanski et al. (2006) in their study of the population structure of the vector species Aedes vexans in Kansas, indicated a high level of genetic diversity with a large amount of genetic variation within and among populations. On the other hand, many other molecular genetic studies on different species of mosquito vectors have found

48 evidence that geographical boundaries and habitat differences act as barriers to dispersal and gene flow. For example, Beebe and Cooper (2002) found that climatic factors such as temperature, humidity and rainfall, and geographic barriers formed by elevation and sea restricted An. punctulatus distributions and gene flow in northern Australia and Papua New Guinea; O’Loughlin et al. (2007) used mitochondrial DNA sequences for molecular identification of four highly divergent lineages between northern and southern populations in the malaria vector Anopheles scanloni in Thailand. They found habitat, historical environmental changes and marine transgressions may cause restricted gene flow, population extinction and drift contributing to lineage divergence.

The aim of this chapter was to determine the extent of genetic variation within and among populations of Ae. vigilax in Australia. Specifically the aims were to:

• determine the population structure of Ae. vigilax at the continental scale;

• to analyse evolutionary relationships among haplotypes of Ae. vigilax to infer the evolutionary history of the species;

• determine any geographical component of its genetic structure such as isolation by distance, or the presence of obvious barriers to dispersal.

I hypothesized that dispersal would be widespread across the continent resulting in homogeneity of gene frequencies.

3.2 Material and methods

A total of 318 individual mosquitoes (as in chapter 2) were analysed for the mtDNA gene Cytochrome Oxidase subunit І using two oligonucleotide primers. The forward primer LCO1490 (5΄-GGT CAA CAA ATC ATA AAG ATA TTG G-3΄), and reverse primer HCO2198 (5΄-TAA ACT TCA GGG TGA CCA AAA AAT CA-3΄) (Folmer et al. 1994) were used to amplify a fragment of the COI gene. Polymerase Chain Reaction performed using 12.5μl final volumes containing 1.25μl PCR buffer (10x),

1.0μl of 25Mm MgCl2, 0.5μl of each primer, 0.25μl dNTP’s, 0.05μl of Taq DNA

49

1 polymerase and 1μl of /20 diluted DNA template and 7.95μl H²O (to take the volume to 12.5μl). The cycling profile was 94ºC for 5min, 25 cycles of denaturation at 94ºC for 30s, annealing at 40ºC for the first 15 cycles, and 52ºC for subsequent 30 cycles for 30s, extension at 72ºC for 1min, and a final elongation of 72ºC for 7min.

3μl of PCR products added to 1μl of concentrated loading dye were run on to a 1.6% agarose gel to estimate the concentration of purified DNA and to check if the PCR reaction had been successful. The gel was placed into an electrophoresis bath, containing 1x TAE buffer, and 100 volts was applied for about 30 minutes, which is sufficient for separation of the DNA fragments. The DNA fragments from PCR product were photographed directly by computer through a UV light provided by a UV transilluminator. Purification and Sequencing techniques are as described in chapter 2.

3.3 Data analysis

The sequences of a 433-basepair region of the mitochondrial Cytochrome Oxidase I (COI) gene were analysed from the total of 318 individual Ae. vigilax sampled from 13 geographic regions around Australia (Table 2.1 & Figure 2.1).

Haplotype and nucleotide diversity were calculated using DNAsp version 4.1 (Rozas et al. 2003). This program was also used to calculate DNA divergence between populations; while net sequence divergence between clades was calculated using MEGA version 4. The relationships among haplotypes were identified using a network with TCS version 1.21 (Clement et al. 2000). The neutrality statistics were examined to detect any evidence of past population growth (Fu 1997). DNAsp version 4.1 was used to run neutrality tests to examine evidence for population expansions and / or selection. Mismatch distributions were calculated to also examine evidence of any past population growth, using ARLEQUIN version 3.11. To estimate population differentiation AMOVA was used; whereas, Mantel tests were used to test for isolation by distance. Both tests were calculated using Arlequin version 3.11 (Excoffier et al. 2005).

50

As two divergent lineages were identified, based on mtDNA sequence data, some analyses were undertaken on the two clades separately in case there were cryptic species, as is common in mosquitoes. For example, for Anopheles gambiae s.l., Collins et al. (1988) identified a group of six morphologically identical but genetically different mosquitoes, of which only two were important vectors of malaria in Africa. This is extremely important because some species of these complexes may be important disease vectors whereas others may not.

3.4 Results

Sequences were obtained from 318 individuals, from which 119 unique haplotypes were identified. There were 55 segregating sites, 50 were synonymous, 5 were non synonymous and 38 were parsimony informative (15% in the first codon position, 0% in the second and 85% in the third).

3.4.1 Bayesian analysis

The Bayesian method showed that the sequences in the analysis formed two distinct clades (Figure 3.1). One clade (clade-I) contained only individuals from eight sites in four locations across eastern Australia (QLD & NSW) from Brisbane, Gold Coast, Cairns and Sydney. It had 27 unique haplotypes from 44 individuals. In contrast, the other clade (Clade-II) contained individuals from all 13 sites, and was found throughout the sampling area (Northern Territory, Western and eastern Australia); 92 unique haplotypes were detected from 274 individuals. These two distinct clades were separated by seven mutations, with a total of 57 variable sites in both clades.

51

HE M05 Sydney B RI08 P LM07 NE W25 ONS 04 Gold Coast BAN01 WY N12 B RI24 Brisbane ONS 06 P LM11 WY N13 B RI30 Cairns STA16 BAN17 B RI17 BAN09 Darwin B RI29 HE M11 RUC22 HE L23 Wyndham B RI11 WIL04 B RI18 NE W02 Willie Creek HE M24 HE M07 WIL19 Onslow HE L21 NE W03 ONS 15 ONS 26 Perth BAN06 WIL15 B RI10 BAN25 ONS 03 HE M23 WY N25 BAN13 BAN18 BNA05 SMA07 NE W19 HE M03 HE M28 B RI02 STA10 HE M04 STA28 RUC11 RUC17 B RI01 WIL13 STA23 WY N06 Clade-II HE M18 HE M19 ONS 29 SMA12 B RI15 B RI20 HE L02 HE M10 NE W20 HE M02 ONS 10 SMA25 BAN29 B RI09 WIL09 WY N08 B RI14 HE M29 BAN08 LS P 22 LS P 20 LS P 29 BAN11 BAN20 ONS 28 SMA09 RUC23 WY N02 BAN19 NE W22 A eprocax BAN07 BAN28 B RI21 HE M08 B RI03 B RI23 NE W29 RUC05 BAN10 BAN23 RUC14 SMA29 HE L10 HE M06 HE M16 HE M25 Clade-I BAN16 BAN21 NE W18 HE M13 HE M01 HE M17 SMA05 STA30 HE M12 STA04 RUC25 1 change -0.001 substitutions/site

Figure 3.1: Phylogenetic relationships among 119 mtDNA haplotypes of Ae. vigilax individuals collected from across Australia constructed using MrBayes.

52

3.4.2 Divergence time and mismatch distribution analysis

Based on molecular clock estimates for COI (Gaunt & Miles 2002; Galtier et al. 2009), the two different lineages diverged by 2.1% approximately 900,000 years ago.

Fu’s Fs demonstrated significant negative values in both clades; and Tajima’s D values were non-significant in clade-I and mostly non-significant in clade-II. Mismatch distributions for each of the clades showed smooth unimodal patterns (Figure 3.2), which suggested recent expansion events (Rogers & Harpending 1992). The distributions clearly contrast with those expected for populations at mutation drift equilibrium (Slatkin & Hudson 1991; Aris-Brosou & Excoffier 1996). The raggedness indices were non-significant (Table 3.1), also suggestive of a population expansion. To estimate the time since the expansions, the mutation rate of Drosophila of 10-8 per site per year (Powell et al. 1986; Gaunt & Miles 2002; Galtier et al. 2009) was assumed. Tau (τ) was estimated separately for each clade (Table 3.1). The expansions were estimated to have occurred approximately 11 000 years ago (95% confidence interval [C.I.] 6-13 thousand years) for clade-I; and 13 000 years ago (95% C.I. 8-16 thousand years) for clade-2, both in the late Pleistocene.

Mismatch distribution clade-I Mismatch distribution clade-II

300 10000

250 8000 200 Observed 6000 Observed 150 Expected 4000 Expected 100 Frequency Frequency 50 2000 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 1 3 5 7 9 11 13 15 17 Pairwise differences Pairwise differences

Figure 3.2: Mismatch distributions of Ae. vigilax for clade-I & clade-II.

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Table 3.1: Mismatch distribution results for clade I and clade II for Ae. vigilax.

Demographic Expansion Model Clade-I Clade-II Tau (T) (95% C.I.) 2.8 (1.621-3.328) 3.3 (1.953-4.145) Onset of expansion (95% C.I.) 11 kya (6-13) 13 kya (8-16) Raggedness Index 0.055 0.021

Overall r = 0.038 (p = 0.415)

3.4.3 Genetic diversity

The COI sequences of Ae. vigilax were characterized by low nucleotide and high haplotype diversity (Tables 3.2a & 3.2b). The pooled haplotype diversity appears to be higher in clade-I samples (Brisbane, Gold Coast and Cairns) than clade-II samples, even though the number of individuals and haplotypes per site is lower for clade-I samples. The lowest diversity was in the Perth sample.

3.4.4 Neutrality tests

The neutrality tests showed significant negative values of Fu’s Fs in both clades, and non-significant values of Tajima’s D in clade-I, but in clade-II some sites such as HEM and BRI in Brisbane, and HEL in Gold Coast were significant (Table 3.3).

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Table 3.2 (a) Diversity measures for populations of clade-I using 433 bp of COI. Numbers in parenthesis are standard deviations.

No. No. No. Polymorphic Sample site Sample name individuals haplotypes sites Haplotype diversity Nucleotide diversity Sydney Newington (NEW) 6 5 5 0.933 ± (0.122) 0.004 ± (0.001) Salt Ash "Port Stephens Shire" (STA) 4 3 2 0.833 ± (0.222) 0.002 ± (0.001) Gold Coast Rusty Cars (RUC) 3 6 7 1.000 ± (0.096) 0.006 ± (0.001) Helensvale Rd. (HEL) 6 5 6 1.000 ± (0.126) 0.006 ± (0.001) Brisbane Hemmant (HEM) 8 8 9 1.000 ± (0.063) 0.006 ± (0.001) Banyo (BAN) 8 7 9 0.964 ± (0.077) 0.008 ± (0.001) Brighton (BRI) 4 4 13 1.000 ± (0.177) 0.015 ± (0.006) Cairns Smith Field (SMA) 5 3 14 1.000 ± (0.272) 0.022 ± (0.009)

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Table 3.2 (b) Diversity measures for populations of clade-II

Sydney Newington (NEW) 16 19 18 0.974 ± (0.028) 0.008 ± (0.001) Salt Ash "Port Stephens Shire" (STA) 25 15 15 0.980 ± (0.024) 0.007 ± (0.001) Gold Coast Rusty Cars (RUC) 16 15 17 0.992 ± (0.025) 0.008 ± (0.001) Helensvale Rd. (HEL) 30 9 15 0.817 ± (0.095) 0.006 ± (0.001) Brisbane Hemmant (HEM) 21 14 18 0.943 ± (0.033) 0.007 ± (0.001) Banyo (BAN) 22 15 17 0.922 ± (0.046) 0.008 ± (0.001) Brighton (BRI) 26 23 33 0.988 ± (0.016) 0.011 ± (0.002) Cairns Smith Field (SMA) 26 13 23 0.880 ± (0.051) 0.009 ± (0.002) Darwin Palm Creek (PLM) 20 10 9 0.917 ± (0.049) 0.005 ± (0.001) Wyndham Wyndham (WYN) 22 17 17 0.938 ± (0.034) 0.006 ± (0.001) Willie Creek Willie Creek (WIL) 16 11 12 0.908 ± (0.063) 0.005 ± (0.001) Onslow Onslow (ONS) 16 21 20 0.975 ± (0.015) 0.008 ± (0.001) Perth Leschenault Peninsula "Belvidere" (LSP) 18 4 4 0.600 ± (0.077) 0.002 ± (0.000)

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Table 3.3 Results of Tajima’s D and Fu’s Fs tests for Ae. vigilax populations; shows clade-I and clade-II. CLADE –I

Location Sites Sample Tajima’s P-value Fu’s FS P-value size D Brisbane HEM 8 -1.06800 0.17300 -5.99945 0.00000 Brisbane BAN 8 0.19202 0.57500 -4.9581 0.00200 Brisbane BRI 4 -0.84307 0.09500 -0.18671 0.24400 Cairns SMA 3 0.00000 0.71400 1.06599 0.44700 Gold Coast RUC 6 -0.88407 0.25500 -3.64154 0.00100 Gold Coast HEL 5 -0.66823 0.34400 -2.51718 0.01600 Sydney NEW 6 -0.82582 0.28300 -4.55266 0.00000 Sydney STA 4 -0.70990 0.27300 -3.13549 0.00000

CLADE –II

Location Sites Sample Tajima’s P-value Fu’s FS P-value size D Brisbane HEM 21 -1.60210 0.04100 -26.37295 0 Brisbane BAN 22 -0.96829 0.17300 -26.01373 0 Brisbane BRI 26 -1.74722 0.02600 -25.68160 0 Willie Creek WIL 16 -1.35529 0.07300 -20.72932 0 Onslow ONS 30 -1.22973 0.10700 -26.16523 0 Wyndham WYN 26 -1.39657 0.06300 -26.49852 0 Darwin PLM 16 -0.78396 0.24000 -21.69838 0 Cairns SMA 25 -1.44564 0.05900 -26.01138 0 Perth LSP 20 -0.89489 0.22400 -3.4E+38 0 Gold Coast RUC 16 -1.12105 0.13800 -16.15331 0 Gold Coast HEL 16 -1.75813 0.02400 -20.03217 0 Sydney NEW 22 -1.18562 0.11100 -26.08950 0 Sydney STA 18 -1.01293 0.13900 -21.12762 0

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3.4.5 AMOVA

The overall AMOVA analysis, with both clades together showed significant genetic variation between eastern and western regions. The FCT value was significant (P=0.018) (see Table 3.4). In contrast, there was no significant variation between eastern regions for clade-I, or between eastern and western regions for clade-II when analysed separately (Table 3.5). There was, however, significant genetic variation among populations within eastern groups (Fsc) for clade-I (p=0.002) and among populations within eastern and western groups for clade-II (p<0.0001), although there was a large amount of variation within populations for both clades (Table 3.5). This indicated that there was significant genetic structure among populations within at least some regions.

3.4.6 F-statistics

The overall pairwise differences between populations using ΦST values showed significant ΦST values for 29 of 40 comparisons between eastern and western sites and most of them were highly significant (p< 0.001). In contrast, there were no significant differences between eastern sites, and only the Perth site (LSP) was significantly different from other western sites (Table 3.6).

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Table 3.4 Results of Overall AMOVA analysis for Ae. vigilax from all sites. * P< 0.05 Source of variation d.f Percentage of F-Statistics P-value variation

Between groups 1 6.65 % FCT: 0.06647 0.0176*

Among populations 8 -0.75 % FSC: -0.00806 0.80547 within groups

within population 246 94.11% FST: 0.05894 0.06745

Table 3.5 Results of hierarchical AMOVA for Ae. vigilax based on 433bp of COI sequence. CLADE –I Source of variation d.f. Percentage of F-Statistics P-value variation

Among eastern 3 -4.36 FCT: -0.04358 0.80938 regions

Among populations 4 9.35 FSC: 0.08956 0.00293 within regions within populations 36 95.01 FST: 0.04988 0.04888

CLADE –II

Between western & 1 0.77 FCT: 0.00771 0.17986 eastern regions

Among populations 11 8.39 FSC: 0.08457 0.0000 within regions within populations 261 90.84 FST: 0.09162 0.0000

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Table 3.6 Pairwise ΦST values based on mtDNA differentiation among 13 sites for Ae. vigilax.

Gold Gold Willie Sydney Sydney Coast Coast Brisbane Brisbane Brisbane Cairns Darwin Wyndham Creek Onslow Perth NEW STA HEL RUC HEM BAN BRI SMA PLM WYN WIL ONS LSP NEW 0 STA -0.00371 0 HEL -0.02889 -0.01115 0 RUC -0.01535 -0.01377 -0.03146 0 HEM -0.01191 -0.00983 -0.02611 -0.03052 0 BAN -0.00973 -0.01333 -0.01809 -0.02688 -0.01659 0 BRI -0.01584 -0.01094 -0.01779 -0.00312 0.00806 0.00708 0 SMA -0.00064 0.00069 0.00502 0.01428 0.02269 0.02344 -0.02153 0 PLM 0.08195*** 0.07776** 0.07190 0.09355* 0.09448** 0.11259** 0.02467 0.03076 0 WYN 0.07707** 0.08164** 0.08819*** 0.10939*** 0.11401** 0.11234*** 0.02099 0.01699 0.01907 0 WIL 0.06762** 0.07172** 0.06887 0.09284 0.09695** 0.10995*** 0.01211 0.01102 -0.02095 0.00699 0 ONS 0.07166** 0.06921*** 0.08167*** 0.10200*** 0.10607*** 0.10967*** 0.02089 0.01253 -0.00153 0.01682 -0.01922 0 LSP 0.32879*** 0.35940*** 0.38988*** 0.35086*** 0.35074*** 0.29977*** 0.31788*** 0.35853*** 0.61741*** 0.46078*** 0.60545*** 0.46366*** 0 * P<0.05 ** P<0.03 *** P<0.001

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3.4.7 Isolation by Distance

There was a significant correlation between geographic, across the continent, and genetic distances (r2 = 0.27, P < 0.05) when both clades were analysed together (Figure 3.3). The result was similar when the distances between pairs of sites were measured along the coastline (data not shown). However, Mantel’s test indicated no significant correlation between geographic distance and genetic distance for mtDNA in either clade when tested separately. In clade-I, the correlation coefficient (r2) was 0.30 (P = 0.12) and for clade-II the correlation coefficient was 0.27 (P = 0.09) (Figures not shown). This indicates an isolation by distance effect for populations overall, but not within each clade.

Mantel test

1.8 1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 Slatkin's linearised FST 0 0 1000 2000 3000 4000 5000 Geographic Distance (km 2)

Figure 3.3: Scatter diagram of geographic distance and Slatkin’s linearized FST between pairs of eastern and western populations for mtDNA data. (The points are denoting pairwise between sites).

3.4.8 Network and Nested Clade Analysis

The haplotype network illustrated a high level of genetic diversity with 119 haplotypes detected. In both clades (I & II) most haplotypes were shared among multiple sites, except that haplotypes from Perth in clade-II were unique and were restricted to an individual population (LSP). They were found only in clades (2-9) and (2-10) (Figure 3.4).

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The NCA indicated that there was significant geographical structure at many steps. It contained twenty two two-step clades, seven three-step clades, two four-step clades and the total cladogram as shown in Figure 3.4. Notably, the nesting clades 2-18 and 2-21 in clade I; and clades 2-9 and 2-14 in clade-II that contain most of the genetic variation at the haplotype level, are found in most samples, and cover almost all sampling locations and have the largest sample sizes. Also, they have many mutational connections. They are likely to be ancestral (Crandall & Templeton 1993). Based on coalescent theory, under restricted gene flow, older haplotypes should have a wider distribution than those more recently derived; also they should have more mutational derivatives and be placed internally in the network, whereas recent haplotypes are more likely to be found at the tips (Templeton et al. 1995). In contrast, nesting clades 2-4, 2-6, 2-7, 2-11, 2-13 and 2-22 in Clade-II have smaller levels of haplotype variation and small numbers of observations. Four clades showed non-random geographic distributions and indicated significant results: three-step clades (3-2) and (3-6); four-step clade (4-1); and the total cladogram (Table 3.7).

Nested clade analysis provided evidence for restricted gene flow with isolation by distance for clades (3-2) and (3-6) in clade-II and clade-I respectively; the inference key suggested that clade (3-2) had significant geographical structure due to the tip clade (2-5) having significantly small Dc and Dn values, and the interior clade (2-14), which was the ancestor of clade-II, having significantly large Dc and Dn values. The significant pattern for clade (3-6) was caused by the interior clade (2-21), which is the ancestor of Clade-I, having significantly large Dc and Dn values (Table 3.7). Clade (4-1) was also significant with contiguous range expansion inferred because the interior clade (3-2) had significantly small Dc and Dn values that suggest the geographic distribution of spatial genetic variation within clade (4-1) shows contiguous range expansion from west to east and from north-west to south-west (figure 3.4). It was uncertain which of clade (4-1) or (4-2) was a tip or interior. Clade (4-2) was considered to be the tip clade due to its being less common, having fewer haplotypes and being less widespread. Clade (4-1) was designated as the interior clade as it was found within most of the populations of Ae. vigilax samples across the entire range (figure 3.4). NCA supports allopatric fragmentation at the highest level of the total cladogram where allopatric fragmentation

62 of clades (4-1) and (4-2) was inferred. The significant geographical pattern for the total cladogram caused by the interior clade (4-1) had Dc and Dn values significantly large and for the tip clade (4-2) these values were significantly small.

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Newington Sydney Sal Ash 2-19 Rusty car Gold Helensvale Coast Hemmant Banyo Brisbane

Clade-I Brighton Cairns eastern 2-18 Darwin Australia 2-20 Wyndham 3-6 2-21 Willie creek Onslow Perth 2-17 2-16

3-5 4-2

2-9 3-4

2-12 2-10 2-13

2-8

2-1 2-15

Clade-II Western, 2-2 2-11 2-3 Northern & 3-1

eastern 2-4 Australia 2-7

2-22

2-14 2-6

2-5 3-2 3-3 4-1 3-7 Figure 3.4: Network showing the relationships between 119 haplotypes found in 318 individuals and the arrangement of nested clade for Ae. vigilax. Haplotypes in clade-I and clade-II (approximately 2.1% divergent). The lines between haplotypes indicate a single mutation. Missing haplotypes are indicated by black circles. 64

Table 3.7 NCA results showing only clades with significant geographical relationships based on the X2 test.

Nested Clade Dc Dn Inference key Conclusion Clade position Clade 3-2 Restricted gene 2-5 Tip 366 S 1270 S 1-2-3-4-No flow with IBD 2-11 Tip 853 S 1360 2-14 Interior 1596 L 1585 L 2-15 Tip 368 1276 I-T Tip 954 L 263 L Clade 3-6 Restricted gene 2-18 Tip 261 249 1-2-3-4-No flow with IBD 2-19 Tip 170 208 2-20 Tip 24 146 2-21 Interior 539 L 514 L I-T Tip 322 L 285 L Clade 4-1 Contiguous Range 3-1 Tip 1661 1687 1-2-11-12-No Expansion 3-2 Interior 1528 S 1599 S 3-3 Tip 1465 S 1594 3-4 Tip 1645 1914 L 3-7 Tip 1595 1614 I-T Tip -72 -128 S Total cladogram Allopatric 4-1 Interior 1677 L 1658 L 1-2-3-4-9-No fragmentation 4-2 Tip 335 S 1323 S I-T Tip 1341 L 335 L

Significantly small or large values for Dc and Dn are indicated by ‘S’ and ‘L’ respectively. Numbers in

Inference key indicate the steps followed. 65

3.5 Discussion

The Bayesian analysis of the mtDNA sequence data produced a tree with two distinct clades in Australia. One (clade-I) consisted only of eastern Australian haplotypes and the other (clade-II) contained western, northern and eastern Australian haplotypes. My results from the molecular clock analysis suggested that the two different lineages diverged about 900,000 years ago, and the mismatch distributions suggested that there had been very recent expansion in both clades; NCA showed contiguous range expansion throughout the western region within clade-II.

Phylogenies of many species may result in regionally monophyletic clades due to biogeographic barriers separating them (Avise & Walker 1998; Avise 2008). The phylogeographic pattern for Ae. vigilax fits category ІІ of Avise’s phylogeographic categories. It showed two clades that are widely distributed across eastern Australia. It is possible that the current distribution is explained by isolation at some time in the past, followed by range expansion of one or both clades resulting in secondary contact. This suggests that the supposed Carpentarian arid Barrier may have been associated with the divergence between eastern and western Australian Ae. vigilax populations (Cracraft 1986; Ford & Blair 2005). This may be inferred from the NCA analysis which showed allopatric fragmentation between clade-I (clade 4-2) and clade-II (clade 4-1). The divergence between east and west was corroborated with AMOVA, which indicated significant population structure at FCT (P = 0.018) for the subdivision of populations into eastern and western regions. This finding of divergence during the Pleistocene, followed by subsequent range expansion has been suggested by several other studies in the region. Chenoweth et al. (1998a) showed divergence in the barramundi Lates calcarifer between eastern and western Australia using the mtDNA control region and Toon et al. (2003) used mtDNA gene COI to show divergence of eastern and western clades of the magpie Gymnorhina tibicen in Australia.

My data supports not only a range expansion, but also a population expansion, as evidenced by the mismatch analysis and neutrality tests. The mismatch analysis and the Fu’s Fs values provide clear evidence of recent population expansion in both clades

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(Tajima 1989; Aris-Brosou & Excoffier 1996). Mismatch distributions show a closer fit to expectations under population growth than to constant population size (Figure 3.2). A significant fit to a unimodal curve but non-significant raggedness index means the data show a relatively good fit to a model of population expansion. Rogers & Harpending (1992) and Peck & Congdon (2004) suggested that significant Fu’s Fs values and non- significant Tajima’s D values are most likely to result from a population expansion rather than selection. The results from this study are exactly those expected under a population expansion scenario, with significant negative values of Fu’s Fs and non-significant values of Tajima’s D. I suggest that clade-II expanded from north-west to south-west and from west to east to establish secondary contact with clade-I populations.

Similarly, evidence for late Pleistocene population expansions has been found in other studies. For example, Hughes et al. (2003) showed that mayfly populations from the Rocky Mountains, Colorado expanded around 150 000 years ago. There is also evidence for expansion of mosquito populations globally. For example, population expansion of An. dirus in Southeast Asia has been proposed between 135 000 and 250 000 years ago based on the mitochondrial cytochrome oxidase I gene (Walton et al. 2000). Population analysis of an island malaria vector An. flavirostris in the Philippines showed evidence of a recent demographic expansion 214 000 years ago based on mismatch distribution analysis of the mitochondrial COI gene (Foley & Torres 2006). An. jeyporiensis in southern China showed a recent population expansion 50 000 years ago using mtDNA COII sequences (Chen et al. 2004). In my study, the two clades Ae. vigilax have only expanded recently (6 000 – 13 000 years ago), since the last glacial maximum (Otto- Bliesner et al. 2006), whereas most other studies have suggested expansions longer ago. This discrepancy may be due to the generation assumed for this species approximately 30 generations per year, compared to the generation time of Anopheles, with only 10 generations per year (Walton et al. 2000).

There was significant divergence between clade-I and clade-II; and both clades occurred sympatrically at most eastern sites. This may indicate the presence of two cryptic species, which is often observed in mosquitoes (Cook et al. 2005). The two clades differed by 2%, which is around the cut off suggested by proponents of DNA bar-coding to indicate

67 separate species (Eccleston 2007; Natural History Museum 2007; Kumar et al. 2007). However, data from a nuclear gene is necessary before such a conclusion is possible. This would indicate whether the two clades were not interbreeding and therefore whether or not they represent separate biological species (Beebe et al. 2000; Ravel et al. 2002).

These results raise the possibility that independent evolution in the different locations may have created strains with different abilities to transmit viruses. This idea is partly supported by results for RRV reported by Lindsay et al. (1993a) who isolated RRV from mosquitoes, humans and other mammals from different geographical regions in Australia. They found two clusters of the T48 topotype strain of RRV isolated from Ae. vigilax; with one cluster isolated from WA, NT, QLD, NSW & Vic and the other cluster from NSW, QLD and NT. The apparent geographical overlap of the distributions of these strains with those of Ae. vigilax clades suggests the possibility that the two lineages may have different vector competencies. A similar suggestion was made by Hemmerter et al. (2007) in their research on the vector competence of Cx. annulirostris for Japanese encephalitis, suggesting that different competencies of two lineages explained the absence of that disease on mainland Australia.

3.5.1 Clade – I

This study on the genetic structure of Ae. vigilax, sampled from across the continent, using hierarchical analysis showed that there was significant population structure with a significant component of variability due to genetic variation among sites within regions, and suggesting some limitation to gene flow among eastern populations. Similar results have been reported for An. punctipennis by Fairley et al. (2000) who found significant genetic structure among localities within latitudinal regions. Szalanski et al. (2006), studying Ae. vexans populations among four states from 11 counties in Kansas, showed a significant variance component among populations within groups. Widespread gene flow and patchy habitat availability probably results in local extinction of some geographically widespread lineages and persistence of others (Avise et al. 1992). My results show high levels of genetic variation at a relatively small spatial scale; these results could also result from patchy recruitment. Similar patterns were observed by Bunn & Hughes (1997),

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Hughes et al. (1998), Hughes et al. (2000) and Schultheis & Hughes (2005), where the highest levels of variation were found at the smallest spatial scale in a number of aquatic insect species in freshwater streams in south-east Queensland. There was no significant differentiation at the largest scale, across catchments. These patterns have been proposed to result from a limited number of females ovipositing at a site, and limited larval movement among sites. This was also shown in Ae. vigilax in south-east Queensland by Chapman et al. (1999). They found significant genetic variation in larvae between some breeding sites, and non-significant genetic differentiation among adult females throughout the study area.

In my study, the analysis of molecular variance showed significant genetic variation at small spatial scales, among eastern populations, but no significant variation at large scale, among eastern regions for clade-I. This could be inferred as a signal of patchy recruitment resulting from a limited number of females contributing to reproduction at a site.

Clade-I was much less common than clade-II (Table 3.2), so some of the non-significant results for this clade could result from the smaller sample size. Nevertheless, the results at the large scale imply widespread gene flow. Alternatively, the recent population expansion may mean that the populations are not in equilibrium between gene flow and genetic drift. This may be explained where the time to reach equilibrium is dependent on the effective population size and the probability of migration per generation (Crow & Aoki 1984). The effects of historical events can also persist in genetic data for a long time, which means a temporary halt between historical and contemporary patterns can greatly contribute to the error in inference of demographic structure made from genetic patterns. This error may be partially reduced through genetic analyses of mtDNA markers due to a small theoretical effective population size (Chenoweth et al. 1998b).

The results from the Mantel’s test showed lack of isolation by distance in clade-I, which can occur either because the individual dispersal distance is greater than the range of the study area, or because the population has not reached equilibrium since a recent expansion. The earlier study of Ae. vigilax by Chapman et al. (1999), also found that IBD

69 was not significant in south-east Queensland although that study was at a much smaller scale. So clade-I is probably a very efficient disperser with local extinctions, sampling error and/or patchy recruitment probably explain the significant differences between nearby populations.

3.5.2 Clade – II

Most pairwise comparisons for clade-II showed low Fst values except those including Perth. Significant values of Fu’s Fs indicated an excess of recent mutations and possible population expansion and/ or selection, as did the negative values of Tajima’s D and significant p-values for some sites in Brisbane and Gold Coast, indicative of expansion (Table 3.3). Hierarchical analysis again showed that there was no significant differentiation at the highest level of clade-II, but there was significant differentiation among sites within regions.

On the other hand, the genetic results from the Mantel’s test for this clade showed lack of isolation by distance, suggesting that there is gene flow among populations of clade-II or they are not in equilibrium. If there is no relationship between these distances, this may due to the relatively small sample sizes as mentioned above or population subdivision may be hidden within the sample, or there may have been a selective sweep or a bottleneck during recent evolution (Chakraborty et al. 1994).

Also it may reflect other factors such as climate, habitat variability and transport by humans (Tabachnick & Black 1995). Moreover, Kambhampati & Rai (1991) did not observe a correlation between genetic and geographic distance among populations of Aedes albopictus around the world. This species is known to be spread by human transport. Thus, human movement can transport mosquitoes long distances, thereby increasing genetic similarity between geographically distant populations.

Most haplotypes in the Perth population were not shared with other sites. This is the only population that has never had larvicide or adulticide treatments, so the genetic differentiation of this population could result from different selection pressures in that

70 location. Alternatively, there could be restricted gene flow between Perth and other WA sites. Possibly the isolation of this location is caused by geographical or climatic barriers. The Murchison arid barrier is located between the Perth and Onslow locations and may represent a barrier to dispersal as suggested for some other species (Cracraft 1986; Ford 1987). It could be that population expansion to the Perth site occurred during wetter times before that barrier formed.

The genetic structure observed could be due to distance and habitat differences acting as barriers to dispersal and gene flow for this species and possibly for the virus. This has been shown for other mosquito species, such as Anopheles aquasalis from Venezuela, Trinidad and Brazil (Conn et al. 1993); An. nuneztovari from Venezuela-Colombia and Amazonia, which were separated by three mountain ranges (Conn et al. 1993; Conn et al. 1998); An. punctipennis from Vermont (Fairley et al. 2000); An. arabiensis from Nigeria (Onyabe & Conn 2001); Beebe & Cooper (2002) found An. punctulatus from north Australia and Papua New Guinea are isolated by climatic barriers such as temperature, humidity or rainfall and geographic barriers such as mountains or sea gaps. O’Loughlin et al. (2007) used mitochondrial DNA sequences for molecular identification of four divergent lineages between northern and southern populations in the malaria vector Anopheles scanloni. All these studies demonstrate the role of barriers such as geography and climate in restricting species distribution and gene flow.

3.6 Conclusion

This chapter has examined the distribution of Ae. vigilax across geographic areas to determine the extent of spatial and temporal concordance in phylogeographic patterns and resolve that with the biology of the individuals and climate history.

It has demonstrated that Ae. vigilax is a wide-ranging species with efficient dispersal in Australia. In this chapter, I provide evidence that there are two distinctly divergent clades. Both had significant structure and showed evidence of recent population expansion at most sites. The fact that clade-I only occurs in the east, implies restricted east to west gene flow. Alternatively they might be different species and the clade-I 71 species is restricted to the east by habitat. I suggest that later, haplotypes of clade-II dispersed from north-west to south-west and from the west to east to establish secondary contact with clade-I along the east coast. The question of whether clade-I and clade-II are separate species is addressed in the next chapter.

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Chapter 4______

Chapter 4

Are the two mitochondrial clades of the Ross River Virus Vector (Ae. vigilax) reproductively isolated?

4.1 Introduction

Speciation is the process by which new species arise through evolution. The details of this process have been debated for many years since publication of Darwin’s Origin of Species in 1859. The concept of species is still controversial (Dobzhansky 1935; Coyne 1992; Avise 2000; Griffiths et al. 2008). The most widely accepted species concept is the biological species concept, which recognised distinct species on the basis of reproductive isolation (Mayr 1999; Avise 2004; Coyne & Orr 2004; Allendorf & Luikart 2007). This is particularly easy to determine if the two ‘taxa’ in question occur sympatrically. If the two species are reproductively isolated, they should show evidence of non-random mating.

A consequence of reproductive isolation is genetic divergence of other characters, particularly adaptive changes that evolve through natural selection in response to different environmental conditions in separate geographic areas (Avise 2004). However, these changes are not always obvious in the external morphology.

There are four geographic modes of speciation that have been proposed (Avise 2004; Coyne & Orr 2004). These are based on the extent to which speciating populations are geographically isolated from each other: allopatric, peripatric, parapatric, and sympatric.

The most commonly accepted method of speciation is allopatric speciation. Allopatric speciation begins when subpopulations of a species become isolated geographically by an extrinsic barrier, such as habitat fragmentation or migration and develop intrinsic (genetic) reproductive isolation, when individuals of the populations can no longer interbreed (Allendorf & Luikart 2007). The isolated subpopulations then

73 Chapter 4______undergo genotypic and/or phenotypic divergence as they either come under dissimilar selective pressures or independently undergo genetic drift or mutation or local adaptation (Dobzhansky 1935; Coyne & Orr 2004). When these subpopulations come into contact again (secondary contact), they have evolved such that they are reproductively isolated and are unable to exchange genes and the two subpopulations can then be considered separate species (Avise 2004).

Peripatric speciation occurs when a new species forms in a peripheral population isolated by a natural barrier. It is similar to allopatric speciation in that the populations are geographically isolated, but here the two populations are closely adjacent to one another and one of the populations is much smaller (isolated niche) than the other. It was first identified by Mayr (1963). Genetic drift is often proposed to play a significant role in peripatric speciation, and small populations may undergo bottlenecks.

Parapatric speciation is midway between allopatric and sympatric speciation. It occurs when a new species arises where subpopulations become geographically and reproductively isolated, due to variation in the mating habits of a population within a continuous geographical area. The parent species live in contiguous habitats. They do not overlap but inhabit areas immediately adjacent to each other with restricted gene flow (Coyne & Orr 2004).

Sympatric speciation occurs when a new species appears within the range of a single parent species that inhabits the same geographic area. The two species overlap geographically with no barrier to gene flow.

In biology, a cryptic species (also called sibling species) complex is a group of species which meet the biological definition of species in that they are reproductively isolated from each other, but their morphology is very similar. Mayr (1948) was the first to generally review sibling species. The individual species within the complex can sometimes only be separated by DNA sequence analysis, or through life history studies. Molecular markers can be of great utility in diagnosing closely related species, even when morphological or other markers fail (Avise 2004). Morphological variability is restricted, but the physiological differences and the reproductive barriers between populations clearly indicate the specific status of many populations.

74 Chapter 4______Populations may have reached a high level of differentiation while still having relatively similar morphology. It is important to understanding past demographic processes when using genetic markers to answer questions relating to mechanisms of population divergence and speciation (Avise & Walker 1998). Mayr (1999) noted that the first discovery of sibling species was reported in 1768 by White in the avian genus Phylloscopus. However, the American Ornithologists’ Union in 2005 reported that sibling species were first recognized by William Derham (1718) (Winker 2005).

There are many examples of cryptic species complexes in insects. For example, the Drosophila willistoni group consists of 23 species, of which six are sibling species based on allozyme differences at 28 loci (Ayala & Powell 1972). Garcia et al. (2006) confirmed these six sibling species using a new method based on allozyme variation at the Acph-1 locus, because each sibling species has it own specific allelic form. Four pairs of avian sibling taxa could be distinguished using electrophoretic variation in proteins and restriction site variation in mtDNA (Avise & Zink 1988). For mosquitoes, similar methods have found that genomic DNA probes made from the l8S ribosomal DNA could distinguish between two recently identified members of the Anopheles punctulatus complex, Anopheles sp. near punctulatus from Papua New Guinea, and Anopheles farauti No. 7 from the Solomon Islands (Beebe et al. 1996).

If more than one species is present at a site, deviations from HWE and linkage equilibrium would be expected. This would represent non-random mating between the two species. In order to determine whether the two clades identified in chapter 3 represented different cryptic species, I used microsatellite variation to:

a) test for Hardy Weinberg Equilibrium. b) test for Linkage Disequilibrium. c) use multilocus assignment tests to determine whether individuals could be correctly assigned to their mtDNA clade on the basis of their microsatellite genotype. If there were diagnostic alleles, then individuals should be assigned to their mtDNA clade with a high level of certainty.

75 Chapter 4______4.2 Methods

4.2.1 Identification of microsatellite loci

The microsatellite markers used in this study for Ae. vigilax (Table 4.1) were isolated from other species as described in chapter 2. AP5 was from Aedes polynesiensis, JO338B from Aedes japonicus and redesigned GT48 from Culex pipiens; AP5 and JO338B were identified from Behbahani et al. (2004) and Widdel et al. (2005) respectively, while GT48 was redesigned from (cxpGT46) Keyghobadi et al. (2004). Due to the large allele size range of the locus in Ae. vigilax (between 350-450 bp) new primers were designed to reduce the size of amplicons and improve the quality of banding patterns.

The AP5 locus was isolated and characterized from 30 individuals of the Aedes scutellaris complex (15 from Sigatoka and 15 from Suva in Fiji). The locus was polymorphic in Ae. polynesiensis and showed no significant departures from Hardy- Weinberg equilibrium, and the pairwise tests for linkage disequilibrium were not significant (Behbahani et al. 2004). The JO338B locus had been tested in American populations of Aedes japonicus japonicus. This locus was also found to be polymorphic in two other of the four Ae. japonicus subspecies and Aedes koreicus. There was no significant deviation from Hardy-Weinberg equilibrium, and pairwise tests of linkage disequilibrium between loci were non-significant also (Widdel et al. 2005). The cxpGT46 locus was isolated and characterized from 29 individuals in the northern house mosquito Culex pipiens. The locus was highly variable with many alleles with expected heterozygosity ranging from 0.66 to 0.93; no significant deviation from Hardy-Weinberg equilibrium or pairwise tests of linkage disequilibrium between loci were observed (Keyghobadi et al. 2004).

76 Chapter 4______Table 4.1: Characteristics of three microsatellite loci analysed for Ae. vigilax.

Locus Repeat motif Alleles No. of Primer Sequence (3'-5') AT Name Size Alleles (oC) (bp)

AP5 (TGC)3 N6 (TGT)1 141-147 3 HEX5'-

N12 (TGA)3 F: AGATGGTGCTGGGTGAAGAC 56

R: AGTGCAAACAACACCAGCAG B JO338 (CAA)10 186-192 3 M13-

F: TCTCCTGATCCTGAGAAGC 50

R: AGGGAGCAGAGCAACACTTG cxpGT48* (TG)15 154-158 3 M13-

F: AGTGGACAACATCGTCACCA 46

R: ATCATGTCCACCCTTCTTGC AT annealing temperature * Locus redesigned in this study

4.2.2 Amplification and screening of microsatellite loci

Gradient PCR was used to determine the optimal annealing temperature for primers that failed to amplify at the reference temperature. Loci were optimized in 12.5μl

reactions containing 1.25μl of 10x Taq polymerase buffer, 1μl of 25mM MgCl2, 0.25μl of dNTPs, 0.5μl of each primer, 0.25μl of Red Taq polymerase (Astral) and 1μl of DNA template (diluted 1/20 as extracted in the methodology section in chapter 2). The PCR conditions for locus AP5 used forward primer labelled with 5'HEX. The same reaction was used for JO338B and cxpGT48 with the addition of 0.5μl of 5'- fluorescently labelled M13. The M13 tailed primer method of Boutin-Ganache et al. (2001) was used for visualization on a Gel-scan 3000 (Corbett Robotics). The forward primers were 5'-tailed with the 23-basepair M13 (uni-43) sequence (AGGGTTTTCCCAGTCACGACGTT).

77 Chapter 4______

PCR conditions for each locus were: denaturation for 5 minutes at 94oC followed by 40 cycles of 94oC for 30 seconds, at the specific annealing temperature for 30 seconds (each locus with different temperature as in Table1), 72oC for 2 minutes, an extension stage for 5 minutes at 72oC and a final hold at 4 oC.

10ml of 5% polyacrylamide (42g urea, 12ml 5x TBE buffer, 12.5ml 5% 19:1 acrylamide) mixed with 60μl of 10% ammonium persulphate (APS) and 6μl of Tetramethyl Ethylenediamine (TEMED) was prepared and polymerized for 1 hour before loading the denatured products. The microsatellite product was denatured for 5 min at 950C after mixing (1:1) with formamide dye (5 mg Bromophenol blue, 50 ml Formamide, 100 µl 0.5 M EDTA) and cooled immediately on ice to keep DNA single stranded; then 1μl of that denatured product was loaded on to the gel. Gels were run under an electric current at 1200V for 45 min; fragments were visualized on a Gel- scan 3000 DNA Analyser (Corbett Robotics), scored against TAMRATM size standards (Applied Biosystems), and analysed with ONE-SCAN software version 2.03 (Scanalytics) to automate measurement of allele length and to quantify peak heights.

4.3 Data analysis

4.3.1 Hardy Weinberg Equilibrium

The data were tested for non-random associations between alleles at each locus by testing for deviation from Hardy-Weinberg expectations (Hartl & Clark 1997). To investigate for significant deviations from Hardy-Weinberg equilibrium, exact tests were used at each locus at each site. Significance was evaluated using GENEPOP version 3.1 (Raymond & Rousset 1995).

The observed and expected frequencies under Hardy-Weinberg equilibrium were compared. FIS statistics were calculated in GENEPOP. FIS measures the degree to which observed heterozygosity matches expected heterozygosity under random mating.

78 Chapter 4______

FIS = He – Ho

Ho

FIS is equal to zero, when observed heterozygosity is equal to expected

heterozygosity. When FIS>0, there are less heterozygotes than expected under random mating. This is the situation expected when more than one species is present in the sample.

4.3.2 Linkage Disequilibrium

The data were tested in Genepop for non-random associations among loci by testing for linkage disequilibrium (Hill & Robertson 1968). The Fisher Exact test was performed to detect genotypic linkage disequilibrium between pairs of loci in each population (Raymond & Rousset 1995). Linkage disequilibrium may be expected if two species with different allele frequencies at two loci are present in the sample.

4.3.3 Assignment tests

I used the methods of Rannala & Mountain (1997) and Paetkau et al. (1995) in GENECLASS2 software version 2.0 (Piry et al. 2004) to determine the probability of individuals being assigned to the mitochondrial clades to which they belonged. In addition, assignment testing methods were used for each site separately, to examine the confidence of being correctly or wrongly assigned to that clade, by using four assignment levels with high level (> 90%) or low level (< 90%) confidence. This test was done only for eastern populations where the two clades were sympatric.

Genetic variability was estimated from the mean number of alleles per locus and the gene diversity per population and locus by calculating the expected heterozygosities under Hardy-Weinberg expectations.

79 Chapter 4______4.4 Results

All loci conformed to Hardy-Weinberg expectations, (P> 0.05). FIS values were both positive and negative (Table 4.2).

The three microsatellite loci each had only three alleles. The AP5 locus had slightly lower levels of diversity than the other two loci (JO338B and cxpGT48) (Table 4.3).

Table 4.2: Hardy Weinberg equilibrium tests (FIS-values) for mosquito populations from 13 sites at three microsatellite loci. Locations Sites AP5 Locus JO338B Locus GT48 Locus Brisbane Hem 0.097 0.027 -0.160 Ban 0.231 -0.092 -0.006 Bri -0.115 0.196 -0.058 Gold Coast Hel -0.256 0.235 0.206 Ruc 0.002 -0.020 0.197 Cairns Sma -0.070 0.061 0.173 Sydney New -0.094 0.210 0.045 Sta -0.162 0.082 -0.164 Darwin Plm -0.042 -0.019 -0.254 Onslow Ons -0.173 0.118 -0.278 Wyndham Wyn 0.350 0.102 -0.025 Willie Creek Wil -0.101 0.163 -0.256 Perth Lsp 0.153 -0.010 -0.126

80 Chapter 4______

Table 4.3: Three microsatellite loci analysed for Ae. vigilax populations in Australia.

AP5 JO338B cxpGT48

Sites N HO HE P HO HE P HO HE P Hem 30 0.28 0.31 0.230 0.63 0.65 0.187 0.77 0.66 0.099 Ban 30 0.33 0.43 0.142 0.73 0.67 0.203 0.67 0.66 0.905 Bri 30 0.23 0.21 1.000 0.50 0.62 0.340 0.70 0.66 0.477 Hel 28 0.57 0.46 0.285 0.46 0.61 0.097 0.54 0.67 0.150 Ruc 30 0.30 0.30 0.561 0.67 0.65 0.136 0.53 0.66 0.488 Sma 30 0.21 0.19 1.000 0.63 0.67 0.827 0.50 0.60 0.165 New 30 0.27 0.24 1.000 0.50 0.63 0.420 0.63 0.66 0.500 Sta 30 0.40 0.35 1.000 0.57 0.62 0.243 0.70 0.60 0.277 Plm 30 0.57 0.54 0.416 0.53 0.52 0.350 0.83 0.67 0.152 Ons 31 0.60 0.51 0.461 0.52 0.58 0.092 0.71 0.56 0.270 Wyn 29 0.35 0.53 0.089 0.59 0.65 0.247 0.62 0.61 0.823 Wil 22 0.64 0.58 0.583 0.50 0.60 0.153 0.82 0.66 0.440 Lsp 29 0.52 0.61 0.100 0.66 0.65 0.292 0.76 0.68 0.444

N: Number of individuals scored from each site. HO: Observed heterozygosity. HE: Expected heterozygosity. P: p-value of Hardy-Weinberg.

Linkage disequilibrium analysis results are shown in Table 4.4. There was no significant deviation from expectations under linkage disequilibrium between any pair of loci (P > 0.05, Fisher’s exact test), except for AP5 and JO338B at the Bri site at Brisbane, and the Wil site at Willie Creek (Table 4.4). After Bonferroni correction for multiple tests (Rice 1989) no locus pair comparisons showed significant linkage disequilibrium between loci.

81 Chapter 4______

Table 4.4: Multilocus Linkage Disequilibrium in thirteen Ae. vigilax populations

Population Locus # 1 Locus # 2 P-value Hem AP5 JO338B 0.2230 Hem AP5 cxpGT48 0.9570 Hem JO338B cxpGT48 0.9616 Ban AP5 JO338B 0.6765 Ban AP5 cxpGT48 0.2552 Ban JO338B cxpGT48 0.4588 Bri AP5 JO338B 0.0421* Bri AP5 cxpGT48 0.1159 Bri JO338B cxpGT48 0.1940 Hel AP5 JO338B 0.1483 Hel AP5 cxpGT48 0.5782 Hel JO338B cxpGT48 0.1646 Ruc AP5 JO338B 0.9166 Ruc AP5 cxpGT48 0.4283 Ruc JO338B cxpGT48 0.2294 Sma AP5 JO338B 0.7002 Sma AP5 cxpGT48 0.7858 Sma JO338B cxpGT48 0.6239 New AP5 JO338B 0.3760 New AP5 cxpGT48 0.7434 New JO338B cxpGT48 0.6108 Sta AP5 JO338B 0.7800 Sta AP5 cxpGT48 0.2261 Sta JO338B cxpGT48 0.0502 Plm AP5 JO338B 0.0462 Plm AP5 cxpGT48 0.4984 Plm JO338B cxpGT48 0.1829 Ons AP5 JO338B 0.9314 Ons AP5 cxpGT48 0.3688

82 Chapter 4______Ons JO338B cxpGT48 0.2380 Wyn AP5 JO338B 0.6573 Wyn AP5 cxpGT48 0.3923 Wyn JO338B cxpGT48 0.3272 Wil AP5 JO338B 0.0279* Wil AP5 cxpGT48 0.9752 Wil JO338B cxpGT48 0.4822 Lsp AP5 JO338B 0.6776 Lsp AP5 cxpGT48 0.4530 Lsp JO338B cxpGT48 0.7245

* P < 0.05

83 Chapter 4______Assignment tests showed that a high proportion of individuals in clade-I were wrongly assigned, with a low proportion of individuals correctly assigned. Almost all those that were correctly assigned were only assigned at < 90%. In clade-II, most individuals were correctly assigned to their clade, although only at Gold Coast (Ruc) and Cairns (Sma) was there a significant proportion correctly assigned with confidence > 90% (Figure 4.1 & Table 4.5).

Hem / clade-I Hem / clade-II Ruc / clade-I Ruc / clade-II

Sma / clade-I Sma / clade-II Ban / clade-I Ban / clade-II

New / clade-I New / clade-II Bri / clade-I Bri / clade-II

Hel / clade-I Hel / clade-II Sta / clade-I Sta / clade-II

1 Correctly Assigned to mtDNA with a high level > 90% 2 Correctly Assigned to mtDNA with a low level < 90% 3 Wrongly Assigned to mtDNA with a high level > 90% 4 Wrongly Assigned to mtDNA with a low level < 90%

Figure 4.1: Assignment test using multilocus genotype, showing the percentage of Ae. vigilax clades correctly or wrongly assigned to the mtDNA clades. Brisbane sites (Hem, Ban & Bri), Gold Coast sites (Hel & Ruc), Cairns site (Sma) and Sydney sites (New & Sta).

84 Chapter 4______

Table 4.5: Percentage of Ae. vigilax individuals correctly and wrongly assigned to their clades, using the Rannala & Mountain 1997 method with 90% level of confidence.

Number of Correctly Correctly Wrongly Wrongly individuals Assigned Assigned Assigned Assigned Location Clades > 90% < 90% > 90% < 90% Hem Clade-I 8 3 37 0 60 Clade-II 22 0 60 0 40 Ban Clade-I 8 0 37 0 63 Clade-II 22 0 63 0 37 Bri Clade-I 4 0 27 0 73 Clade-II 26 0 73 0 27 Hel Clade-I 5 0 54 0 46 Clade-II 23 0 46 0 54 Ruc Clade-I 6 3 37 0 60 Clade-II 24 20 40 0 40 Sma Clade-I 3 7 30 0 63 Clade-II 27 30 33 0 37 New Clade-I 6 0 33 0 67 Clade-II 24 3 64 0 33 Sta Clade-I 4 3 27 0 70 Clade-II 26 7 63 0 30

4.5 Discussion

From chapter-3’s analyses of mtDNA, the haplotype of variation in Ae. vigilax showed evidence of allopatric fragmentation between clade-I and clade-II with subsequent secondary contact. In this chapter, I looked into the differences in microsatellite allele frequencies between the two lineages identified using mtDNA.

85 Chapter 4______Eastern populations contained mixtures of clade-I and clade-II, whereas western and northern populations contained only clade-II. Therefore, if these two mitochondrial clades represent different biological species, I would expect to find evidence of non random mating in eastern populations only. Specifically, I expected that where the two clades co-occurred, samples from eastern populations would show significant deviations from Hardy Weinberg Equilibrium and linkage equilibrium, and that individuals would be able to be assigned correctly to mitochondrial clades on the basis of their multilocus microsatellite genotype. No deviations from Hardy Weinberg Equilibrium or linkage equilibrium would be expected in northern and western samples. This was not the case. No populations showed significant deviations from Hardy-Weinberg equilibrium and only two populations showed evidence of linkage disequilibrium, one from the east and one from west. Unfortunately, variation was very low at the only loci that gave reliable results. In addition, the assignment testing was not able to assign individuals to the correct clade with any consistency. It appeared that there was a roughly equal chance of assigning an individual to its correct clade as to the incorrect clade. Some other researches have also shown no concordance between the mtDNA and nuclear results. For example, four cryptic species of the Anopheles albitarsis complex showed monophyletic clades in parsimony, likelihood and Bayesian analyses using mtDNA (COI); whereas they revealed polyphyly or paraphyly in all phylogenetic analyses using random amplified polymorphic DNA (Lehr et al. 2005). That suggests the gene trees and species trees did not detect the same evolutionary route (Presa et al. 2002). Also, related Australian mosquitoes Culex annulirostris and Culex palpalis demonstrated incongruent results between mtDNA (COI), which showed eight divergent lineages, and nuclear acetylcholine esterase 2 which confirmed only four of them (Hemmerter et al. 2009).

Taken together, these results did not support the idea that the two clades identified in chapter 3 are reproductively isolated species. It seems more likely that they are the result of historical isolation; followed by secondary contact and that they interbreed freely where they co-occur. The low levels of variation were unfortunate, but if the two clades were reproductively isolated, some divergence in allele frequencies would be expected. A similar study on divergent clades of a small shrimp in south-east Queensland was able to assign greater than 95% of individuals to the correct

86 Chapter 4______mitochondrial clade with > 90% probability, based on only three loci (Rodriguez 2009). Therefore, I can be reasonably confident that we have a single species.

The divergence time of Ae. vigilax clades, 900,000 years ago means that the isolation time between the two clades was insufficient to result in speciation. Compared to other insects, such as the Galapaganus weevils, divergence occurred about 7.2 million years ago (mya) (Sequeira et al. 2000). In Drusus (Trichoptera) divergence was between 7.1-2.5 mya (Previsic et al. 2009). In this study, the two clades may not have been separated for long enough for new species to arise. In contrast, the divergence time between other closely related mosquito species, for example An. gambiae and An. funestus lineages, which belong to the same subgenus, was estimated to be 5 mya (Sharakhov et al. 2002). The divergence time of the black saltmarsh mosquito (Aedes taeniorhynchus) was 200,000 years ago to separate the Galapagos Archipelago and countries on the American continents (Bataille et al. 2009). The divergence of Anopheles mosquitoes in the Oriental region was early to mid Pliocene (3.2-4.5 mya) within the Annularis Group, more recently during the late Pliocene between (2.6 and 3.2 mya) and within the Maculatus Group, slightly earlier, during the mid to late Pliocene (3.4 ± 0.5 mya) within the Jamesii Group. The members of the Annularis Group (within An. annularis) also clustered into two divergent lineages 1.8 mya, and each lineage diverged into two clades indicating allopatric fragmentation occurred 1.12 mya (Morgan et al. 2009). So, in summary, most species that have been identified as being reproductively isolated from their sister species have been separated for greater than the 900,000 years identified for Ae. vigilax.

4.6 Conclusion

The data presented in this chapter indicated that there are no significant deviations from Hardy-Weinberg equilibrium or from linkage disequilibrium expectations where the two mitochondrial clades co-occur, which suggests that Ae. vigilax belongs to one species. This was confirmed by the assignment tests, which appeared to have low success at correctly allocating individuals to their mitochondrial clade. However, it should be acknowledged that the low levels of diversity and the small number of loci would greatly reduce the discrimination power.

87 Chapter 5 ______

Chapter 5

Microsatellite variation at the continental scale in Ae. vigilax

5.1 Introduction

The genetic structure of natural populations is dependent on the interaction between the biology of species, their environment and evolutionary processes, such as mutation, natural selection, genetic drift and gene flow (Slatkin 1987). The life history strategies of an organism will determine its ability to disperse through the environment. Dispersal is essential for the maintenance of gene flow and genetic diversity within a species. On the other hand, species with low dispersal abilities will exhibit genetic structure between subpopulations. Generally, in the absence of selection, limited gene flow will lead to genetic differentiation among subpopulations due to genetic drift (Nei 1975; Slatkin 1987).

Knowledge of Ae. vigilax’s population genetic structure is important to an understanding of RRV epidemiology and essential for the success of potential genetic control strategies, which will depend on the ability to target all populations and will require a comprehensive understanding of the variants of the species that produce and maintain the population structure, especially gene flow.

This chapter focuses on the study of the genetic structure of Ae. vigilax using nuclear microsatellites to determine whether the populations show genetic structure or whether this species has broad dispersal capabilities, as suggested by its wide distribution around the Australian environment over large and diverse areas (Russell 1993).

A number of studies have examined genetic diversity within and between mosquito species using nuclear gene markers. Microsatellite loci are isolated and identified from multiple genomic libraries, then used to assess the amount of variation and contemporary patterns of gene flow of different species of mosquito populations at small and/or large geographic scales. This provides a tool for evaluating the population genetic variability of different mosquito vectors and may

88 Chapter 5 ______contribute to their control. For example, this approach has been used for the dengue vector Ae. aegypti in Mexico (Ravel et al. 2001), in Vietnam (Huber et al. 2002), in Brazil (Costa-Ribeiro et al. 2006), in Australia and Vietnam (Endersby et al. 2009); West Nile Virus vector Cx. tarsalis in Los Angeles (Rasgon et al. 2006); yellow fever vector Ae. aegypti in Trinidad (near Spain) and Oropouche (near San Fernando) (Chambers et al. 2007); and the major vectors of malaria An. arabiensis in Ethiopia and Eritrea, An. marajoara in Amazonia and Brazil, and An. darlingi in central and south America (Nyanjom et al. 2003; Li et al. 2005; Mirabello et al. 2008) respectively. Chapman et al. (1999) also used other nuclear gene markers. They assessed six allozyme loci of the Ross River Virus vector Ae. vigilax in Australia, although at a very small scale, only in south-east Queensland. In that study some differences were found between larval samples in the region, but this was thought to be due to natural selection on these loci as it was not consistently observed across the six loci investigated, and analysis of adult samples suggested widespread gene flow and that a distance of around 9 km across the ocean did not constitute a significant barrier to dispersal. Other than this small scale study based on allozymes, no information exists on dispersal of Ae. vigilax at broader spatial scales.

The present study adds to the knowledge of genetic diversity in Ae. vigilax using microsatellite markers. In particular, the aim of this chapter is to investigate genetic diversity, and to assess the extent of divergence between the populations. From the analysis of mtDNA COI variation (chapter 3), it appeared that gene flow between east and west may be limited, as one clade occurred only in the east. However, mtDNA, because it is maternally inherited, reflects only female dispersal patterns. Therefore, it is possible that east-west dispersal is limited in females but may be more widespread in males (Melnick & Hoelzer 1992; Favre et al. 1997; de Meeûs et al. 2002).

However, if dispersal has no sex bias, then I expected to find evidence of some restriction to gene flow between east and west, as well as some evidence of limited gene flow within each region. As the analysis from chapter 4 suggested that the two mitochondrial clades were not separate species, all individuals were analysed together in this chapter. The same three microsatellite loci were analysed as in chapter 4.

89 Chapter 5 ______5.2 Methodology As in chapter 4

5.3 Data analysis

Hierarchical partitioning of molecular variance was used to quantify the levels of population subdivision. ARLEQUIN version 3.11 (Excoffier et al. 2005) was used to test the hierarchical groups for the three microsatellite loci. The levels of partitioning investigated were among groups (between east and west), among populations within groups, and within populations.

F-statistics indicates the degree of population differentiation as a result of the combined effect of genetic drift and gene flow. It is based on allele frequency distributions, and measures the proportion of variance in allele frequencies among subpopulations (Wright 1951). FST is affected by effective population size and the migration rate. Population pairwise differences between all populations generated in ARLEQUIN (Excoffier et al. 2005) were calculated to examine population subdivision of observed allelic variation.

The relationship between the genetic distance and geographic distance was tested for significance using a Mantel test (Mantel 1967); Slatkin’s linearized FST was used as [t/M=FST/(1-FST) (M=2N for diploid data) (Slatkin 1995)] and was performed in ARLEQUIN version 3.11. Geographic distances via coastline and across the continent were analysed separately.

5.4 Results

As presented in chapter 4, there was no significant deviation from Hardy-Weinberg equilibrium, and all pairwise tests of linkage disequilibrium between loci were non-significant (data presented in chapter 4), after Bonferroni correction.

5.4.1 Allele frequencies

There were only three alleles at each of the three loci and all alleles occurred at almost all sites Figure 5.1 a, b & c. AP5 locus had three alleles (1, 2 & 3) with allele sizes 141, 144 & 147 respectively (Figure 5.1a); JO338 locus had three alleles and their allele sizes were 186, 189 & 192

90 Chapter 5 ______(Figure 5.1b); and cxpGT48 locus also had three alleles with allele sizes 154, 156 & 158 (Figure 5.1c). There were slight differences at the AP5 locus, in allele frequency between eastern and western populations. Allele “1” was most common in eastern locations and allele “2” was most common in western locations at the AP5 locus. All three loci had the same allele most common in all the locations except for the AP5 locus at (bri) Brisbane, where there were only two alleles. Alleles “2 & 3” at AP5 locus were at frequencies <0.1 in some populations, mostly in the eastern region (bold values in Table 5.1). At the JO338B locus, two alleles “1 & 3” were rare at Onslow and Darwin (0.097 & 0.067) respectively. The cxpGT48 locus only had an allele at low frequencies (0.081) in the Onslow population.

Figure 5.1a: Pie charts representing allele frequencies at the AP5 locus for Ae. vigilax at 13 sites.

91 Chapter 5 ______

Figure 5.1b: Pie charts representing allele frequencies at the JO338 locus for Ae. vigilax at 13 sites.

Figure 5.1c: Pie charts representing allele frequencies at the GT48 locus for Ae. vigilax at 13 sites.

92 Chapter 5 ______Table 5.1: Microsatellite allele frequencies for each locus at 13 locations. (Bold values: allele at low frequencies <0.1)

Geographic Locus Ap5 JO338 GT48

Locations Allele N 141 144 147 HO 186 189 192 HO 153 156 159 HO Brisbane Hem 30 0.828 0.103 0.069 0.28 0.450 0.217 0.333 0.63 0.383 0.383 0.233 0.77 Ban 30 0.733 0.100 0.167 0.33 0.333 0.283 0.383 0.73 0.383 0.383 0.233 0.67 Bri 30 0.883 0.000 0.117 0.23 0.517 0.300 0.183 0.50 0.400 0.367 0.233 0.70 Gold Coast Hel 28 0.714 0.143 0.143 0.57 0.554 0.214 0.232 0.46 0.268 0.375 0.357 0.54 Ruc 30 0.833 0.074 0.093 0.30 0.350 0.217 0.433 0.67 0.433 0.267 0.300 0.53 Cairns Sma 30 0.897 0.069 0.034 0.21 0.367 0.350 0.283 0.63 0.317 0.533 0.150 0.50 Sydney New 30 0.867 0.067 0.067 0.27 0.300 0.500 0.200 0.50 0.383 0.383 0.233 0.63 Sta 30 0.800 0.083 0.117 0.40 0.417 0.450 0.133 0.57 0.517 0.350 0.133 0.70 Darwin Plm 30 0.617 0.267 0.117 0.57 0.317 0.617 0.067 0.53 0.300 0.417 0.283 0.83 W.A Ons 31 0.633 0.300 0.067 0.60 0.097 0.387 0.516 0.52 0.565 0.355 0.081 0.71 Wyn 29 0.569 0.397 0.034 0.35 0.466 0.276 0.259 0.59 0.552 0.224 0.224 0.62 Wil 22 0.591 0.227 0.182 0.64 0.182 0.568 0.250 0.50 0.455 0.318 0.227 0.82 Perth lsp 29 0.448 0.431 0.121 0.52 0.207 0.448 0.345 0.66 0.310 0.379 0.310 0.76

N: Sample size. Numbers (141-192): Allele sizes.

HO: Observed heterozygosity.

5.4.2 Hierarchical analysis

The Analysis of Molecular Variance (AMOVA) was performed on a total of 379 Ae. vigilax individuals (Table 5.2). The AMOVA showed high levels of genetic structure at all levels of the

hierarchy (p< 0.001). The FCT value was highly significant indicating differentiation between the groups (east and west) even though it represented only 3.9% of the total variation. Differentiation

among populations within regions (Fsc) was also highly significant.

93 Chapter 5 ______

Table 5.2: AMOVA results based on three microsatellite loci.

Source of variation d.f Percentage F-Statistics P-value of variation

Between groups 1 3.85% FCT: 0.03850 0.00000 (East & West)

Among 11 2.66% FSC: 0.02765 0.00000 populations within groups

Within populations 366 93.49% FST: 0.06508 0.00000

The overall FST value was highly significant at all three loci. However, the FCT value was only B significant at the AP5 locus. At the JO338 and cxpGT48 loci, the FSC value was significant, but the

FCT value was not (Table 5.3).

Table 5.3: Overall FST values for each microsatellite locus among Ae. vigilax samples. Significant values in bold (*** P<0.001).

Among Groups Among population Within Populations

Locus FCT %variation FSC %variation FST %variation

AP5 Locus 0.11804*** 11.80387 0.00979 0.86381 0.12668*** 87.33231

JO338 locus 0.02101 2.10144 0.05090*** 4.98278 0.07084*** 92.91579

GT48 locus -0.00219 -0.21871 0.01576*** 1.57897 0.01360*** 98.63974

94 Chapter 5 ______

5.4.3 Pairwise FST comparisons

Pairwise differences between populations using F-statistics showed highly significant FST values for 39 of 40 comparisons between eastern and western sites. 32 of these comparisons were highly significant (p<0.001) (Table 5.4). In contrast, eight of 15 pairwise comparisons between western sites were significant and only 11 of 36 between eastern sites were significant. The largest FST value

(0.125) was between Onslow and Brisbane, with many of the very low FST values between pairs of sites in the same region; e.g. within the Sydney region and within the Brisbane region. Only one site

(Ruc) in Gold Coast had significant FST values compared to other sites in the same region, within the east.

95 Chapter 5 ______

Table 5.4: Pairwise FST values based on three microsatellite loci among Ae. vigilax sites. Significant values in bold. Squares indicate comparisons between sites in the same region. P<0.05, ** P<0.03, *** P<0.001

New Sta Hel Ruc Hem Ban Bri Sma Plm Wyn Wil Ons Lsp New 0 Sta 0.00160 0 Hel 0.04510** 0.04397*** 0 Ruc 0.03733** 0.04327*** 0.02389* 0 Hem 0.02256* 0.02257* 0.00212 -0.00230 0 Ban 0.01651 0.02282** 0.01329 -0.00353 -0.00618 0 Bri 0.01449 0.00710 0.01170 0.02777* 0.00077 0.01664 0 Sma 0.00617 0.02223 0.03342* 0.03376** 0.00458 0.00975 0.01284 0 Plm 0.02754 0.03744*** 0.05946*** 0.09084*** 0.07305*** 0.05599*** 0.07386*** 0.06014*** 0 Wyn 0.07710*** 0.04979*** 0.04576*** 0.04429*** 0.04123** 0.04288*** 0.07356*** 0.08702*** 0.06519*** 0 Wil 0.02356* 0.02870* 0.07077** 0.05192** 0.05910*** 0.02640** 0.07654*** 0.06083*** 0.00766 0.04325** 0 Ons 0.07818*** 0.08089*** 0.11984*** 0.05278*** 0.07217*** 0.04083*** 0.12498*** 0.08263*** 0.09673*** 0.05708*** 0.02711* 0 Lsp 0.07864*** 0.09087*** 0.07124*** 0.07159*** 0.07723*** 0.04795** 0.12212*** 0.09130*** 0.02998** 0.03952** 0.01192 0.04093*** 0

(New & Sta: Sydney; Hel & Ruc: Gold Coast; Hem, Ban & Bri: Brisbane; Sma: Cairns; Plm: Darwin; Wyn: Wyndham; Wil: Willie Creek; Ons: Onslow; Lsp: Perth).

96 Chapter 5 ______5.4.4 Mantel tests

The Mantel test indicated that there was a significant relationship between geographic distance and genetic distance when the three loci were analysed together (r2=0.69 p<0.001) both for across continent or via coastline distances (Figure 5.2). When the three loci were analysed separately, two of them (AP5 and JO338B) showed a significant correlation (r2=0.58 p<0.01 and r2=0.35 p<0.03 respectively); but locus cxpGT48 did not show a significant correlation (r2=0.13 p=0.14).

Mantel test

0.16 0.14 0.12 0.1 0.08 0.06 0.04

Genetic Distances 0.02 0 0 1000 2000 3000 4000 5000 Geographic Distance (km 2)

Figure 5.2: Scatter diagram of geographic distance and genetic distances between pairs of eastern and western populations for three loci. (The points are denoting pairwise between sites).

5.5 Discussion

The results presented in this chapter support the hypothesis that Ae. vigilax shows genetic structure among populations across continental Australia. This is evident from the AMOVA’s, which showed significant structure at all spatial scales examined between east and west and among sites within regions.

Although the loci used in this study were only slightly polymorphic in Ae. vigilax, significant genetic differentiation has still been identified between the samples from

97 Chapter 5 ______different regions in Australia. The hierarchical pattern of genetic differentiation, observed between east and west groups suggested limited exchange of individuals between these regions. Overall, the AMOVA suggested limited gene flow between east and west and between samples within regions. Also, the significant isolation by distance effect supports the idea that these populations have reached equilibrium between genetic drift and gene flow. In the absence of selection, limited gene flow will lead to genetic differentiation among populations as a result of genetic drift (Crow & Aoki 1984; Chenoweth et al. 1998b). This may suggest that dispersal of individuals is limited among populations. The populations from different regions should be genetically more differentiated than populations within the same region (Wright 1978).

However, it should be acknowledged that the three loci show slightly different patterns. Only the locus (AP5) showed significant differences between east and west whereas the other two loci indicated significant differentiation among samples within regions. The fact that the three loci show slightly different patterns in the hierarchical analysis makes it difficult to make firm conclusions about the overall structure. The differences between the loci could be due to chance or, alternatively, it is possible that selection could be affecting one or more loci. Although it is usually assumed that data from microsatellite loci represent only the effects of mutation, migration and drift (i.e. are neutral), this is not always true. Some microsatellites occur within coding regions (Metzgar et al. 1998; Chang et al. 2001; Scott et al. 2003), and this is more likely for trimers where differences in number of repeats may not affect codons downstream (because they will just differ by multiples of them). The fact that AP5 is a trimer and a complex one, suggests that it could represent a coding region. Therefore, I can not discount the possibility that selection, rather than limited gene flow explains the significant east and west differentiation.

Mantel tests also suggested different results, being significant just for two loci. Clearly more loci are needed to distinguish between possible explanations for the patterns observed. Unfortunately, despite screening 47 sets of primers from the literature, it was not possible to obtain more useful loci within the timeframe of this study.

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5.6 Conclusion

There was significant, although low levels of genetic differentiation at the continental scale, with evidence of limited gene flow within each region. This suggests that the species is not panmictic at the continental scale, but levels of dispersal are probably quite high at the regional level. Before firm conclusions can be reached, more loci, with high levels of polymorphism are required.

99 Chapter 6 ______

Chapter 6

General Discussion

The distribution of genetic variation in populations is not only a reflection of contemporary processes such as ecological and intraspecific factors, but is also created by historical events such as expansion and vicariance (Templeton et al. 1995; Hedrick 2005). The aim in phylogeographic evaluation is to use gene trees to deduce the historical and contemporary forces that have produced the current genealogical architecture of populations and closely related species (Avise 2008). The temporal information provided by mtDNA analyses can assist in determining the extent to which the population structure results from recurrent processes, such as persistent gene flow, against historical processes, such as range expansion (Avise 2000). Many methods exist for making direct comparisons between the phylogenetic structures of independent taxa.

6.1 Phylogeographic pattern and evolutionary history

Overall, studies of phylogeographic variation have suggested that climate fluctuations during the Pleistocene had a significant effect on contemporary distributions of many species (Cracraft 1982; Lee & Edwards 2008). There were arid to pluvial oscillations, and aridity rather than pluviality correlates with glaciations (Bowler 1982). Because a concentric zonation of climatic regions pervaded the Australian continent from about 2.5 million years before present (BP), the amplitude of successive climatic cycles has progressively increased and the last glacio-aridity of 25 000 to 15 000 y BP was the severest (Bowler 1982). At the peak of this aridity, which occurred towards the end of the last glaciation, increased continentality brought about by lower sea-levels was characterised by simultaneous activity of longitudinal, parabolic and lunette dunes, and widespread dessication of lakes in the present semi-arid zone as well as in the arid zone (Figure 6.1) (Ford 1987).

100 Chapter 6 ______

The results of the present study showed there were two divergent clades suggesting that the species has been separated geographically at some time in the past. Clade-I is only found in eastern Australia and clade-II is found throughout the whole sampling area of Northern Territory, Western and eastern Australia.

This isolation could be explained by populations diverging during the Pleistocene about one million years ago while being isolated in different regions (on opposite sides of Australia), resulting in clade-II in the west and clade-I in the east. Subsequently, more recently, clade-I expanded but only along the east coast (QLD & NSW); while clade-II expanded throughout Australia (QLD, NSW, NT and WA) from the north-west to the south-west and from west to east; and established secondary contact with clade-I along the east coast due to both clades occurring sympatrically in the eastern region (Figure 6.2, 6.3).

The data and findings here support that hypothesis, as the Nested Clade Analysis suggested evidence for allopatric fragmentation at the total cladogram level. Similarly, research on different Australian avifauna populations supports the hypothesis. For example, Cracraft’s (1982) study of the concordance between phenotypic and genotypic characteristics of the genus Poephila, and the findings of Toon et al. (2007) that also showed genetic variability of mtDNA haplotypes and microsatellite loci of Gymnorhina tibicen. These studies confirmed divergence between east and west Australian populations of these species.

Range expansion in clade-II was indicated for the western region in the Nested Clade Analysis. There was evidence for population growth in north-western and south- western populations indicating movement from high diversity in the north, the proposed ancestral area, toward low diversity in the south. Certainly, the Perth site has the least diversity. Moreover, in clade-II, the populations of eastern Australia have less diversity than the populations of Western Australia. The range of expansion in clade-I populations indicates the movement from the south-east, which has more

diversity, to the north-east, supporting my suggestion.

101 Chapter 6 ______

Figure 6.1: Hydrologic zonation of Australia at present and at the glacial maximum using the climatic index (Ford 1987).

102 Chapter 6 ______

Carpentarian Barrier

Clade-II Western Australia

Murchison Clade-I Barrier eastern Australia

Figure 6.2: Hypothesis of population divergence and isolation in the Australian Ae. vigilax.

Clade-II Western & Northern Clades-I & II Australia Sympatrically in eastern Australia

Figure 6.3: Scenario of range expansion of Ae. vigilax in both clades with secondary contact by clade- II individuals by expanding their range from west to east.

103 Chapter 6 ______Recent research has suggested that there are several barriers to gene flow across the Australian continent due to its extreme aridity. One of the barriers is the Carpentarian barrier in northern Australia (Figure 1.4). This barrier is generally assumed to have arisen as a result of the fluctuations in sea level over time; as well as because of extreme climate and habitat change across this area during the last glacial period (Smart 1977; Cracraft 1986; Ford & Blair 2005). The Carpentarian barrier is roughly 150 km wide depending on the taxon being considered and is a semiarid region extremely poor in vegetation (Lee & Edwards 2008). The barrier affects the dispersal of many organisms (vertebrates and invertebrates) and has been implicated in reduction in exchange of individuals between populations between eastern and western Australia (Jennings & Edwards 2005), leading to an increase of differentiation through random drift (Avise 2004).

The Carpentarian barrier could have been an important Pleistocene barrier to dispersal for some mosquito species across the north coastal region between north-east and north-west Australia. In this study, I used population genetic analyses to investigate the effect of the Carpentarian barrier, which would have been an arid one, during the Pleistocene on contemporary distributions of the widespread Ae. vigilax species. Generally mitochondrial DNA suggests that increasing aridity and the Pleistocene barrier played an important role in the divergence of Ae. vigilax into two clades about one million years ago (see chapter 3). The study showed that eastern and western mtDNA clades are monophyletic, that divergence was a result of isolation. Nevertheless, the dispersal ability and the habitat change may have assisted the expansion of this species across the continent as indicated in the Nested Clade Analysis (Bowler 1982; Templeton et al. 1995).

The mtDNA data suggest that the populations were subdivided into two distinct clades with low gene flow for long enough to develop private lineages, but not long enough for speciation to occur. The nuclear results in chapter 4 support this suggestion. The assignment test showed that the individuals were not able to be assigned to the correct mitochondrial clades with any consistency. The populations also have not revealed significant deviations from Hardy Weinberg Equilibrium or linkage equilibrium, which would be expected if the two lineages were not interbreeding. Therefore, the data presented from the nuclear markers supported the

104 Chapter 6 ______idea that there is no evidence for Australian Ae. vigilax being reproductively isolated or having cryptic species. This suggests that the movement of Ae. vigilax from west to east occurred recently, which also supports our conclusions about recent expansion of this species in Australia. That might indicate there has not been sufficient time (13 thousand years ago) for lineage sorting to occur and populations to reach equilibrium with populations appearing similar genetically while being isolated demographically (Crow & Aoki 1984).

However, low gene flow from the south-western population (Perth site) compared to other western sites for mtDNA data, suggests that climatic-ecological change may have occurred, including aridification during the Pleistocene that affected species dispersal. For example when the continent was colder and drier, south-western Australia may not have been a suitable habitat for Ae. vigilax (Bowler 1982; Cracraft 1986; Byrne 2008). That suggests the Perth population expanded there when the climate was wetter during climate fluctuations or natural cycles (Graham & Grimm 1990; Cameron & Scheel 2001). Recently, the Murchison arid barrier across the south-west located between Perth and Onslow locations (Ford 1987; Toon et al. 2007) could have been an important barrier (when the climate dried out) to the dispersal of Ae. vigila.

6.2 AMOVA, expansion and gene flow as evidence for contemporary structure in Australian Ae. vigilax species

AMOVA was used to compare the population structure of Ae. vigilax across the Australian continent. The analysis indicated a contemporary structure at different geographic scales. Overall mtDNA showed significant levels of population structuring subdividing populations into eastern and western regions (FCT=0.066, P<0.05). Additionally, the mitochondrial network demonstrated that the presence of distinct evolutionary lineages in the western and eastern regions. The analysis of microsatellite data also supported the geographic structure between east and west

(FCT=0.039, P<0.001). This indicates limited gene flow between regions, which was also supported by significant isolation by distance.

105 Chapter 6 ______In both clades evidence of population expansion was corroborated by significant Fu’s Fs and Tajima’s D. The populations were mostly significant for Fu’s Fs and non- significant for Tajima’s D indicating that population growth by expansion rather than selection is the likely factor (Peck & Congdon 2004). Conversely, on a local scale the AMOVA results were significant within regions for both mtDNA and microsatellite data, suggesting that structuring was present at least within one region. That was indicated also for mtDNA by NCA when clade 3-2 showed restricted gene flow with isolation by distance within clade-II populations, and clade 3-6 also showed restricted gene flow within clade-I populations. Pairwise analyses of genetic differentiation among regions also revealed restricted gene flow with mtDNA and microsatellite markers showing significant results.

More variation was observed in mtDNA in clade-II populations than in clade-I populations, which may indicate that the western populations are more ancestral. Recent contiguous range expansion for Australian Ae. vigilax throughout the clade-II (clade 4-1) was inferred in the western region. In contrast, range expansion was not inferred from NCA analysis for clade-I in the eastern region. That suggests the clade-I populations have expanded more recently than Clade-II. The possible explanation for the observed pattern is that the species has had insufficient time to achieve equilibrium between gene flow and genetic drift. That explanation is supported by mismatch distribution results, which showed the expansion time of clade-I was 11 000 years ago, which is more recent than the expansion time of clade-II, estimated at 13 000 years ago. This suggests that mosquito populations have expanded since the last glacial maximum (~ 20,000 years ago), when the climate was much cooler (Bowler 1982; Otto-Bliesner et al. 2006). These conditions were probably not very favourable for Ae. vigilax and could have resulted in a limited amount of suitable habitat.

Furthermore, when the two clades were analysed separately, the results were different. Neither of the two clades conformed to the expectations at the large scale, because the degree of genetic differentiation between populations within regions was higher than between regions. In concordance with these findings, from the Mantel tests (mtDNA data), there seems to be no real evidence for significant correlation between genetic distance and geographic distances within each clade, suggesting that Ae. vigilax

106 Chapter 6 ______populations in clade-I have not yet reached equilibrium, or the individual dispersal distance is greater than the range of the study area.

6.3 Further Research

Unfortunately, the loci that gave variable results in this study were few in number and levels of diversity were also low. Therefore, in further research it would be important to isolate more polymorphic loci to provide more discriminating results. Further analysis of nuclear markers is required to confirm that gene flow occurs between the divergent lineages of Ae. vigilax. As mtDNA has a smaller effective population size than nuclear DNA, such as microsatellites markers, due to its maternal inheritance and haploid structure (Birky-Jr et al. 1989), the small effective population size leads to a greater effect of genetic drift. As mtDNA is inherited maternally, it may not show genealogical patterns that represent the whole population history, especially when there is sex biased dispersal, with males dispersing more than females (Hare 2001). Also, mtDNA assumes lack of recombination, although recently mitochondrial recombination has become increasingly recognised (Posada et al. 2002; Tsaousis et al. 2005). The mtDNA is more sensitive than nuclear markers in detecting limited gene flow and usual estimates of gene flow will reveal that mtDNA population subdivision. However, the range of breeding and migrating is greater for nuclear genes due to gene diversity and population subdivision is higher for nuclear than mtDNA.

Although recently phylogeographical analysis of nDNA polymorphisms has sometimes corroborated evidence from mtDNA data, nDNA has still slower coalescence times than mtDNA. Populations or species that diverged in the Pleistocene are likely to be in a ‘mixed-monophyly’ zone of divergence (Zink & Barrowclough 2008). The mtDNA has a higher probability of monophyly, but the average nuclear locus will still be polyphyletic or paraphyletic. It is expected to take about three times longer for nDNA to become monophyletic than mtDNA (Hoelzer 1997; Hare 2001). Currently mtDNA is still regarded as a very valuable marker in phylogeographical studies (Zink & Barrowclough 2008), but combination of nDNA and mtDNA will come more important in further studies (Hare 2001).

107 Chapter 6 ______

6.4 Implications for vector control

In this study, mtDNA and microsatellites were used to investigate genetic variation among 13 population locations of Ae. vigilax from coastal regions of Australia, to detect the patterns of gene flow among different geographic regions of the species range and the spatial distribution of haplotypes and alleles associated with the effect of dispersal and evolution. The study indicated that the population of Ae. vigilax is moderately subdivided (between and within regions), suggesting that the dispersal abilities of the species play an important role in determining levels of gene flow between subpopulations. It is very important to understand dispersal to formulate disease control as dispersal ability is expected to be a key factor determining vector and virus transmission (Tabachnick & Black 1995). From an ecological perspective (Russell 1993), and from genetic data from this study and Chapman (1999), Ae. vigilax has the ability to disperse for many kilometres. Therefore, when adults disperse over large distances, genes resistant to pesticides can spread rapidly through the population (McCarroll et al. 2000). Similarly, pesticide spraying of a local area will have only short-term effects because adults will quickly move in from elsewhere (Chevillon et al. 1999). If restricted dispersal or specific barriers are identified, then we can better understand the dispersal of mosquito vector and virus transmission.

However, molecular study and the determination of the genetic structure and gene flow among populations of this vector may assist the development of RRV control strategies by identification, detection and monitoring of insecticide susceptibility, and resistance (Sakallah 2000; Remme et al. 2002; Hemingway et al. 2004). This study is important for risk management programs by encouraging further research to disseminate information about vectors and their dispersal, to know where and when mosquitocides can be sprayed, as pesticide resistance is still a big issue for vector control programs. Also, it is potential to identify differences in vector competence of different lineages. This would help priorities control, especially if the lineages were spatially segregated.

108 References

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125

Appendices

Appendix 1: Ae. vigilax sampling sites and the geographic locations for all sequences used in chapter 3.

Locations Sites Latitude Longitude Sample size Newington 33º 53' 01 150º 55' 47 28 Sydney (NEW) Salt Ash "Port 33º 59' 27 151º 06' 28 22 Stephens Shire" (STA) Gold Coast Rusty Cars (RUC) 27º 53' 12 153º 16' 37 22 Helensvale Rd. 27º 53' 17 153º 20' 44 22 (HEL) Brisbane Hemmant (HEM) 27º 26' 57 153º 07' 36 29 Banyo (BAN) 27º 30' 01 153º 04' 59 30 Brighton (BRI) 27º 25' 38 152º 59' 05 30 Smith Field 16º 55' 57 145º 43' 53 28 Cairns (SMA) Palm Creek 12º 26' 10 130º 47' 41 16 Darwin (PLM) Wyndham Wyndham (WYN) 15º 25' 09 128º 10' 09 26 Willie Willie Creek 17º 44' 43 122º 13' 51 16 Creek (WIL) Onslow Onslow (ONS) 21º 38' 10 115º 06' 44 30 Leschenault Peninsula 31º 58' 55 115º 46' 18 20 Perth "Belvidere" (LSP)

126 Appendix 2: Variable nucleotide sites among Ae. vigilax haplotypes for 433 bp of mitochondrial DNA COI gene from chapter 3. Haplotypes compared to Ban-3. Synonymous sites denoted by a dot and variants sites as per type of nucleotide substitution: A, T, C and G. The locations name: New, Sta (Sydney); Ruc, Hel (Gold Coast); Ban, Bri, Hem (Brisbane); Sma (Cairns); Plm (Darwin); Wyn (Wyndham);

Wil (Willie Creek); Ons (Onslow) and Lsp (Perth).

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