The same groups of 20 males as used for wing shape analysis were also examined for variation in aedeagus length. Total number of specimens depended on host fruit (Chapter 3) or location (Chapters 4 & 5). The complete abdomen of each individual was removed and placed in 5 ml of 10% potassium hydroxide (KOH) solution and left overnight at room temperature; this process softened and cleared the structures for ease of dissection. Following softening, abdomens were dissected under water. Each aedeagus was removed from the remaining genitalic structures and straightened on a microscope slide. Aedeagi were measured from the base of the aedeagus to the base of the distiphallus according to Iwaizumi et al. (1997) (Figure 2.6). Measurements were made by eye-piece micrometer and converted into millimeters. Measurements were taken to the nearest 0.01 mm.
Chapter Two 56 2.3.2.2 Data (aedeagus) analysis ANOVA with a post hoc Tukey test (where appropriate) was used to assess for significant differences among sites using SPSS. As for wing measurements, all data were verified for conformity with assumptions of normality and homogeneity of variance before being analysed. Again, if assumptions were not met, the data were appropriately log-transformed. If the assumptions still were not met, equivalent non-parametric tests; Kruskal-wallis to compare all locations or Mann-Whitney to compare between two groups (e.g., Upper and lower Isthmus of Kra barrier, the West pacific and South-east Asia) were undertaken.
2.3.2.3 Isolation- By -Distance for aedeagus length
Isolation-by-Distance was assessed based on linear regression analysis of geographic (km) distance against aedeagus length for flies collected throughout sampled locations using the program SPSS. Figure2.6 Dissected aedeagus of Zeugodacus cucurbitae representing measurement taken between base of the aedeagus (on far right) and base of the distiphallus (on left). 0.1 mm Distiphallus
Chapter Two 57 2.4 MOLECULAR PROCEDURES AND ANALYSES 2.4.1 Mitochondrial DNA procedure and analyses
2.4.1.1 Mitochondrial DNA extraction, polymerase chain reaction (PCR) amplification sequencing and Sanger sequencing
The same groups of 20 males as used for morphometric analysis were also examined in Mitochondrial DNA. Three legs (fore, mid and hind) were removed from each individual for genomic DNA extraction using the ISOLATE II Genomic DNA Kit (Bioline, Australia) following the manufacturer’s protocol. A 650 bp fragment of mitochondrial cytochrome c oxidase subunit I (cox1) was amplified using the universal invertebrate cox1 primers;
LCO1490 (forward: GGTCAACAAATCATAAAGATATTGG) and
HCO2198 (reverse: TAAACTTCAGGGTGACCAAAAAATCA) (Folmer et al., 1994; Wilson, 2012).
PCR amplification was carried out with 2 µl of template DNA, 0.5 µl of each primer (10 pmol/µl) (Integrated DNA Technologies (IDT), USA), 2 µl of 5X polymerase buffer (Bioline), 3.0 µl of 25 mM MgCl2 (Bioline), 0.1 µl of 5 U/µl MyTaq HS Red DNA Taq polymerase (Bioline, Australia), and made up to a final volume of 25 µl with distilled deionized water (ddH2O) Amplifications were performed in an Eppendorf Mastercycler® Pro S thermal cycler with an initial denaturing step at 94˚C for 3 minutes followed by 28 cycles at 94˚C for 30 seconds, 50˚C for 30 seconds, 72˚C for 30 seconds, and a final elongation step at 72˚C for 5 minutes. One µl of PCR product was separated in 1.5% (w/v) agarose gel using TBE buffer (40 mM Tris- acetate, 1 mM EDTA) to confirm the quality of PCR product.
Total PCR product was purified using the commercial ISOLATE PCR and Gel Kit (Bioline) following the manufacturer’s guidelines. Purified PCR product was amplified in a sequencing reaction containing 1.0 μl of PCR product, 1.0 μl of forward primer (3.2pmol/μl), 0.5 μl of version 3.1 ABI Prism® Big Dye Terminators (Applied Biosystems, California, USA), 3.5 μL of 5x sequencing dilution buffer (400mM Tris pH9, 10mM MgCl2), adjusted to a total reaction volume of 20μL with ddH2O. The sequencing cycle protocol involved initial denaturing at 96°C for 5
Chapter Two 58 minutes, followed by 30 cycles of 96°C for 10 seconds, 50°C for 5 seconds, 60°C for 4 minutes, before a final hold at 15°C for 10 minutes.
Sequencing fragments were cleaned using a standard ethanol precipitation protocol prior to sequencing at the Molecular Genetics Research Facility of the Faculty of Science and Technology (MGRF), QUT. All sequences will be deposited in GenBank (Accession Numbers will be supplied upon acceptance of manuscripts currently in preparation).
2.4.1.2 Mitochondrial DNA analyses
- Mitochondrial DNA Gene diversity
Sequences were aligned by eye and checked for internal stop codons and double peaks (indicative of pseudogenes) using BioEdit Sequence Alignment Editor Version 7.2.5 (Hall, 1999). Analysis of genetic diversity (gene diversity, equivalent to expected heterozygosity; Ɵπ, a diversity estimate based on the mean number of pairwise differences among populations; number of haplotypes; Tajima’s D; and Fu’s FS) were performed using MEGA version 4.0 (Tamura et al., 2007) and ARLEQUIN Version 3.5.1.2 (Excoffier et al., 2005). Tajima’s D (Tajima, 1993) and Fu’s
FS (Fu, 1997) were used to detect deviations in gene diversity from what would be expected under neutrality and to search for demographic changes or the effects of selection on gene diversity (Fu & Li, 1993). Both tests included coalescent simulations in DnaSP Version 5.0 (Librado & Rozas, 2009). Tajima’s D tests of neutrality were performed for each site and for the total dataset. A significant negative Tajima’s D suggests that there are more low frequency polymorphisms than would be expected, whereas a significant positive Tajima’s D suggests there are low levels of low and high frequency polymorphisms (Tajima, 1989, 1989, 1993). Fu’s FS is based on the probability of observing an expected number of haplotypes (k) or more in a sample of a given size and is particularly useful for detecting whether populations have departed from equilibrium (e.g., following population expansion). A significant negative value indicates that there are more haplotypes than expected, which may occur if the population has recently expanded. A positive value of FS indicates fewer haplotypes than would be expected, which may occur if the population had recently been through a bottleneck (Fu, 1997).
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Genetic differentiation among each geographical population or host plant was estimated using pairwise FST incorporating the Tamura and Nei model evolution (ΦST) using ARLEQUIN. Median joining networks among Z. cucurbitae haplotypes were constructed and post-processed under maximum parsimony in Network Version 4.6.1.1 (Bandelt et al., 1999). Median joining networks were considered the most appropriated method in this case over other alternatives (e.g., minimum spanning and maximum parsimony). Minimum spanning networks perform best when sampling of haplotypes across the population is relatively complete; they do not perform well if there are significant gaps in sampling across the distribution of a species and where some internal node haplotypes are not sampled. On the other hand, median joining networks incorporate the maximum parsimony criterion and infer internal node haplotypes that may have been missed by incomplete sampling (Cassens et al., 2005). This gives a better estimate of the true genealogy (Woolley et al., 2008). - Genetic differentiation
An analysis of molecular variance (AMOVA) was conducted in ARLEQUIN to assess partitioning of variation within and among sites. AMOVA is a robust method for testing hypotheses about hierarchical differentiation directly from molecular data. Genetic structure (within individuals, within populations, within groups of populations, among groups) was tested using non-parametric permutation procedures (Excoffier et al., 1992). Samples were constrained according to geographical location in relation to different host plants (Chapter 4) and biogeographic barrier (the six Thai biogeographical regions and two islands, Isthmus of Kra [Chapter 3], Southeast-Asia and West-Pacific [Chapter 5]) to assess the partitioning of variation under different hypotheses of structure.
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2.4.1.3 Isolation-By-Distance for genetic distance (ΦST).
Isolation-By-Distance was assessed based on linear regression analysis of geographic distance against genetic distance among populations (ΦST).