Population structure in relation to host-plant ecology and Wolbachia infestation in the comma butterfly

Authors


Ullasa Kodandaramaiah, Department of Zoology, Stockholm University, 106 91 Stockholm, Sweden.
Tel.: +46 8 164 398; fax: +46 8 167 715;
e-mail: ullasa.kodandaramaiah@zoologi.su.se

Abstract

Experimental work on Polygonia c-album, a temperate polyphagous butterfly species, has shown that Swedish, Belgian, Norwegian and Estonian females are generalists with respect to host-plant preference, whereas females from UK and Spain are specialized on Urticaceae. Female preference is known to have a strong genetic component. We test whether the specialist and generalist populations form respective genetic clusters using data from mitochondrial sequences and 10 microsatellite loci. Results do not support this hypothesis, suggesting that the specialist and generalist traits have evolved more than once independently. Mitochondrial DNA variation suggests a rapid expansion scenario, with a single widespread haplotype occurring in high frequency, whereas microsatellite data indicate strong differentiation of the Moroccan population. Based on a comparison of polymorphism in the mitochondrial data and sequences from a nuclear gene, we show that the diversity in the former is significantly less than that expected under neutral evolution. Furthermore, we found that almost all butterfly samples were infected with a single strain of Wolbachia, a maternally inherited bacterium. We reason that indirect selection on the mitochondrial genome mediated by a recent sweep of Wolbachia infection has depleted variability in the mitochondrial sequences. We also surmise that P. c-album could have expanded out of a single glacial refugium and colonized Morocco recently.

Introduction

Herbivorous insects are strikingly diverse, accounting for a quarter of all described eukaryote species (Bernays, 1998), and the mechanisms underlying their diversification have intrigued biologists for long. The pioneering work of Ehrlich & Raven (1964) on butterfly host-plant coevolution first called attention to the potential role of herbivory in insect diversification. Since then, it has been convincingly demonstrated that intimate herbivore–plant interactions have time and again led to elevated speciation rates (Mitter et al., 1988; Farrell, 1998). Within butterflies, studies have shown that wider host ranges are correlated with enhanced species diversity (Janz et al., 2001, 2006; Weingartner et al., 2006; Nylin & Wahlberg, 2008). Nevertheless, little is known about the mechanistic basis of host-plant-mediated speciation.

One hypothesis – the ‘oscillation hypothesis’ (Janz & Nylin, 2008) – posits that speciation is driven by successive cycles of expansion of the host-plant repertoire (i.e. broadening of the host range by the addition of novel host-plant species) and the genesis of new species through specialization on different hosts. Although experimental tests of such hypotheses are impractical, investigations into divergences between populations of extant polyphagous species can potentially shed light on incipient speciation. Thus, divergence between populations that differ in host-plant preferences indicates possible segregation of genetic variation between host-plant ‘races’ (Via, 2001; Drès & Mallet, 2002). In this regard, studies that have attempted to assess the geographic structuring of host-plant preferences within species are noteworthy. One species that has been extensively investigated with regard to host-plant preference is the nymphalid butterfly Polygonia c-album (Nymphalidae: Nymphalinae). Knowledge from past experimental work on this species indicates that female host-plant preference varies across populations and that these differences have a strong genetic component (Nylin, 1988; Janz, 1998; Nygren et al., 2006; Nylin et al., 2009). In this study, we test two competing hypotheses of genetic divergence between populations that have a bearing on our understanding of incipient ecological speciation mediated by host choice.

P. c-album is a widespread Palearctic species distributed in Europe, North Africa and Asia, with minor external morphological differences across its range. Several subspecies of P. c-album have been described – e.g. kultukensis (Siberia), hamigera (South East Russia and Japan), asakurai (Taiwan, China and Japan) and imperfecta (Morocco). The taxonomic status of P. interposita has been disputed. In the study of Tuzov (2000), it was considered a species, whereas Gorbunov (2001) treated it as a subspecies within P. c-album. More recently, Churkin (2003) regarded P. interposita as a bona fide species, based on differences in wing pattern and genitalia. Recent molecular analyses of nuclear genes confirm differences between P. c-album and P. interposita, although sequences of the mitochondrial gene cytochrome oxidase subunit I (COI) are almost identical (Wahlberg et al., 2009a). In this study, we have considered P. interposita as a separate species in accordance with the study of Wahlberg et al. (2009a) and thus excluded it from our analysis. The larvae of P. c-album feed on foliage of plants in the families Urticaceae, Ulmaceae, Cannabidaceae, Salicaceae, Grossulariaceae, Betulaceae and Corylaceae (Nylin, 1988; Tolman & Lewington, 1997; Savela, 2010).

Nylin (1988) compared the host preferences of spring generation females from the UK and Swedish populations. He found that the UK females were specialized to a stronger degree on Urtica dioca (Urticaceae) compared with Salix caprea (Salicaeae), whereas the Swedish females were more general in preference, a result that was further corroborated in the study of Nylin & Janz (1993). Janz (1998) and Nygren et al. (2006) also showed, using reciprocal crosses, that the female preference trait was linked to the X chromosome. The X chromosome has also been implicated in female host choice in Papilio butterflies (Thompson, 1988; Scriber & Lederhouse, 1992). Nygren et al. (2006) and Nylin et al. (2009) reported that Spanish populations strongly preferred Urticaceae, whereas the Belgian and Swedish populations were more general in host-plant choice. Furthermore, Estonian females are generalists, similar to Swedish females.

Experiments have shown that Urticalean rosids (Urticaceae, Ulmaceae, Cannabidaceae) support faster larval growth rates in this species (Nylin, 1988; Janz et al., 1994). Nygren et al. (2006) surmise that the geographic variation in female host preference could be linked to patterns of voltinism, and this was christened the ‘time constraint hypothesis’ in the study of Nylin et al., 2009. Accordingly, specialization on urticalean rosids is selected for in the time-stressed bi- or trivoltine southern populations, whereas the northern univoltine populations face relaxed selection. A competing hypothesis is that the specialist and generalist traits evolved separately in different populations, and the current pattern we observe is determined by the phylogeographic history of, and geneflow between, populations – for instance, specialist and generalist populations could have survived in two different glacial refugia, followed by post-glacial expansions from each. Thus, this hypothesis predicts that the generalist and specialist populations form respective genetic clusters. We here test this hypothesis based on molecular data from mitochondrial (henceforth mtDNA) and microsatellite (henceforth nDNA) markers from nine populations, including two specialist (UK and Spain) and two generalist (Sweden and Belgium) populations.

The goal of this study was to understand the population genetic and phylogeographic structure of populations in this species. In addition to being a framework to test the above-stated hypothesis, such data are crucial to understand the evolution of host-plant use in this model species and ultimately shed light on the mechanistic basis of host-plant-mediated ecological speciation. We found that results from mtDNA and nDNA were not congruent and therefore also tested for Wolbachia infestation in the species. Bacteria in the genus Wolbachia are commonly occurring cytoplasmic endosymbionts found in 20 to >66% of all insect species (Werren & Windsor, 2000; Hilgenboecker et al., 2008). Being maternally inherited, they also have the potential to alter the contemporary mitochondrial genetic structure of populations. We have used molecular assays to ascertain the presence of Wolbachia and strain diversity in P. c-album.

Materials and methods

Sampling and DNA extraction

We obtained 85 specimens of P. c-album from nine populations in Europe, Africa and Asia (Appendix 1). The sampling mainly represents populations in Spain, Morocco, UK, Sweden and Russia, but also Macedonia, France, Belgium and Finland. Most samples were collected by us or colleagues, and some were bought from commercial suppliers. DNA was preserved either by desiccation or by immersing two legs in alcohol. Extractions were made from two legs of each individual using the QIAGEN (Hilden, Germany) DNEasy Extraction Kit following the manufacturer’s protocol.

Sequencing

The mitochondrial gene COI, the ‘barcoding gene’ (Hebert et al., 2003), is known to be variable enough within nymphalid species (Wahlberg & Saccheri, 2007; Vandewoestijne et al., 2004) for phylogeographic analyses. The two primer pairs, LCO-HCO and Jerry-Pat, were used to amplify 1450 bp of COI (see Kodandaramaiah & Wahlberg, 2007 for primer sequences). The PCR protocol was as follows: 95 °C for 5 min, followed by 40 cycles of 94 °C for 30 s, 50 °C for 30 s, 72 °C for 1 min and a final extension period of 72 °C for 10 min. Sequencing was carried out in a Beckman Coulter (Bromma, Sweden) CEQ 8000 capillary sequencer using forward primers. In the cases where the sequence quality in the second half was not acceptable, they were resequenced with the reverse primer. Chromatograms were checked and aligned visually in BioEdit v7.0.5.3 (Hall, 1998). Appendix 1 lists GenBank accession numbers of all sequences.

Microsatellites

Ten variable microsatellite loci, the development of which has been described in the study of S. Nylin, E. Weingartner, N. Janz and U. Kodandaramaiah (submitted), were utilized in the study. The PCR cycling profile consisted of the following steps: (i) 95 °C (15 min); (ii) 30 cycles of annealing temperature (30 s), 72 °C (30 s) and 95 °C (30 s); (iii) annealing temperature (1 min); and (iv) final extension of 30 min at 72 °C. The annealing temperature was 50 °C, except for ‘Polalb 11’ for which it was 60 °C. DNA amplifications were carried out with 10-μL PCRs that included 1.0 μL DNA template, 1 × PCR buffer, 40 μm dNTP, 0.5 units of HotStart Taq (Qiagen) and 0.1–0.25 μm primer (adjusted according to the binding efficiency of respective primer pairs). Forward primers were dye-labelled, and amplified products were electrophoretically separated on a Beckman Coulter CEQ 8000 capillary sequencer (Bromma, Sweden).

Sequence analyses

A statistical parsimony network (Templeton et al., 1992) of COI haplotypes was reconstructed in TCS v1.21 (Clement et al., 2000). Standard genetic diversity indices were calculated in Arlequin v 3.1 (Excoffier et al., 2005). These included total number of haplotypes, haplotype diversity (H; the probability that two randomly chosen haplotypes in the sample are different (Nei, 1987)) and nucleotide diversity (πn; the probability that two randomly chosen homologous nucleotide sites are different (Tajima, 1983; Nei, 1987)). Global and among population ΦST values were calculated as an estimate of the genetic differentiation. Exact tests of pairwise population differentiation (Raymond & Rousset, 1995; Goudet et al., 1996) were conducted with a Markov Chain of 100 000 steps.

A mismatch distribution analysis was performed in Arlequin to test whether the distribution of pairwise differences fits a model of sudden demographic expansion (Slatkin & Hudson, 1991; Rogers & Harpending, 1992). Deviations from neutrality were examined by performing Fu’s Fs test (Fu, 1997). This test uses the infinite-site model and is sensitive to departure from population equilibrium due to recent mutations. A large negative value generally indicates population demographic expansion. In total, 5000 samples were simulated in the analysis.

Microsatellite analyses

Allelic variability and pairwise population FST values were calculated in Arlequin v 3.1 (Excoffier et al., 2005) with significance for the latter values tested based on 100 000 permutations. Exact tests of Hardy–Weinberg equilibrium, both globally and within each population, were conducted with default parameters in Arlequin (1 million steps in the Markov chain and 100 000 dememorization steps). A significant heterozygote deficit compared with the expected values suggested the presence of null alleles in eight loci. Hence, we also analysed the data set in the software FreeDNA (Chapuis & Estoup, 2007), which detects and corrects for the presence of null alleles in each population. The program implements the expectation maximization (EM) algorithm of Dempster et al., 1977 to estimate frequencies of null alleles and adjusts genotype frequencies according to the ENA correction method (Chapuis & Estoup, 2007). Pairwise FST values were estimated with the method of Weir, 1996 using the ENA-corrected values. A Mantel test (Mantel, 1967) was conducted in Genepop v4.0 using default parameters (Rousset, 2008) to test for geographic structuring among populations due to isolation-by-distance (Rousset, 1997). The analysis was repeated excluding Morocco from the analysis.

STRUCTURE v2.3 (Pritchard et al., 2000) was used to identify distinct genetic populations and assign individuals to these populations. STRUCTURE implements a model-based clustering method within a Bayesian framework to infer population structure. We analysed the data set with K (the prior on the maximum number of populations) values ranging from 1 to 10 using the admixture model (where each individual is allowed to have a fraction of its genotype from more than one population). The LOCPRIOR model, which assists clustering by making use of information on the origin of samples, was turned on. The analysis was run for 1 000 000 MCMC (Markov chain Monte Carlo) steps after a burnin of 100 000 steps. The analysis was rerun after discarding the locus ‘Polalb 6’, which failed to amplify in the Moroccan samples. The 10-locus data set was also analysed using the dominant markers model (Falush et al., 2007) by turning on the ‘RECESSIVEALLELES’ option in the program and setting the recessive value to an allele not observed at that locus (as suggested by the authors of STRUCTURE). This model, although designed primarily to deal with dominant markers such as AFLPs, allows more accurate results with codominant markers when null alleles are present in the data set (Pritchard et al., 2000; Falush et al., 2007). Individuals that did not amplify a particular locus were considered to have missing data, rather than being treated as null homozygotes.

Wolbachia-related assays

Wolbachia specific primers wsp81F (5′-TGG TCC AAT AAG TGA TGA AGA AAC3-’) and wsp691R (5′-AAA AAT TAA ACG CTA CTC CA-3′; (Zhou et al., 1998) amplify a gene (wsp) encoding a surface protein of Wolbachia. The primer pair has been extensively used to detect the presence of the bacterium in DNA extracts. PCR products visualized as bands in a standard 1% agarose gel indicate the presence of the bacterium in the DNA extract, thus providing a rapid method of detecting Wolbachia infestation in a large number of samples. We used this assay to ascertain infestation in our samples. The forward and reverse primers were concatenated with the universal primers T7Promoter and T3, respectively, to facilitate sequencing (see Wahlberg & Wheat, 2008 for more details). PCR protocols were as for COI with the exception that the annealing temperature was 55 °C and 8 μL product was checked on the gel. All PCR sets included positive and negative controls. Because the Wolbachia assays were conducted 3 years after COI was sequenced, samples that did not produce a visible band on the gel were tested with the LCO–HCO primer pair for the integrity of the DNA extracts.

Four Wolbachia genes, in addition to wsp, were sequenced from 20 samples (one to three from each population; Appendix 1) to ascertain strain diversity in P. c-album. These genes (ftsZ, gatB, groEL and gltA) were chosen from published studies demonstrating their variability within Wolbachia (Casiraghi et al., 2005; Baldo et al., 2006; Ros et al., 2009). Appendix 2 lists the primers used along with their respective annealing temperatures.

Nuclear gene sequencing

We also sequenced a relatively fast-evolving nuclear gene, ribosomal protein subunit 5 (RPS5), from these 20 P. c-album samples in order to compare variability in COI to that in a nuclear gene region. PCR protocols were the same as for wsp. A Fisher’s exact test was performed in R v 2.11.1 (R Development Core Team, 2010) to test for a difference in polymorphism between COI and RPS5, after correcting for differences in effective population size (sensuHudson et al., 1987).

Results

Mitochondrial sequences

A 36-bp (between 643 and 681 bp) region contained missing data for several sequences and was hence deleted from the analysis to avoid possible ambiguities in haplotype assignment. The final data set thus consisted of 1414 bp. Seventeen unique haplotypes were found among the 84 sequenced specimens of P. c-album. Haplotype diversity was 40.25% (SD ± 6.9%), and nucleotide diversity was 0.039% (SD ± 0.34%). Respective values for each population are presented in Table 1. Pairwise ΦST are shown in Table 2. In the exact tests of population differentiation, Spain was significantly differentiated from UK and Sweden. None of the other pairwise comparisons was significant.

Table 1.   Total number of mtDNA haplotypes as well as nucleotide and haplotype diversity values for population. Belgium, France and Finland were excluded from the analysis because they were represented by less than five individuals.
 No. of haplotypes (samples)Haplotype diversity (H)Nucleotide diversity (πn)
Macedonia3 (5)0.7000 ± 0.21840.001132 ± 0.000933
Morocco2 (9)0.2222 ± 0.16620.000157 ± 0.000231
Russia3 (14)0.2747 ± 0.14840.000303 ± 0.000326
Spain2 (19)0.1053 ± 0.09200.000074 ± 0.000144
Sweden6 (16)0.6167 ± 0.13470.000607 ± 0.000506
UK4 (14)0.5824 ± 0.13720.000474 ± 0.000433
Table 2.   Pairwise Φst values calculated from mtDNA haplotype frequencies in Arlequin. Φst values of population pairs that were significantly different in the exact tests of differentiation are in bold.
 BelgiumFinlandFranceMacedoniaMoroccoRussiaSpainSweden
Belgium       
Finland0.00      
France0.000.00     
Macedonia0.000.000.00    
Morocco0.000.150.150.09   
Russia0.000.070.070.100.000.00  
Spain0.000.340.340.230.020.010.00 
Sweden0.000.000.000.060.000.010.030.00
UK0.000.030.030.090.020.040.070.04

The haplotype network of P. c-album was star-shaped (Fig. 1) and contained four missing haplotypes. Of the 84 individuals, 65 (77.3%) carried the same haplotype (Widespread), which formed the centre of the network and was found in all nine populations. Of the remaining 16 haplotypes, 13 were represented by a single individual. Two haplotypes (UK1 and UK2) were represented by two samples each from UK, whereas one haplotype (SW1) was represented by two individuals from Sweden.

Figure 1.

 Statistical parsimony network of the 17 Polygonia c-album cytochrome oxidase subunit I haplotypes. SW – Sweden; UK – United Kingdom; RU – Russia; SP – Spain; FI – Finland, MO – Morocco; FR – France; MK – Macedonia; BE – Belgium. The central haplotype ‘Widespread’ was found in 65 individuals, whereas SW1, UK1 and UK2 were represented by two individuals each. Remaining haplotypes were restricted to single individuals.

The mismatch distribution showed a negative slope, implying the leading edge of a unimodal curve. The curve did not deviate from that expected under a model of sudden expansion (SSD = 0.00037164, P (SSDsim ≥ SSDobs) = 0.86450000). Fu’s Fs value was significantly negative (Fs = −22.73881, P (sim_Fs ≤ obs_Fs) < 0.0001).

Microsatellites

All 10 loci were variable, with allele numbers ranging from 13 to 35 (Table 3, Appendix 3). One locus (Polalb 6) did not amplify in the butterflies from Morocco. The Belgian population was represented by a single individual and was hence deleted from pairwise population comparisons. Eight loci had significantly lower heterozygosity values compared with the expected Hardy–Weinberg heterozygosity values (all except Polalb 11 and Polalb 20; Table 3). This indicates the possible presence of null alleles in these loci, which was corroborated by FreeNA. However, FST estimates with and without correction using the ENA method showed similar patterns of differentiation. In both the cases, Morocco was differentiated from other populations with FST values >0.1 (Table 4), whereas all other pairwise FST values were <0.1. Morocco was significantly differentiated from all populations except Belgium and France, which were represented by one and three individuals, respectively. Russia was significantly different from Morocco, Sweden, Finland and Spain (Table 4).

Table 3.   Allelic variability, expected and observed heterozygosity values estimated from genotyping 85 Polygonia c-album individuals for 10 microsatellite loci. Observed heterozygosity values significantly lower than expected are in bold.
LocusAllelic rangeNo. of allelesExp. HObs. H
Polalb 2120–164170.875490.36111
Polalb 5269–309350.946540.425
Polalb 669–139260.920740.54795
Polalb 7173–233240.924710.48649
Polalb 8117–145130.849010.28395
Polalb 10117–181240.944570.37681
Polalb 11205–257140.803830.71765
Polalb 1781–119150.899460.23729
Polalb 20175–213150.896140.83529
Polalb 12119–163220.925560.4878
Table 4.   Estimates of pairwise population differentiation based on microsatellite data measured as FST values. Numbers to the left of the diagonal are FST estimates without correcting for the presence of null alleles, calculated in Arlequin. FST values in bold are significantly greater than zero. Numbers to the right of the diagonal are FST values estimated after ENA correction for null alleles in FreeNA.
 UKRussiaMoroccoSwedenFinlandSpainMacedoniaFrance
UKN/A0.020.160.000.040.01−0.010.06
Russia0.02N/A0.150.020.090.020.020.06
Morocco0.190.21N/A0.140.200.140.150.20
Sweden0.010.050.19N/A0.050.000.000.04
Finland0.020.060.180N/A0.06−0.040.07
Spain0.010.060.190.000.01N/A−0.010.05
Macedonia0.020.070.190.03−0.030.01N/A0.01
France0.050.020.080.01−0.010.020.00N/A

The Mantel test indicated no significant isolation-by-distance (one tailed P value = 0.1), and this was true even when Moroccan samples were excluded from the analysis. In all STRUCTURE analyses, the log likelihood value increased as we increased the prior on the maximum number of populations (K) from one to two, indicating that there were at least two genetic clusters in the data set. However, we found no consistent pattern of increase or decrease in the log likelihood values as we increased K from 2 to 10. We first report results from the analysis on the 10-locus data set without imposing the dominant markers model (i.e. not ‘correcting’ for the presence of null alleles). A well-defined cluster comprised of the Moroccan individuals was recovered in all runs except with K = 1. Another cluster, of the Russian individuals, was recovered in runs with K = 3–6, whereas this cluster was less cohesive with higher K values. In keeping with the principle that the minimum number of populations that capture the structure should be chosen (Pritchard et al., 2000, in the documentation to STRUCTURE), we depict results from the run with K = 3 (Fig. 2). We do note that we were unable to objectively choose the best value for K. The results with respect to the Moroccan and Russian clusters were mirrored in the analysis where the locus Polalb 6 was discarded, confirming that the missing data did not affect results. With the dominant markers model imposed (on the 10-locus data set), we obtained similar results, i.e. the Moroccan cluster was well resolved with all values of K, whereas the Russian cluster was well defined only with lower K values.

Figure 2.

 Population structure inferred in the STRUCTURE analysis of the 10-locus data set with K = 3. Each individual is represented by a single vertical line with a maximum of three coloured segments, with lengths proportional to each of the three inferred clusters. Numbers on the horizontal axis correspond to the eight predefined populations. 1 corresponds to the Russian individuals and 7 to the Moroccan ones.

For all loci except Polalb 11, we were unable to amplify all individuals. We ran a FST population comparison in Arlequin (among Russian, UK, Swedish, Spanish and Moroccan populations) including those loci with most representation and least deficit of heterozygotes (Polalb 5, Polalb 7, Polalb 8, Polalb 11, Polalb 20 and Polalb 22). The Moroccan population was strongly and significantly different from the other populations. Similarly, the Russian population was significantly different from the rest (data not shown).

Wolbachia

Of the 85 samples, 82, representing all nine populations, produced a distinguishable band on the gel after PCR amplification with the wsp primers. Two samples subsequently failed to amplify LCO–HCO, indicating degradation of DNA in storage. The 20 wsp PCR products sequenced resulted in identical sequences. Similarly, the ftsz, gatB, Wglt and groEL sequences displayed no variation across individuals. All five sequences belonged to Wolbachia supergroup B (sequences submitted to GenBank, accession numbers JN09314953).

Nuclear gene sequencing

The 20 RPS5 sequences (602 bp) had a total of 25 segregating sites, and 24 of these positions were heterozygous in one or more individuals. COI (1414 bp) had four segregating sites for the same set of individuals. The four times higher effective population size of autosomal genes compared with that of mitochondrial genes was taken into account by scaling the number of segregating sites in COI by a factor of four (sensuHudson et al., 1987). Nucleotide polymorphism in COI was significantly lower than that in RPS5 (P < 0.001).

Discussion

Rapid expansion of mtDNA

Analyses of the mitochondrial data set indicate a rapid expansion of the mitochondrial genome of P. c-album in the Palearctic. The haplotype network is a typical star-shaped network with a widespread central haplotype and geographically restricted ‘satellite’ haplotypes radiating from it (Fig. 1). This pattern is consistent with an exponentially growing population (Slatkin & Hudson, 1991). The widespread haplotype is ubiquitous across the species range, whereas no population is significantly differentiated genetically. The lack of differentiation over such a large geographic expanse suggests a recent and rapid expansion of the mitochondrial genome. This is further corroborated by the results from the mismatch distribution analysis and the significantly negative Fu’s F value, which fit with a model of sudden demographic expansion.

Wolbachia and its effects

Wolbachia are known to manipulate the reproductive ecology of their hosts in order to gain a selective advantage. The most common strategy is cytoplasmic incompatibility, where infected males cannot produce viable offspring with uninfected females or females infected with a different strain (Werren, 1997). The net effect is that uninfected females are selected against (Jansen et al., 2008). Because the bacterium is maternally inherited, this allows it to spread rapidly in the host population (Hurst et al., 1993). They are also known to distort sex ratios in favour of females, which is also thought to increase prevalence rates in host populations (Hurst & Majerus, 1993). In some cases, Wolbachia provide direct selective benefits to the host; for example, in Drosophila melanogaster, Wolbachia infection decreases susceptibility to mortality by a range of RNA viral infections (Hedges et al., 2008; Teixeira et al., 2008). Therefore, Wolbachia have evolved multiple mechanisms that facilitate rapid spread across populations of a host species. Being maternally inherited, mtDNA hitchhikes along with the bacterium (Turelli et al., 1992; Hurst et al., 1993; Rasgon et al., 2003; Hurst & Jiggins, 2005). This ultimately results in selective sweeps through which a single mtDNA haplotype dominates (Turelli & Hoffmann, 1991; Jiggins, 2003; Ballard & Whitlock, 2004; Hurst & Jiggins, 2005). Moreover, the effective population size of mtDNA at equilibrium is reduced because mutations in uninfected females are rapidly lost (Johnstone & Hurst, 1996).

Our Wolbachia assays show a near 100% prevalence of the bacterium in c-album and strongly indicate that all populations have the same strain. It must be noted that the PCR assays used to ascertain infection status are not error free (Jeyaprakash & Hoy, 2000) – it is thus possible that the individual that was not positive in the assay may still be infected. The infection rate we report in this species is one of the highest reported so far. We have been using P. c-album from various populations in laboratory experiments for over two decades and have never observed any marked bias in favour of females through the course of innumerable laboratory rearings. Neither has this been reported among wild populations, to our knowledge. We suspect that the high prevalence rate of the bacterium is maintained by the induction of cytoplasmic incompatibility.

Cytochrome oxidase subunit I is generally a fast-evolving gene, because of which it is commonly employed in phylogenetics and as a barcoding gene. RPS5 has a lower substitution rate in published nymphalid phylogenetic data sets (Wahlberg et al., 2009a,b; Kodandaramaiah et al., 2009; Peña & Wahlberg, 2008; Peña et al., 2011). Wahlberg et al. (2009a) included 400 taxa across Nymphalidae, whereas Wahlberg et al. (2009a) studied the genus Polygonia. Hence, it is highly unlikely that RPS5 has an inherently higher mutation rate than does COI. We can also rule out balancing selection in RPS5 as most substitutions are synonymous – only three of the 23 lead to a change in amino acid. RPS5 has higher polymorphism in this species even under the assumption that both genes have the same mutation rate. This, coupled with the Wolbachia assays, leads us to conclude that indirect selection on the mitochondrial genome mediated by the spread of the bacterium has reduced mtDNA diversity drastically. The effects of Wolbachia infestation resulting in altered mtDNA structure have been shown in several groups including mosquitoes, butterflies, ants and beetles (Keller et al., 2001; Shoemaker et al., 2003; Shaikevich et al., 2005; Gompert et al., 2006; Narita et al., 2006; Nice et al., 2009; see Hurst & Jiggins, 2005 for more examples). Selective sweeps of Wolbachia leave traces very similar to that from a demographic expansion (Johnstone & Hurst, 1996; Hurst & Jiggins, 2005). That the mtDNA data correspond to a rapid expansion model further corroborates the Wolbachia scenario. The depleted variation in COI suggests that the symbiont invasion is quite recent. However, most RPS5 sequences possessed two or more heterozygotes; we were hence unable to reliably identify haplotypes and reconstruct a haplotype network.

Microsatellite null alleles

Historically, microsatellites have been difficult to develop for butterflies and other Lepidoptera (Meglecz & Solignac, 1998; Zhang, 2004). Null alleles have been reported in almost all papers describing the development of microsatellites in Lepidoptera (reviewed in Meglecz et al., 2004). The presence of null alleles hence appears to be a feature of the lepidopteran genome, perhaps because of high mutation rates in the flanking regions of microsatellites. They have been considered problematic because they can bias FST estimates and population assignment (Oosterhout et al., 2006; Chapuis & Estoup, 2007); thus, null allele-free loci are assumed to be preferable. However, it is difficult to assess the presence of null alleles before genotyping a substantially large number of individuals. Moreover, in the case of butterflies, loci without null alleles are relatively uncommon, making studies with only null allele-free loci infeasible unless data from the majority of loci are discarded. Fortunately, recent methods (Oosterhout et al., 2006; Chapuis & Estoup, 2007) can correct for the bias introduced by null alleles, providing more realistic results. In this study, we found that the overall results were not strongly affected depending on whether or not these corrections were applied. A simulation study (Carlsson, 2008) showed that the presence of null alleles leads to a small overestimation of FST values (between 0.003 and 0.004) and slightly reduced the proportion of correctly assigned individuals in STRUCTURE. The author of the study goes on to state that the presence of null alleles is unlikely to have major impacts on conclusions regarding the presence or absence of genetic differentiation. Our results are in agreement with the author’s conclusions. FST estimates based on loci with null alleles are probably more robust to their presence than previously assumed.

Population structure vis-a-vis host-plant use

All microsatellite markers in our study had considerable variation, with a minimum of 14 alleles per locus (Table 3). This data set indicates strong differentiation of the Moroccan population from all other populations. The Russian population is also differentiated, although not as strongly as in the case of the Moroccan population. Geographic isolation due to the intervening marine barrier explains the differentiation of the Moroccan population. The Russian samples used in this study originated from the south-eastern part of the country and hence geographically relatively distant from the remaining populations. Surprisingly, there was no significant IBD, even when Morocco was excluded from the analysis, which indicates that the FST values were not correlated with distance. However, the FST analyses indicate that the Russian samples are significantly differentiated from several European populations, supporting the STRUCTURE results. Furthermore, Churkin (2003) states that the male genital morphology of the Siberian P. c-album is very different from the European butterflies. We hence conclude that the Russian samples used in this study comprise a distinct genetic cluster. Little is known about host-plant preference of this species in Russia, and future work on this population will be crucial in understanding the evolution of host-plant use in this species.

There was weak structuring among the remaining populations, suggesting considerable geneflow among them. Therefore, we find no support for the hypothesis that generalist populations (represented by Sweden and Belgium in this study) are genetically more proximate to each other in relation to specialist populations (represented by Spain and UK). We also do not find any evidence for latitudinal structuring based on voltinism. We reason that host-plant preferences have evolved more or less independently in different populations depending on local conditions. Thus, generalist and specialist traits appear to be evolutionarily plastic and little constrained by the phylogeographic history of populations, which is also of relevance in predicting the effects of climate change (Braschler & Hill, 2007).

Literature records indicate that the species feeds on Ribes spp in Morocco (Tennent, 1996). To our knowledge, Ribes is not one of the preferred species in Europe. It is possible that the Moroccan population has adapted to a different host-plant over time according to local conditions. We identity this population as a focal system to understand the evolution of host-plant preferences and to study early stages of host-plant mediated speciation.

Summary and conclusions

Population structure inferred from microsatellites suggested a strongly differentiated Moroccan population and to a lesser degree, another genetic cluster comprising the Russian individuals. However, mtDNA suggests a recent rapid expansion and little divergence among populations. We surmise that the best explanation is indirect selection on the mitochondrial genome by Wolbachia, resulting in rapid fixation of a single haplotype and reduced diversity overall. The data also suggest that specialist and generalist traits with respect to host-plant use are evolutionarily plastic and have evolved independently in different populations.

Acknowledgments

We acknowledge funding from the Swedish Research Council (Vetenskapsrådet) and the Strategic Research Programme EkoKlim at Stockholm University. UK was partly funded by the ERC grant EMARES during manuscript preparation. Samples were provided by Andrew B. Martynenko, Georg Nygren, Niklas Wahlberg, Slobodan Davkov, Anton Chichvarkhin, Michel Tarrier, Bengt Karlsson, A. G. Belik, Constantí Stefanescu, Jane Hill, Anssi Teräs and Reza Zahiri. We are also grateful to Karin Norén for help in the laboratory and Emily Hornett for providing information on Wolbachia primers.

Appendices

Appendix 1

List of samples used in this study along with their collection locality, GenBank accession numbers and their haplotypes as identified here

VoucherCollection localityHaplotypeGenBank (cytochrome oxidase subunit I)GenBank (ribosomal protein subunit 5)
EW28-24Belgium, AntwerpenWidespreadJN093199JN093173
EW39-4Finland, KaarinaWidespreadJN093216JN093167
EW46-20Finland, TurkuFIJN093238
EW46-21Finland, TurkuWidespreadJN093239
EW23-5France, Aquitaine, Dordogne, Le BugaFRJN093178JN093166
EW23-7France, La CourtWidespreadJN093204
EW23-6France, Languedoc-Roussillon, Aude, RoqueféreWidespreadJN093176
EW19-12MacedoniaWidespreadJN093185
EW19-13MacedoniaWidespreadJN093184
EW19-14MacedoniaMK1JN093183
EW19-15MacedoniaMK2JN093182JN093163
EW19-16MacedoniaWidespreadJN093181JN093164
EW38-3MoroccoWidespreadJN093213JN093160
EW38-4MoroccoWidespreadJN093209JN093161
EW38-5MoroccoMOJN093210
EW38-6MoroccoWidespreadJN093212
EW38-8Morocco, Haut Atlas septentrional, Tizi-n-OufraouWidespreadJN093217JN093162
EW38-7Morocco, Moyen Atlas central, Col du Zad et env.WidespreadJN093214
EW26-32Morocco, Moyen Atlas central, Djebel TarharhatWidespreadJN093175
EW38-1Morocco, Moyen Atlas central, Env. AzrouWidespreadJN093211
EW38-2Morocco, Moyen Atlas central, Env. AzrouWidespreadJN093215
EW13-1Russia, Buryatia Republic, Baruzinskiy MtsRU1JN093177
EW13-2Russia, Buryatia Republic, Baruzinskiy MtsWidespreadJN093207
EW13-5Russia, Buryatia Republic, MondyWidespreadJN093195
EW13-6Russia, Buryatia Republic, MondyWidespreadJN093194
EW13-14Russia, Chita Region, Udocan MtsWidespreadJN093198JN093155
EW13-15Russia, Chita Region, Udocan MtsWidespreadJN093205
EW13-16Russia, Chita Region, Udocan MtsWidespreadJN093201
EW14-6Russia, Chita Region, Udocan MtsWidespreadJN093206JN093172
EW37-2Russia, Irkutsk region, Slyudyanka valleyWidespreadJN093218
EW26-2Russia, Primorskiy KraiRU2JN093174
EW13-3Russia, Primorskiy Krai, VladivostokWidespreadJN093197
EW13-4Russia, Primorskiy Krai, VladivostokWidespreadJN093196
EW13-7Russia, Primorskiy Krai, VladivostokWidespreadJN093200JN093154
EW46-5Spain, CataloniaWidespreadJN093243
EW46-6Spain, CataloniaWidespreadJN093244
EW46-7Spain, CataloniaWidespreadJN093245
EW46-8Spain, CataloniaWidespreadJN093246
EW45-1Spain, Catalonia, Can LiroSPJN093219
EW45-2Spain, Catalonia, Can LiroWidespreadJN093220
EW45-7Spain, Catalonia, Can LiroWidespreadJN093225
EW46-1Spain, Catalonia, Can LiroWidespreadJN093237
EW46-2Spain, Catalonia, Can LiroWidespreadJN093240
EW46-3Spain, Catalonia, Can LiroWidespreadJN093241
EW46-4Spain, Catalonia, Can LiroWidespreadJN093242
EW23-1Spain, Catalonia, Girona Prov., El CortaletWidespreadJN093180
EW23-2Spain, El CortaletWidespreadJN093179JN093165
EW23-3Spain, El CortaletWidespreadJN093208
EW23-4Spain, El CortaletWidespreadJN093203
EW45-6Spain, El PuigWidespreadJN093224
EW45-3Spain, VallfornarsWidespreadJN093221JN093168
EW45-4Spain, VallfornarsWidespreadJN093222JN093169
EW45-5Spain, VallfornarsWidespreadJN093223
EW46-10Sweden, Uppland, ÅkersbergaWidespreadJN093227
EW46-11Sweden, Uppland, ÅkersbergaWidespreadJN093228
EW46-12Sweden, Uppland, ÅkersbergaWidespreadJN093229
EW46-13Sweden, Uppland, ÅkersbergaWidespreadJN093230
EW46-14Sweden, Uppland, ÅkersbergaWidespreadJN093231
EW46-9Sweden, Uppland, ÅkersbergaWidespreadJN093247
EW46-15Sweden, Uppland, HäggvikSW2JN093232
EW46-16Sweden, Uppland, HäggvikWidespreadJN093233
EW46-17Sweden, Uppland, HäggvikSW1JN093234
EW46-18Sweden, Uppland, HäggvikWidespreadJN093235
EW46-19Sweden, Uppland, HäggvikSW5JN093236JN093170
EW17-5Sweden, Uppland, StockholmWidespreadJN093187JN093158
EW17-6Sweden, Uppland, StockholmSW4JN093186
EW17-7Sweden, Uppland, StockholmWidespreadJN093202JN093159
EW17-11Sweden, Uppland, VallentunaSW3JN093192
EW17-15Sweden, Uppland, VallentunaSW1JN093191
EW17-1UK, OxfordWidespreadJN093193
EW17-2UK, OxfordUK2JN093190
EW17-3UK, OxfordUK1JN093189
EW17-4UK, OxfordWidespreadJN093188JN093157
EW45-8UK, Yorkshire, Bishop WoodWidespreadJN093226JN093171
EW47-1UK, Yorkshire, Bishop WoodUK1JN093248JN093156
EW47-2UK, Yorkshire, Bishop WoodWidespreadJN093249
EW47-3UK, Yorkshire, Bishop WoodUK3JN093250
EW47-4UK, Yorkshire, Bishop WoodWidespreadJN093251
EW47-5UK, Yorkshire, Bishop WoodWidespreadJN093252
EW47-6UK, Yorkshire, Bishop WoodWidespreadJN093253
EW47-7UK, Yorkshire, Bishop WoodWidespreadJN093254
EW47-8UK, Yorkshire, Bishop WoodWidespreadJN093255
EW47-9UK, Yorkshire, Bishop WoodUK2JN093256

Appendix 2

PCR primers used to determine Wolbachia strain diversity among populations. ‘T °C’ refers to annealing temperatures used. ‘bp’ indicates the length of the sequence in P. c-album

GeneEncodingForward PrimerReverse PrimerT °CbpReferences
tsZCell division proteinftsZ_F1 (ATYATGGARCATATAAARGATAG)ftsZ_R1 (TCRAGYAATGGATTRGATAT)54435Baldo et al. (2006)
gatBGlutamyl-tRNA(Gln) amidotransferase, subunit BgatB_F1 (GAKTTAAAYCGYGCAGGBGTT)gatB_R1 (TGGYAAYTCRGGYAAAGATGA)54369Baldo et al. (2006)
groELHSP 60groEL-Fd (CAACRGTRGSRRYAACTGCDGG)GroEL-Rd (GATADCCRCGRTCAAAYTGC)50483Ros et al. (2009)
gltACitrate synthaseWgltAF1 (TACGATCCAGGGTTTGTTTCTAC)WgltARev2 (CATTTCATACCACTGGGCAA)52632Casiraghi et al. (2005)

Appendix 3

Observed and expected heterozygosity values for each locus and population. Bold values indicate observed heterozygosity values that are significantly lesser than the expected values. Data for Finland, Belgium and France are not shown as they were represented by less than four individuals. Polalb 6 was not amplifiable in the Moroccan samples

 UKRussiaMoroccoSwedenSpainMacedonia
LocusExp. HObs. HExp. HObs. HExp. HObs. HExp. HObs. HExp. HObs. HExp. HObs. H
Polalb 20.620.000.890.530.861.000.900.310.820.180.530.00
Polalb 50.940.360.940.690.540.670.930.250.930.280.960.75
Polalb 60.870.670.920.430.000.000.900.500.910.530.930.60
Polalb 70.880.360.890.620.620.000.900.560.950.500.930.50
Polalb 80.870.210.840.570.220.220.880.290.800.050.800.40
Polalb 100.890.500.940.550.780.140.930.380.920.430.800.00
Polalb 110.830.860.700.400.450.440.850.810.860.840.780.80
Polalb 170.910.500.890.330.750.000.910.080.880.270.890.25
Polalb 200.880.790.920.860.710.560.880.860.860.950.820.60
Polalb 220.880.500.900.500.750.220.890.600.920.530.890.25

Ancillary