Advertisement
Research Article

Population Genetic Structure of the Grasshopper Eyprepocnemis plorans in the South and East of the Iberian Peninsula

  • María Inmaculada Manrique-Poyato,

    Affiliations: Departamento de Genética,Universidad de Granada, Granada, Spain, Departamento de Células Troncales, Centro Andaluz de Biología Molecular y Medicina Regenerativa (CABIMER), Sevilla, Spain

    X
  • María Dolores López-León,

    Affiliation: Departamento de Genética,Universidad de Granada, Granada, Spain

    X
  • Ricardo Gómez,

    Affiliation: Departamento de Ciencia y Tecnología Agroforestal, Universidad de Castilla La Mancha, Albacete, Spain

    X
  • Francisco Perfectti,

    Affiliation: Departamento de Genética,Universidad de Granada, Granada, Spain

    X
  • Juan Pedro Martínez Camacho mail

    jpmcamac@ugr.es

    Affiliation: Departamento de Genética,Universidad de Granada, Granada, Spain

    X
  • Published: March 08, 2013
  • DOI: 10.1371/journal.pone.0059041

Abstract

The grasshopper Eyprepocnemis plorans subsp. plorans harbors a very widespread polymorphism for supernumerary (B) chromosomes which appear to have arisen recently. These chromosomes behave as genomic parasites because they are harmful for the individuals carrying them and show meiotic drive in the initial stages of population invasion. The rapid increase in B chromosome frequency at intrapopulation level is thus granted by meiotic drive, but its spread among populations most likely depends on interpopulation gene flow. We analyze here the population genetic structure in 10 natural populations from two regions (in the south and east) of the Iberian Peninsula. The southern populations were coastal whereas the eastern ones were inland populations located at 260–655 m altitude. The analysis of 97 ISSR markers revealed significant genetic differentiation among populations (average GST = 0.129), and the Structure software and AMOVA indicated a significant genetic differentiation between southern and eastern populations. There was also significant isolation by distance (IBD) between populations. Remarkably, these results were roughly similar to those found when only the markers showing low or no dropout were included, suggesting that allelic dropout had negligible effects on population genetic analysis. We conclude that high gene flow helped this parasitic B chromosome to spread through most of the geographical range of the subspecies E. plorans plorans.

Introduction

Supernumerary chromosomes, also called accessory or B chromosomes, are additional chromosomes found in some individuals at some populations of about 15% of eukaryotic species [1]. Most of them are harmful for the host genome and invade populations at the expense of a variety of drive mechanisms [2].The grasshopper Eyprepocnemis plorans subsp. plorans lives in moist habitats near river courses and coastal areas throughout the circum-Mediterranean region, also reaching the Caucasus and the Arabian Peninsula [3]. B chromosomes have been found in almost all natural populations of this subspecies so far analyzed from Spain, Morocco, Italy, Greece, Turkey, Armenia, and Dagestan [4], the only B-lacking region being located at the headwaters of the Segura River basin in Spain [5]. A recent study has suggested that B chromosomes in E. plorans plorans are rather young [6]. It would thus be helpful to understand how these B chromosomes have spread so rapidly throughout most of the geographical range of this subspecies. The existence of meiotic drive for B chromosomes in this species [7] explains how they rapidly invade new populations [8]. However, ascertaining how B chromosomes reached other populations would require the knowledge of population genetic structure, since this would provide indirect estimations of the magnitude of gene flow among populations.

Gene flow may be directly analyzed by demographic studies, e.g. capture-mark-recapture methods, but it can also be indirectly estimated through population genetic analysis by molecular markers. In this latter case, the calculation of Wright's indices to estimate inbreeding at several hierarchical levels (FIS, FST and FIT) from observed intrapopulation heterozygosities as well as expected population and metapopulation heterozygosities, is the most commonly used tool [9], [10]. A number of molecular markers can be used to gain information on population genetics and structure [11], microsatellites (SSRs) being one of the most commonly used because of a number of advantages, such as their co-dominant nature and a high degree of polymorphism. However, the use of SSRs requires a prior process of ascertaining the appropriate primers to amplify them by PCR, which implies the sequencing of DNA regions that are adjacent to each SSR. In organisms with little or no DNA-sequence information, an easier alternative is to use theoretical SSR sequences to design PCR primers that are able to amplify the DNA regions between two consecutive SSRs. The size (in base pairs) of these amplicons constitutes the Inter Simple Sequence Repeat (ISSR) markers [12]. These behave as dominant, with "band presence" corresponding to the dominant phenotype (homozygous or heterozygous) and "band absence" being the recessive phenotype. Although this is a clear disadvantage because it impedes a direct estimation of some population parameters (e.g. observed heterozygosity and thus Wright's FIS and FIT), they have certain advantages: the ease of generating a high number of markers, because no previous genomic information is necessary; relatively high reproducibility; and the possibility of visualizing them on agarose gels [13].

ISSR markers have been used for a variety of genetic and population-genetic purposes, such as: the characterization of different varieties in maize [14], wheat [13], and barley [15]; the genetic characterization of several organisms [16]; the analysis of genetic diversity [17], [18]; the identification of genes associated with disease resistance [19]; phylogenetic analysis [20], [21]; and gene mapping [22]. In insects, ISSR markers have been employed to characterize different lineages in Bombyx mori [23] and Antheraea mylitta [24], as well as in studies on hybridization [25], genetic diversity [26][28], and conservation genetics [29].

One of the main caveats of ISSR markers, and other markers based on PCR amplification, is allelic dropout, i.e. a stochastic failure of PCR amplification in one of the alleles carried by an individual at a given locus, leading to the determination of a wrong genotype [30]. Even though ISSR markers show Mendelian inheritance [31], allelic dropout can yield an excess of the "band absence" allele which could be mistaken for segregation distortion. Therefore, it is advisable to evaluate the effect of allelic dropout on the PCR repeatability and inheritance of ISSR markers [32], [33].

In this paper, we analyze several parameters of population genetics and structure in 10 natural populations of the grasshopper Eyprepocnemis plorans subsp. plorans by means of ISSR markers, and also evaluate the incidence of allelic dropout on these parameters. The populations were sampled at two Spanish geographical regions, one being coastal and located in the south and the other being inland and located in the east, including several populations from the B-lacking region in the Segura River basin. The B chromosome variant found in the eastern region (B1) is different from those found in the southern region (B2 and B24), but all of them share the same kinds of repetitive DNAs [34]. The only population-genetic analysis performed previously in this species used B-chromosome frequency along several rivers in eastern Spain, including the Segura, Júcar, and Turia rivers [35]. These authors provided the first evidence for isolation by distance (IBD) in this species, suggesting that B-chromosome dynamics can best be explained by metapopulation models. Our present results confirm the existence of IBD in this species and show a clear genetic differentiation among populations belonging to the two regions analyzed. In addition, the low Gst values observed help explain how this young B-chromosome system has attained such a widespread geographical distribution.

Results

Genetic variation and population structure

The six primers employed yielded a total of 97 ISSR markers ranging in size from 180 to >2000 bp in the 10 populations as a whole, with 99% of the loci being polymorphic. Per region, however, the percentage of polymorphic loci fell to 77.9% (SD = 3.13) in the southern populations and 76.7% (SD = 1.78) in the eastern ones (Table 1).

thumbnail

Table 1. Proportion (SD) of polymorphic loci in the 10 populations analyzed.

doi:10.1371/journal.pone.0059041.t001

Data analysis with Hickory v1.1 [36], a program providing estimates of population genetic parameters through a Bayesian approach, pointed to both full and f = 0 as the best models, as indicated by the DIC parameters [37] (Table S1). However, the full model indicated an inbreeding value (0.96) being highly unlikely for a polygynandric organism like E. plorans [38]. For this reason, we chose the f = 0 (no inbreeding) for all subsequent calculations.

Table 2 shows that θ(II), an analogous of Nei's GST, was 0.129, i.e. very similar to the Bayesian estimate of this latter parameter (Gst-B = 0.123), and 95% confidence intervals indicated that both were significantly higher than zero. These low GST values indicate that genetic diversification of these populations by drift is impeded by high gene flow. An AMOVA showed the existence of significant population structure, with 9.61% variation among populations within regions (φSC) (P<0.0001), 15.05% between the southern and eastern regions (φCT) (P<0.001) and the remaining 75.34% found within populations (φST) (Table S2).

thumbnail

Table 2. Population parameters estimated by Hickory under the f = 0 model.

doi:10.1371/journal.pone.0059041.t002

The Structure software, which analyzes genetic structure through a Bayesian approach, showed that the number of genetic groups (K value) best fitting our data, inferred following Evanno et al. [39], was K = 2 (Figure 1 and Table S3), with most individuals from the southern populations being included in the Group 1 and most individuals from eastern populations being in the Group 2 (Table 3). This indicates that southern and eastern populations constitute two genetically differentiated groups (Figure 2).

thumbnail

Figure 1. Delta K values with respect to K, according to the calculation method by Evanno et al. [42].

These results were found using all 97 ISSR markers analyzed. Note the highest peak for K = 2.

doi:10.1371/journal.pone.0059041.g001
thumbnail

Figure 2. Ancestry of each individual to any of the two groups, using all 97 ISSR markers analyzed, yielded by the Structure software.

Each vertical bar represents one of the 255 individuals analyzed. Group 1 (southern region) is represented in red color, and Group 2 (eastern region) is shown in green color. Bar length is proportional to the inferred ancestry values into each group for each individual.

doi:10.1371/journal.pone.0059041.g002
thumbnail

Table 3. Proportion of individuals from each population assigned to each group by the Structure software, with K = 2.

doi:10.1371/journal.pone.0059041.t003

The Mantel test revealed significant isolation by distance (IBD) when we used pairwise-Fst values (r = 0.90, P = 0.0004) or Dice's dissimilarity indices (r = 0.68, P = 0.00003) for genetic distance (see genetic and geographical distances in Tables S4 and S5).

An unrooted neighbor-joining phylogenetic tree built with the Pairwise-Fst genetic distances (drawn with the AFLP-surv software), showed the longest distance between the southern and eastern regions (Figure 3).

thumbnail

Figure 3. Unrooted Neighbor-Joining phylogenetic tree.

This was built with Pairwise-Fst genetic distances determined with the 97 ISSR markers analyzed. Note the two groups of populations corresponding to the southern (upper half) and eastern (lower half) regions. All populations harbored B chromosomes excepting Caravaca, Socovos and Claras.

doi:10.1371/journal.pone.0059041.g003

Repeatability and inheritance of ISSR markers

More than half of the ISSR markers (51 out of 97) displayed consistent patterns under the two controls performed, but 3 markers failed to show repeatability under control 1 only, 40 failed for control 2 only, and 4 markers failed for both controls (Table S6). Only six markers (26-500, 26-650, 26-750, 26-1000, 39-250, and 43-540) showed a frequency of allelic dropout higher than 30%. Control 2 revealed significantly higher levels of dropout than control 1 (t = 5.8, df = 97, P<0.0001). Correlation analyses showed no significant association between fragment size and dropout measured in controls 1 (r = -0.05, N = 98, P = 0.65) or 2 (r = -0.15, N = 98, P = 0.13).

A total of 101 progeny analyses (PAs) were performed to test inheritance of 23 ISSR markers (Table S7). As a whole, we found significant departures from Mendelian proportions in 30/90 PAs. To summarize these results, we applied a heterogeneity chi-square test for markers where more than one PA had been performed for a same type of cross. This decreased the number of PAs to 49, 20 of which showed P<0.05. However, only five of these remained significant after the sequential Bonferroni test (three 1x1 and two 0x1 crosses). Remarkably, these five PAs showed an excess of marker-lacking individuals, as expected from allelic dropout. However, among the 15 PAs that lost significance with the sequential Bonferroni test, 8 showed an excess of marker-lacking embryos but the remaining 7 showed an excess of marker-carrying embryos, these figures being consistent with chance effects. It bears noting that four of the five ISSR markers whose inheritance was tested in the five PAs rejecting the null hypothesis on segregation (i.e. 7-1000, 7-1100, 7-1400 and 14-750) had manifested allelic dropout in the PCR test (see Table S6), whereas it had not been observed for 14-1450. In addition, 7 out of the 10 markers involved in the PAs that lost significance after correction for multiple tests, had shown a dropout in the PCR tests. We therefore consider that our results on the inheritance of the 23 ISSR markers analyzed failed to show significant departures from Mendelian segregation that were not caused by allelic dropout.

Population parameter estimates after discarding ISSR markers showing allelic dropout

To analyze the possible incidence of allelic dropout on population genetics and structure results, we repeated calculations with Hickory and Structure after discarding the 10 markers showing high incidence of dropout detected by the PCR repeatability analysis (26-500, 26-650, 26-750, 26-1000, 39-250 and 43-540) or inheritance analysis (7-1000, 7-1100, 7-1400 and 14-750). The population parameters estimated with the 87 remaining markers, listed in Table S8, gave results almost identical to those found with all 97 markers (compare with Table 2). Even using only the 46 markers showing no sign of allelic dropout (after discarding those that failed PCR repeatability or showed segregation distortion), population genetic estimates differed by only 8.16–29.7% from those made with all markers (Table S9).

The analysis of population structure with the 87 ISSR markers showing slight dropout or none at all, with the Structure software, gave very similar results to those found when including all the markers, with K = 2 best fitting our data, and high proportions of individuals from the southern populations being included in one group and those from the eastern populations in the other group (see Table S10 and Figures S1 and S2).

Even with only 46 markers, Structure showed that K = 2 best fitted our data, and the genetic differentiation between the southern and eastern regions was clearly evident, with high proportions of individual assignment to the correct region (see Table S11 and Figures S3 and S4).

Discussion

As in other organisms previously analyzed [23], [25], [28], [40], ISSR markers are highly polymorphic in the grasshopper E. plorans, with 99% of the loci showing variation in the 10 populations as a whole, and more than 75% of loci being variable in each population analyzed. Since ISSR markers are dominant, only expected heterozygosity (hs) can be calculated (assuming Hardy-Weinberg equilibrium), and it ranged between 0.205 in Socovos and 0.235 in Mundo. These values are similar to those described for ISSR markers in other insects such as, for instance, Antheraea mylitta [24] and Plutella xylostella [28]. Much lower values were found for ISSR markers in the hymenopteran Gonatocerus ashmeadi [27], but higher values have been reported for RAPDs in the South American grasshopper Sinipta dalmani (0.274–0.326) [41] and the mosquito Anopheles darlingi (0.27–0.32) [42].

Hs (i.e. the average for hs in all populations) was lower than Ht (expected heterozygosity in all populations as a whole), suggesting a possible heterozygous deficiency which could be explained by allelic dropout, inbreeding or population subdivision. As explained above, dropout effects are negligible in this case, and inbreeding is unlikely since E. plorans is a polygynandric species [38] showing large population size. Unfortunately, Wright's Fis, which would provide an estimate of inbreeding, cannot be calculated with dominant markers. With this concern in mind, we conclude that, in this case, heterozygous deficiency is most likely explained by population subdivision, given that the 10 population samples included two geographically well-separated subsets.

The significant values found for θ(II) and Gst-B, and the highest variance between regions, compared to within regions, revealed the existence of population subdivision for the analyzed areas. Population structure was also shown by the Structure software. A K of 2 best explained the data, with population groups being well defined in coincidence with the southern and eastern regions, so that more than 90% of individuals were properly assigned to them. Remarkably, our analysis of population genetics and structure parameters, after discarding those markers showing allelic dropout, showed almost identical results to those performed with the complete data set, suggesting that allelic dropout has a minor effect on these estimations. Therefore, although ISSR, and other markers being affected by allelic dropout, become less relevant with the advent of new sequencing technologies and genotyping methods, they still constitute an efficient and low-cost method of revealing population structure, especially in non-model organisms.

In the 10 populations of the grasshopper E. plorans analyzed here, the low (but significant) values of GST found suggest that gene flow is high in these populations, but not high enough to avoid a certain genetic differentiation between southern and eastern populations. The grasshopper E. plorans is more a jumper than a flyer, but it has a high dispersal ability facilitated by its polyphagy with low dietary demand [43]. The slight prevalence of gene flow over drift is thus logical, bearing also in mind the significant IBD found among the populations analyzed.

The only previous analysis of FST and IBD in a grasshopper species, performed in the South American grasshopper Sinipta dalmani (using RAPD markers), revealed extensive variation for Fst (0.058–0.23, mean = 0.122), i.e. slightly higher than those detected by us for the ISSR markers in E. plorans, but no significant IBD was found among the 8 populations analyzed in S. dalmani [41]. These authors explained this lack of IBD by non-gradual divergence among the populations analyzed. Crickets in the genus Stenopelmatus are other orthopterans in which significant IBD has been revealed with ISSR and mtDNA markers [44]. The significant IBD observed in E. plorans for the ISSR markers is consistent with previous IBD shown, in this same species, for B-chromosome frequency [35].

The high gene flow inferred from the low GST values observed for these ten populations of E. plorans plorans from the south and east of the Iberian Peninsula, contributes to explain the rapid spread of parasitic B chromosomes throughout most of its geographical range. B chromosomes are genomic parasites of strictly vertical transmission [45]. Therefore, although they can prosper in a given population, reaching high frequency at the expense of meiotic drive [8], gene flow is necessary to augment their spread across populations. Our present analysis, showing high levels of gene flow among populations of the grasshopper E. plorans plorans, in accord with the high dispersal ability reported for this species [43], explains why these B chromosomes have reached almost the entire geographical range of this subspecies, despite being rather young [6].

Whereas the five populations from the southern region (Algarrobo, Torrox, Nerja-0, Nerja-2 and Salobreña) all carried B chromosomes, only two (Mundo and Calasparra) from the eastern region carried them. In this latter region, the observed pairwise Fst values (see Table S4) indicate almost double amount of average genetic distance between populations differing in B chromosome presence (0.09) than that found between populations non differing at this respect (0.049). For instance, Calasparra (+B) and Caravaca (-B) are the two closest populations in the eastern region (20 Km) but show one of the highest pairwise Fst values (0.1365). This suggests higher isolation (i.e. lower gene flow) between the +B and -B populations and is consistent with the possibility that B chromosomes are absent in these latter populations because B chromosomes have not yet arrived to them [5].

Materials and Methods

Genetic variation and population structure

Specimens of the grasshopper Eyprepocnemis plorans were collected at 10 natural populations in the south and east of the Iberian Peninsula (Table 4). No specific permits were required for the field studies. The locations sampled were not privately owned or protected in any way, and this field study did not involve endangered or protected species. Individuals were frozen by immersion in liquid nitrogen and stored at -80°C until used. DNA was extracted with the kit GenElute® Mammaliam Genomic DNA minipreps (SIGMA), following the manufacturer's recommendations. DNA quantification was performed in a TBS-380 minifluorometer (Turner Biosystems) using Picogreen dye (Quant-iT™ PicoGreen® dsDNA Kit; Molecular Probes, Invitrogen).

thumbnail

Table 4. Number of individuals analyzed with different primers for ISSRs.

doi:10.1371/journal.pone.0059041.t004

ISSR markers were amplified by polymerase chain reaction (PCR) using the primers shown in Table S12. PCR was performed in a 25-µl reaction containing 1x PCR buffer, 240 &M dNTPs, 2 µM of primer, 1 U of Taq polymerase (New England, Biolabs), and 10 ng of DNA. Reaction conditions included an initial denaturing at 94°C for 3 min followed by 40 cycles at 94°C (40 s), 57°C (45 s), and 72°C (1.5 min), and a final extension at 72°C for 5 min. All PCR experiments were conducted in an Eppendorf Mastercycler ep Gradient, and PCR products were visualized in 1.5% agarose gel with SYBR Safe (Invitrogen) following the manufacturer's recommendations, and analyzed in a gel-documentation system (Gel Doc XR and ChemiDoc XRS, Biorad). Fragment sizes were determined using the HyperLadder II (Bioline) molecular-weight marker, and the presence or absence of each fragment was coded as 1 and 0, respectively.

To test the incidence of PCR errors generating the absence of bands suggesting false recessive homozygotes (allelic dropout), we performed two types of replicates for the same individual, one where the two replicates shared the same master mix and were amplified at the same time (control 1), and another where the replicates were made from different master mixes and were amplified on different days (control 2). The frequency of allelic dropout was quantified as a percentage of individuals showing inconsistent genotypes in the two replicates.

ISSR data were analyzed by Bayesian methods, using the Hickory v1.1 software [36], bearing in mind their dominant nature. Data were analyzed under four different models: i) the full model, where the values of population differentiation (theta, θ, analogous to Wright's Fst parameter) and inbreeding (f, analogous to Fis) were different from zero, ii) the f = 0 model, which assumes no inbreeding within populations, iii) the theta = 0 model, based on the absence of population differentiation, and iv) the f free model, or free model, where values of f were chosen randomly for the analysis of the data regardless of prior information. The choice of the model was based on the DIC parameter, analogous to AIC in Bayesian model selection [37] and on the Dbar parameter, a measure of how well the model fits the data (smaller values of both are better).

The existence of isolation by distance (IBD) was analyzed by means of a Mantel Test comparing the matrix of pairwise-Fst values with the matrix of geographical distances between the sampled populations, with the Zt-win software [46] using 100,000 random repetitions. The matrix of pairwise Fst values was established by the software AFLP-surv [47]. All geographical distances between populations were calculated as the crow flies.

Also, we investigated IBD by using Dice's dissimilarity index [48], which avoids the assumption of Hardy-Weinberg equilibrium. We first calculated the similarity and dissimilarity coefficients between every two individuals with the Famd v1.25 software [49], and then summarized this information per population. With the dissimilarity coefficients calculated between every two populations, we built a matrix which was analyzed, together with the above-mentioned matrix of geographical distances, by means of the Mantel test.

To elucidate the population structure in E. plorans, we analyzed molecular variance (AMOVA) with the Arlequin v3.5 software [50], with 10,000 permutations and 20% maximum absence of data. We defined independent groups (regions) for southern and eastern populations.

With the genetic matrix distance (pairwise-Fst), estimated using the program AFLP-surv from the ISSR marker data, we inferred a phylogenetic tree under the Neighbor-Joining method [51] implemented in the Neighbor program included in Phylip v3.69 package [52].

To identify genetically homogeneous groups within our samples, we used a Bayesian algorithm implemented in the Structure v2.3.1 software [53], assuming the existence of K populations, or groups, characterized by a set of allele frequencies for each locus. Individuals in the sample were assigned, probabilistically, to one group or more. We used the admixture model and the allele frequencies correlated model, without prior assumptions concerning the population. For each value of K (from 1 to 11), 11 independent runs were made and, for each run, 100,000 iterations were carried out after a burn-in period of 50,000 iterations. To detect the number of genetically homogeneous groups (K) that best fit the data, we used Structure Harvester website [54], which implements the Evanno method [39].

Inheritance of ISSR markers

To analyze inheritance of ISSR markers, we performed 10 controlled crosses analyzing the markers derived from one or more of the ISSR6, ISSR7, and ISSR14 primers (Table S13). The remaining primers were discarded for inheritance analysis because they had shown the highest values of dropout in the PCR controls (see above). In total, we analyzed 19 parents (one male was involved in two crosses) and 1920 embryo progeny.

Inheritance expectations for autosomal markers, shown in Table S14, indicate that when the two progenitors show band presence (1×1 cross), it may happen that all progeny show the marker, in which case we can infer that at least one parent was homozygous for the presence of the marker. However, both parents maybe heterozygous for the presence of marker, in which case 75% of progeny would be expected to have the marker. On the other hand, when one parent has a marker and the other does not (1×0 and 0×1 crosses), 50% of progeny would be expected to have the marker if one parent is heterozygous, or all progeny if the 1 parent is homozygous. Only progenies showing variation for marker presence/absence are suitable for statistical testing. In each cross, the null hypothesis and the expected proportions in the offspring depend on parent phenotypes (see Table S14). Expected proportions for sex-linked markers are summarized in Table S15. Unfortunately, we did not ascertain the embryo progeny sex, but the two former Tables show that, as a whole, the expected frequency of the "band presence" phenotype from every phenotypic cross was about the same for autosomal and sex-linked genes. Goodness-of-fit and heterogeneity chi-square tests were employed to detect significant departures from Mendelian inheritance, and the sequential Bonferroni test was applied to minimize type-I errors.

Supporting Information

Figure S1.

Delta K values with respect to K, according to the calculation method by Evanno et al. [42]. These results were found using the 87 ISSR markers showing no (or low level of) allelic dropout.

doi:10.1371/journal.pone.0059041.s001

(TIF)

Figure S2.

Ancestry of every individual in either of the two groups, using the 87 ISSR markers showing no allelic dropout (or low level thereof), yielded by the Structure software. Each vertical bar represents one of the 255 individuals analyzed. Group 1 (southern region) is represented in red color, and Group 2 (eastern region) is shown in green color. Bar length is proportional to the inferred ancestry values into each group for each individual.

doi:10.1371/journal.pone.0059041.s002

(TIF)

Figure S3.

Delta K values with respect to K, according to the calculation method by Evanno et al. [42]. These results were found using the 46 ISSR markers showing no allelic dropout.

doi:10.1371/journal.pone.0059041.s003

(TIF)

Figure S4.

Ancestry of each individual to either of the two groups, using the 46 ISSR markers showing no allelic dropout, yielded by the Structure software. Each vertical bar represents one of the 255 individuals analyzed. Group 1 is represented in red, and group 2 in green color. Bar length is proportional to the inferred ancestry values into each group for each individual.

doi:10.1371/journal.pone.0059041.s004

(TIF)

Table S1.

Statistical parameters for the different models after Bayesian inference.

doi:10.1371/journal.pone.0059041.s005

(DOC)

Table S2.

Analysis of Molecular Variance (AMOVA).

doi:10.1371/journal.pone.0059041.s006

(DOC)

Table S3.

Likelihood values obtained for calculation of the number of groups (K) best fitting the data through the Evanno method. Reps = replicates.

doi:10.1371/journal.pone.0059041.s007

(DOC)

Table S4.

Pairwise-Fst genetic distances (bottom) and geographical distances in Km (top).

doi:10.1371/journal.pone.0059041.s008

(DOC)

Table S5.

Dice's dissimilarity coefficients between populations (bottom) and standard errors (top).

doi:10.1371/journal.pone.0059041.s009

(DOC)

Table S6.

Failure rate of PCR amplification for every marker (%) under the two controls performed. Dropout values above 30% are marked in bold. N1 and N2 indicate the number of individuals where a given allele was tested for PCR repeatability under controls 1 and 2, respectively.

doi:10.1371/journal.pone.0059041.s010

(DOC)

Table S7.

Inheritance analysis of ISSR markers by means of the goodness-of-fit chi square test. 0 = Absence of the allele, 1 = Presence of the allele, Obs.1 = Observed frequency of 1, exp.1 = Expected frequency of 1. Progeny analyses showing significance after sequential Bonferroni are indicated by an asterisk.

doi:10.1371/journal.pone.0059041.s011

(DOC)

Table S8.

Population genetic parameters found with Hickory (f = 0 model) with 87 loci, i.e. after removing the 10 ISSR markers showing the highest dropout values. Note that hs values are estimates of genetic diversity (panmictic or expected heterozygosity) for each population sample, whereas Hs is the average of hs values for all population samples. Ht is the heterozygosity that would be observed if all population samples would come from a single population.

doi:10.1371/journal.pone.0059041.s012

(DOC)

Table S9.

Difference (%) in the population-genetics parameters estimated with 97 or 46 ISSR markers. Note that hs values are estimates of genetic diversity (panmictic or expected heterozygosity) for each population sample, whereas Hs is the average of hs values for all population samples. Ht is the heterozygosity that would be observed if all population samples would come from a single population.

doi:10.1371/journal.pone.0059041.s013

(DOC)

Table S10.

Proportion of individuals from each population included into each group by Structure for 87 loci and K = 2.

doi:10.1371/journal.pone.0059041.s014

(DOC)

Table S11.

Proportion of individuals from each population included into each group by Structure for 46 loci with K = 2.

doi:10.1371/journal.pone.0059041.s015

(DOC)

Table S12.

Primers employed and number of markers obtained.

doi:10.1371/journal.pone.0059041.s016

(DOC)

Table S13.

Summary of crosses and primers tested.

doi:10.1371/journal.pone.0059041.s017

(DOC)

Table S14.

Expected proportion of marker-carrying progeny for autosomal markers. 1 = marker presence; 0 = marker absence.

doi:10.1371/journal.pone.0059041.s018

(DOC)

Table S15.

Expected proportion of marker-carrying progeny for sex-linked markers. 1 = marker presence; 0 = marker absence.

doi:10.1371/journal.pone.0059041.s019

(DOC)

Author Contributions

Conceived and designed the experiments: MIMP MDLL FP JPMC. Performed the experiments: MIMP. Analyzed the data: MIMP MDLL RG FP JPMC. Contributed reagents/materials/analysis tools: MIMP MDLL RG FP JPMC. Wrote the paper: MIMP MDLL RG FP JPMC.

References

  1. 1. Camacho JPM (2005) B chromosomes. In: Gregory TR, ed. The evolution of the genome.New York: Academic Press. pp 223–286.
  2. 2. Jones RN (1991) B-chromosome drive. Am Nat 137: 430–442. doi: 10.1086/285175
  3. 3. Dirsh VM (1958) Revision of the genus Eyprepocnemis Fieber, 1893 (Orthoptera: Acridoidea). P Roy Entomol Soc Lond B 27: 33–45. doi: 10.1111/j.1365-3113.1958.tb00407.x
  4. 4. López-León MD, Cabrero J, Dzyubenko VV, Bugrov AG, Karamysheva TV, et al. (2008) Differences in ribosomal DNA distribution on A and B chromosomes between eastern and western populations of the grasshopper Eyprepocnemis plorans plorans. Cytogenet Genome Res 121: 260–265. doi: 10.1159/000138894
  5. 5. Cabrero J, López-León MD, Gómez R, Castro AJ, Martín-Alganza A, et al. (1997) Geographical distribution of B chromosomes in the grasshopper Eyprepocnemis plorans, along a river basin, is mainly shaped by non-selective historical events. Chromosome Res 5: 194–198.
  6. 6. Muñoz-Pajares AJ, Martínez-Rodríguez L, Teruel M, Cabrero J, Camacho JPM, et al. (2011) A single, recent origin of the accessory B chromosome of the grasshopper Eyprepocnemis plorans. Genetics 187: 853–863. doi: 10.1534/genetics.110.122713
  7. 7. Herrera JA, López-León MD, Cabrero J, Shaw MW, Camacho JPM (1996) Evidence for B chromosome drive suppression in the grasshopper Eyprepocnemis plorans. Heredity 76: 633–639. doi: 10.1038/hdy.1996.90
  8. 8. Camacho JPM, Shaw MW, López–León MD, Pardo MC, Cabrero J (1997) Population dynamics of a selfish B chromosome neutralized by the standard genome in the grasshopper Eyprepocnemis plorans. Am Nat 149: 1030–1050. doi: 10.1086/286037
  9. 9. Wright S (1951) The genetical structure of populations. Ann Eugen 15: 323–354. doi: 10.1111/j.1469-1809.1949.tb02451.x
  10. 10. Slatkin M (1994) Gene flow and population structure. In: Real L (ed) Ecological Genetics, Princeton University Press, pp. 3–17.
  11. 11. Avise JC (2004) Molecular markers, natural history and evolution. 2nd edn. Sinauer: Sunderland, MA.
  12. 12. Zietkiewicz E, Rafalski A, Labuda D (1994) Genome fingerprinting by simple sequence repeats (SSR)-anchored polymerase chain reaction amplification. Genomics 20: 176–183. doi: 10.1006/geno.1994.1151
  13. 13. Nagaoka T, Ogihara Y (1997) Applicability of inter-simple sequence repeat polymorphisms in what for use as DNA markers in comparison to RFLP and RAPD markers. Theor Appl Genet 94: 597–602. doi: 10.1007/s001220050456
  14. 14. Kantety RV, Zeng X, Bennetzen JL, Zher BE (1995) Assesment of genetic diversity in dent and popcorn (Zea mays L.) inbred lines using intersimple sequences repeated (ISSR) amplification. Mol Breed 1: 365–373. doi: 10.1007/bf01248414
  15. 15. Fernandez ME, Figueiras AM, Benito C (2002) The use of ISSR and RAPD markers for detecting DNA polymorphism, genotype identification and genetic diversity among barley cultivars with known origin. Theor Appl Genet 104: 845–851. doi: 10.1007/s00122-001-0848-2
  16. 16. Cano J, Rezusta A, Sole M, Gil J, Rubio M, et al. (2005) Inter-single- sequence-repeat-PCR typing as a new tool for identification of Microsporum canis strains. J Derm Sci 39: 17–21. doi: 10.1016/j.jdermsci.2005.01.016
  17. 17. Lu Z, Wang Y, Peng Y, Korpelainen H, Li C (2006) Genetic diversity of Populus cathayana Rehd populations in southwestern china revealed by ISSR markers. Pl Sci 170: 407–412. doi: 10.1016/j.plantsci.2005.09.009
  18. 18. Zhang XP, Li XH, Qui YX (2006) Genetic diversity of the endangered species Kirengeshoma palmata (Saxifragaceae) in China. Biochem Syst Ecol 34: 38–47. doi: 10.1016/j.bse.2005.05.007
  19. 19. Ratnaparkhe MB, Tekeoglu M, Muehlbauer FJ (1998) Inter-simple-sequence- repeat ISSR) polymorphisms are useful for finding markers associated with disease resistance gene clusters. Theor Appl Genet 97: 515–519. doi: 10.1007/s001220050925
  20. 20. Datwyler SL, Wolfe AD (2004) Phylogenetic Relationships and morphological evolution in Penstemon subg. Dasanthera (Veronicaceae). Syst Bot 29: 165–176. doi: 10.1600/036364404772974077
  21. 21. Wu W, Zheng YL, Chen L, Wei YM, Yang RW, et al. (2005) Evaluation of genetic relationships in the genus Houttuynia Thunb. in China based on RAPD and ISSR markers. Biochem Syst Ecol 33: 1141–1157. doi: 10.1016/j.bse.2005.02.010
  22. 22. Kojima T, Nagaoka T, Noda K, Ogihara Y (1998) Genetic linkage map of ISSR and RAPD markers in Einkorn wheat in relation to that RFLP markers. Theor Appl Genet 96: 37–45. doi: 10.1007/s001220050706
  23. 23. Pradeep AR, Chatterjee SN, Nair CV (2005) Genetic differentiation induced by selection in an inbred population of the silkworm Bombyx mori, revealed by RAPD and ISSR marker systems. J Appl Genetics 46: 291–298.
  24. 24. Kar PK, Vijayan K, Mohandas TP, Nair CV, Saratchandra B, et al. (2005) Genetic variability and genetic structure of wild and semi-domestic populations of tasar silkworm (Antheraea mylitta) ecorace Daba as revealed through ISSR markers. Genetica 125: 173–183. doi: 10.1007/s10709-005-7002-z
  25. 25. Hundsdoerfer AK, Kitching I, Wink M (2005) The phylogeny of the Hyles euphorbiae complex (Lepidoptera: Sphingidae): Molecular evidence from sequence data and ISSR-PCR fingerprints. Org Divers Evol 5: 173–198. doi: 10.1016/j.ode.2004.11.012
  26. 26. Phillips CB, Cane RP, Mee J, Chapman HM, Hoelmer KA, et al. (2002) Intraspecific variation in the ability of Microctonus aethiopoides (Hymenoptera: Braconidae) to parasitise Sitona lepidus (Coleoptera: Curculionidae). N Z J Agric Res 45: 295–303. doi: 10.1080/00288233.2002.9513519
  27. 27. De Leon JH, Jones WA (2005) Genetic differentiation among geographic populations of Gonatocerus ashmeadi, the predominant egg parasitoid of the glassy-winged sharpshooter, Homalodisca coagulata. J Insect Sci 5: 1–9. doi: 10.1016/j.biocontrol.2006.03.005
  28. 28. Roux O, Gevrey M, Arvanitakis L, Gers C, Bordat D, et al. (2007) ISSR-PCR: Tool for discrimination and genetic structure analysis of Plutella xyllostela populations native to different geographical areas. Mol Phylogenet Evol 43: 240–250. doi: 10.1016/j.ympev.2006.09.017
  29. 29. Vijayan K, Anuradha HJ, Nair CV, Pradeep AR, Awasthi AK, et al. (2006) Genetic diversity and differentiation among populations of the Indian eri silkworm, Samia cynthia ricini, revealed by ISSR markers. J Insect Sci 6: 1–11. doi: 10.1673/2006_6_30.1
  30. 30. Taberlet P, Griffin S, Goosens B, Questiau S, Manceau V, et al. (1996) Reliable genotyping of samples with low DNA quantities using PCR. Nucleic Acids Res 24: 3189–3194. doi: 10.1093/nar/24.16.3189
  31. 31. Tsumura Y, Ohba K, Strauss SH (1996) Diversity and inheritance of inter-simple sequence repeated polymorphisms in Douglas-fir (Pseudotsuga menziesii) and sugi (Cryptomeria japonica). Theor Appl Genet 92: 40–45. doi: 10.1007/bf00222949
  32. 32. Tester DJ, Cronk LB, Carr JL, Schulz V, Salisbury BA, et al. (2006) Allelic dropout in long QT syndrome genetic testing: a possible mechanism underlying false-negative results. Hearth Rhythm 3: 815–821. doi: 10.1016/j.hrthm.2006.03.016
  33. 33. Soulsbury CD, Iossa G, Edwards KJ, Baker P, Harris S (2007) Allelic dropout from a high-quality DNA source. Conserv Genet 8: 733–738. doi: 10.1007/s10592-006-9194-x
  34. 34. Cabrero J, López-León MD, Bakkali M, Camacho JPM (1999) Common origin of B chromosome variants in the grasshopper Eyprepocnemis plorans. Heredity 83: 435–439. doi: 10.1038/sj.hdy.6885960
  35. 35. Perfectti F, Pita M, de la Vega CG, Gosálvez J, Camacho JPM (2004) Spatio-temporal dynamics of a neutralized B chromosome in the grasshopper Eyprepocnemis plorans. Cytogenet Genome Res 106: 376–385. doi: 10.1159/000079315
  36. 36. Holsinger KE, Lewis PO, Dey DK (2002) A Bayesian method for analysis of genetic population structure with dominant marker data. Mol Ecol 11: 1157–1164. doi: 10.1046/j.1365-294x.2002.01512.x
  37. 37. Spiegelhalter DJ, Best NJ, Carlin BP, Van der Linde A (2002) Bayesian measures of model complexity and fit. J Roy Stat Soc B 17: 37–49. doi: 10.1111/1467-9868.00353
  38. 38. Pardo MC, Lopez-Leon MD, Hewitt GM, Camacho JPM (1995) Female fitness is increased by frequent mating in grasshoppers. Heredity 74: 654–660. doi: 10.1038/hdy.1995.89
  39. 39. Evanno G, Regnaut S, Goudet J (2005) Detecting the number of clusters of individuals using the software Structure: a simulation study. Mol Ecol 14: 2611–2620. doi: 10.1111/j.1365-294x.2005.02553.x
  40. 40. Ren Z, Zhu B, Wang D, Ma E, Su D, et al. (2008) Comparative population structure of Chinese sumac aphid Schlechtendalia chinensis and its primary host-plant Rhus chinensis. Genetica 132: 103–112. doi: 10.1007/s10709-007-9153-6
  41. 41. Sesarini S, Remis MI (2008) Molecular and morphometric variation in chromosomally differentiated populations of the grasshopper Sinipta dalmani (Orthopthera: Acrididae). Genetica 133: 295–306. doi: 10.1007/s10709-007-9213-y
  42. 42. Pinedo-Cancino V, Sheen P, Tarazona-Santos E, Oswald W, Jeri C, et al. (2006) Limited diversity of Anopheles darlingi in the peruvian amazon region of Iquitos. Am J Trop Med Hyg 75: 238–245.
  43. 43. Hernandez F, Presa JJ (1984) Sobre la biologia de Eyprepocnemis plorans (Charpentier, 1825) (Orthoptera: Acrididae), en la huerta de Murcia (S.E. Espana). Bol Serv Def Plag Ins Fit 10: 245–249.
  44. 44. Vandergast AG, Lewallen EA, Deas J, Bohonak AJ, Weissman DB, et al. (2009) Loss of genetic connectivity and diversity in urban microreserves in a southern California endemic Jerusalem cricket (Orthoptera: Stenopelmatidae: Stenopelmatus n. sp. ‘‘santa monica’’). J Insect Conserv 13: : 329– 345.
  45. 45. Muñoz E, Perfectti F, Martín-Alganza A, Camacho JPM (1998) Parallel effect of a B chromosome and a mite decreasing female fitness in the grasshopper Eyprepocnemis plorans. P Roy Soc Lond B 265: 1903–1909. doi: 10.1098/rspb.1998.0518
  46. 46. Bonnet E, Van de Peer Y (2002) Zt: A software tool for simple and partial Mantel tests. J Stat Soft 7: 1–12.
  47. 47. Vekemans X, Beauwens T, Lemaire M, Roldan-Ruiz I (2002) Data from amplified fragment length polymorphism (AFLP) markers show indication of size homoplasy and of a relationship between degree of homoplasy and fragment size. Mol Ecol 11: 139–151. doi: 10.1046/j.0962-1083.2001.01415.x
  48. 48. Dice LR (1945) Measures of the amount of ecologic association between species. Ecology 26: 297–302. doi: 10.2307/1932409
  49. 49. Schlüter PM, Harris SA (2006) Analysis of multilocus fingerprinting data sets containing missing data. Mol Ecol Notes 6: 569–572. doi: 10.1111/j.1471-8286.2006.01225.x
  50. 50. Excoffier L, Lischer HEL (2010) Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows. Mol Ecol Resour 10: 564–567. doi: 10.1111/j.1755-0998.2010.02847.x
  51. 51. Saitou N, Nei M (1987) The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol Biol Evol 4: 406–425.
  52. 52. Felsenstein J (1989) PHYLIP: Phylogeny Inference Package (Version 3.2). Cladistics 5: 164–166.
  53. 53. Falush D, Stephens M, Pritchard JK (2007) Inference of population structure using multilocus genotype data: dominant markers and null alleles. Mol Ecol Notes 7: 574–578. doi: 10.1111/j.1471-8286.2007.01758.x
  54. 54. Earl DA, vonHoldt BM (2011) STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Conservation Genet Resour: DOI: 10.1007/s12686-011-9548-7.