Crickets detect the genetic similarity of mating partners via cuticular hydrocarbons

Authors


Melissa L. Thomas, Centre for Evolutionary Biology, School of Animal Biology (M092), The University of Western Australia, Crawley, WA 6009, Australia.
Tel.: +61 8 6488 2239; fax: +61 8 6488 1029; e-mail: melissa.thomas@uwa.edu.au

Abstract

Animals should decipher information about the genetic make-up of conspecifics in order to enhance the fitness benefits associated with mate choice. Although there is increasing evidence to suggest that animals make genetically informed decisions about their mating partners, we understand relatively little about the sensory mechanisms informing these decisions. Here, we investigate whether cuticular hydrocarbons, chemical compounds found on the cuticle of most terrestrial arthropods, provide a means of discerning genetic similarity during mate choice in the cricket, Teleogryllus oceanicus. We found that individuals preferentially mated with partners who share more dissimilar cuticular hydrocarbon profiles and that similarity in cuticular hydrocarbon profiles between mating pairs correlated with their genetic similarity. Our results provide good evidence that cuticular hydrocarbon profiles offer a means of assessing genetic compatibility in T. oceanicus, enabling individuals to choose their most genetically suitable mate.

Introduction

The males of many species exhibit conspicuous and elaborate secondary sexual traits that are thought to arise through male–male competition and/or female mate choice (Darwin, 1871). For example, the good genes hypothesis proposes that females mate with males that have elaborate ornaments because these males pass on to their offspring genes that increase their fitness (Reynolds & Gross, 1992; Petrie, 1994). However, a major theoretical difficulty in understanding mate choice on the basis of good genes is that if particular traits are preferred by all females, then genetic variability in these traits will become exhausted (Kirkpatrick & Ryan, 1991). This paradox has resulted in one of the longest running and most contentious questions in the study of sexual selection (Tomkins et al., 2004; Kotiaho et al., 2008): What is maintaining genetic variance in sexual signals?

An alternative mechanism for female choice that does not suffer the potential for choice to exhaust genetic variation is mate choice based on genetic compatibility (Zeh & Zeh, 1996; Zeh, 1997). Compatibility models assume that to increase the fitness of their offspring, females choose males with genotypes that will complement their own, rather than males that bear the largest or most elaborate signal (Tregenza & Wedell, 2000; Neff & Pitcher, 2005). Under this hypothesis, the most genetically suitable male for one female may not be the best for another, because the genetic quality of offspring will reflect interactions between paternal and maternal genomes (Evans & Marshall, 2005; Pitcher & Neff, 2006; Rodriguez-Muñoz & Tregenza, 2009). Individuals might therefore be expected to choose mating partners who are genetically similar or dissimilar to themselves, depending on how maternal and paternal alleles interact. One form of genetic incompatibility arises when individuals mate with close relatives, because inbreeding increases homozygosity and allows the expression of deleterious recessive mutations that can generate a reduction in fitness, inbreeding depression (Keller & Waller, 2002; Roff, 2002). Individuals may thus prefer mating partners that are genetically dissimilar to themselves and thereby avoid breeding with close relatives (Pusey & Wolf, 1996). Finally, heterozygosity has been shown to have a small but significant effect on fitness (Chapman et al., 2009), and individuals may choose partners that are genetically different to themselves in order to promote heterozygosity at one or a few important fitness loci (Kempenaers, 2007).

Although the benefits of avoiding genetic incompatibility are clear, the mechanisms by which individuals identify compatible mates are less apparent. Current evidence suggests that pheromones may provide a good means of assessing genetic compatibility. In vertebrates, one of the most cited examples of preferences for genetic dissimilarity involves the genes of the major histocompatibility complex (MHC), where discrimination is based almost exclusively on pheromones (see Tregenza & Wedell, 2000 for review). More recently, a study on lemurs has compared genetic heterozygosity to the production of pheromones and found a link between genetic composition and scent-marking behaviour as a potential advertisement of a male’s genetic background (Charpentier et al., 2008, 2010). In invertebrates, an association between pheromone and genetic variation has been found in a number of insect species (e.g. Tsutsui et al., 2003; Dronnet et al., 2006; Van Zweden et al., 2010), and in Drosophila, several genes have been cloned that affect pheromone biosynthesis (reviewed by Ferveur, 2005; Wicker-Thomas & Chertemps, 2010). Here, we investigate not only whether pheromone signals reveal underlying genotype but also whether individuals base their choice of mates on pheromone similarity.

The Australian field cricket Teleogryllus oceanicus is an excellent model species in which to investigate this question. There is some evidence to suggest that female T. oceanicus show differential behaviour based on genetic relatedness; females fertilize their eggs with sperm from nonsibling males rather than full-siblings (Simmons et al., 2006). Further, quantitative genetic analysis of cuticular hydrocarbons indicates that male T. oceanicus have sufficient phenotypic and genetic variation in this trait for females to distinguish kin from nonkin (Thomas & Simmons, 2008a). Cuticular hydrocarbons are the waxy substances found on the exoskeleton of most insect species. Although their original function was thought to be prevention against desiccation, it is now widely accepted that these chemical compounds are used during communication. In crickets, cuticular hydrocarbons have been shown to play an important role in mate recognition (Tregenza & Wedell, 1997; Mullen et al., 2007; Thomas & Simmons, 2009a), and in T. oceanicus both females (Thomas & Simmons, 2009a) and males (Thomas & Simmons, 2010) use cuticular hydrocarbon cues during mate choice. Here, we investigate whether individuals base their choice of mate on how similar their partners cuticular hydrocarbon profiles are to their own and whether cuticular hydrocarbon similarity reflects underlying genetic similarity.

Materials and methods

Experimental animals were the offspring of adult females collected from Carnarvon, north-western Australia. These females would have mated freely in the field with as many as six different males (Simmons & Beveridge, 2010). Thus, the crickets used in this experiment represent a random sample of genotypes drawn directly from the natural population. Individuals were separated into individual containers (7 × 7 × 5 cm) as late instar nymphs. Only adult crickets that were 11 ± 2 days post-final moult were used in experiments. All crickets were maintained, and experiments conducted, in a constant-temperature room, at 25 °C with a 12 : 12 h light/dark cycle. Food and water were provided ad libitum.

Mating trials

To minimize observer disturbance during mating trials, we conducted trials in a room dimly lit by red incandescent lights. We also placed each pair of crickets in a small plastic box (7 × 7 × 5 cm) that was then placed inside a larger plastic box (17 × 12 × 6 cm), which was lined with packing material on three sides to minimize the mating pairs acoustic and visual exposure to other mating pairs under observation. We used 48 males and 48 females that were haphazardly allocated a mating partner. Both males and females were unmated prior to their use in this experiment. Males were purged of their old spermatophores prior to being exposed to their mating partner, thereby forcing them to invest in the production of a fresh spermatophore when in the presence of the experimental female. Spermatophores are discrete vessels containing sperm, which remain attached outside the female following mating (e.g. Simmons, 1986). Males require approximately 60 min to manufacture a spermatophore, upon which they court the female by the production of courtship song in order to encourage the female to mount for spermatophore attachment (Loher & Rence, 1978). We used mating success as our measure of mate choice. A mating was scored as successful when sperm had the opportunity to be transferred to the female. Therefore, pairings in which males failed to produce a spermatophore, did not commence a courtship song, or failed to successfully transfer a spermatophore to females following the female mounting the male were scored as unsuccessful. Pairings in which females failed to mount the courtship singing male were also scored as unsuccessful.

Cuticular hydrocarbon analysis

To quantify cuticular hydrocarbons, we immersed freeze-killed individual crickets in 5 ml of hexane for five minutes, using C32 as an internal standard at a concentration of 0.02 g L−1. We injected 1 μL of this sample into a gas chromatograph and mass spectrometer (GCMS, Agilent GC-6890N, MS-5975 with inert Mass Selective Detector; Agilent Technologies, Santa Clara, California, USA). The GCMS was operated in the splitless mode and fitted with a Stabilwax column (Restek, Bellefonte, Pennsylvania, USA) of 30 m × 0.25 mm internal diameter using helium as a carrier gas (flow 1 mL min−1). The column temperature profile began at an initial temperature of 40 °C for 1 min and was ramped at 20 °C per min to 250 °C for 20 min. The transfer line from the GC to the mass spectrometer was set at 250 °C. We aligned peaks using retention times and fragmentation patterns of compounds. To compare the relative contribution of peaks, we divided the area of each peak within a sample by the signal of the internal standard for that sample. For a more detailed description of the qualitative and quantitative analysis of the hydrocarbon profiles of males and females, see Thomas & Simmons (2008b). We analysed washes derived from 48 females and 48 males.

For data analysis, we computed the Bray–Curtis similarity index between mating pairs based on the relative peak area of all compounds in their hydrocarbon washes. This similarity index can range from zero where individuals have completely different hydrocarbon profiles to one where individuals have the same cuticular hydrocarbon profiles (Quinn & Keogh, 2002). As this similarity index is determined primarily by variables with high values (i.e. compounds with high abundances), we also calculated the Canberra index, which is less influenced by variables with large values, and the Euclidean index (Quinn & Keogh, 2002). Similarity indices were then used to determine whether mating pairs with more similar cuticular hydrocarbon profiles had a lower mating success. All three similarity indices yielded statistically similar results. Similarity indices and confidence intervals on correlation coefficients were calculated using the statistical program R. The R script for confidence intervals is based on equations of Nakagawa & Cuthill (2007).

Genetic similarity

To establish genetic similarity between mating pairs, DNA was extracted from the hind leg of adult crickets using a commercially available EDNA HISPEX™ Tissue kit (Fisher Biotech, Wembley, Western Australia), following the manufacturer’s instructions. DNA samples were then screened using 12 microsatellite loci that have been specifically developed for T. oceanicus (Beveridge & Simmons, 2005; Simmons & Beveridge, 2010). The 12 loci were combined into four multiplexed polymerase chain reactions (PCRs). Each 10-μL reaction contained 1× PCR buffer (10 mm Tris–HCl pH 8.3, 50 mm KCl; Invitrogen, Melbourne, Victoria, Australia), 1.5 mm MgCl2 (3 mm MgCl2 for multiplex 3 only; Invitrogen), 200 μm of each dNTP (Invitrogen), 250 nm of each forward primer, 250 nm of each reverse primer, 0.5 units of Platinum Taq polymerase (Invitrogen) and 1–10 ng DNA. The four multiplexed PCRs contained the following primer combinations: multiplex 1, ToC3-10, ToC3-86 and ToC3-94; multiplex 2, ToC3-8, ToC3-30 and ToC3-93; multiplex 3, Totri54 and Totri59; and multiplex 4, Totri9a, Totri55a, Totri57 and Totri78. PCR amplification was performed in a Mastercycler ep gradient S thermocycler (Eppendorf, North Ryde, NSW, Australia) with cycling conditions as follows: 94 °C for 3 min, then 30 cycles of 94 °C for 30 s, 60 °C (for multiplex 1 and 2) or 55 °C (for multiplex 3 and 4) for 30 s and 72 °C for 1 min, and finally 72 °C for 30 min. The products from the PCR (1.5 μL) were analysed on an ABI3730xl Sequencer, sized using Genescan-500 LIZ internal size standard and genotyped using Genemapper software (version 3.7; Applied Biosystems, Scoresby, Victoria, Australia). Of the 12 loci that were screened, only nine were used in the analysis. Of the three loci that were not used, one was sex linked (C3-10), one produced no PCR product in 94% of individuals (C3-8), and for the remaining locus (C3-93), all individuals but one were found to be homozygous for the same allele.

We calculated the genetic similarity between mating pairs using two methods. Firstly, we used one of the simplest estimates of genetic distance, the proportion of shared alleles (Bowcock et al., 1994). For individual pairwise comparisons, the proportion of shared alleles (PSA) was estimated by: PSA = ∑u S/2u where the number of shared alleles S is summed over all loci u. The genetic distance between individuals (DSA) was then estimated using: DSA = 1 − PSA (Bowcock et al., 1994). As a second measure of genetic similarity, we used a maximum likelihood estimate of genetic relatedness calculated with the software ML-Relate (Kalinowski et al., 2006). This program accommodates microsatellite loci with null alleles by using maximum likelihood estimates of the frequency of null alleles in the calculations. The Monte-Carlo randomization test (Guo & Thompson, 1992), using the U statistic, is used to test for the presence of null alleles in this program. Eight of our nine loci showed a significant heterozygote deficit (null allele frequency; locus 55a = 0.120, 57 = 0.048, 78 = 0.060, 9a = 0.180, 59 = 0.140, C3-30 = 0.244, C3-86 = 0.041, C3-94 = 0.300), which indicates the possible presence of null alleles, or a lack of random mating in the natural population from which mothers of these crickets were drawn. These potential null alleles were accommodated for in the program ML-Relate when calculating estimates of genetic similarity.

Does exposure to mates influence cuticular hydrocarbon profiles?

It is known from studies of Drosophila that exposure to females can result in males changing their cuticular hydrocarbon profiles (Petfield et al., 2005) and vice versa (Thomas, 2011). We performed a separate experiment in which we examined the cuticular hydrocarbon profiles of individual males before and after either a successful mating or an unsuccessful mating attempt. A successful mating was considered to have occurred when (i) males commenced a courtship song, (ii) females mounted the courtship singing male, and (iii) the male successfully transferred a spermatophore to the female. For the unsuccessful mating treatment, steps (i) and (ii) of matings were allowed to progress as per successful matings, but males were not given the opportunity to progress to step (iii) and thus failed to transfer a spermatophore to females. Finally, we performed an experiment in which we examined the hydrocarbon profiles of individual females before and after a successful mating.

For these experiments, cuticular hydrocarbon profiles of individuals were measured twice using solid-phase microextraction: once immediately prior to the mating trials (time 1) and once 86 ± 22 min following the mating trials (time 2). Solid-phase microextraction involves solventless recovery and concentration of substances on silica fibres and can therefore be repeated on the same animal. Solid-phase microextraction was performed using a polydimethylsiloxane (PDMS) 100-μm Supelco fibre. Prior to sampling, the fibre was cleaned twice by injection into the GC at 250 °C for 5 min using the splitless mode. Following cold anesthetization of crickets (30 min at 4 °C), the full length of the fibre was rubbed softly one way along the principle parts of the cricket’s body (head, thorax, wings, abdomen). This was repeated ten times with the fibre being slightly rotated between rubs. Solid-phase microextraction samples were analysed on the same GCMS as extract samples, with a column temperature profile that began at a temperature of 150 °C for 1 min and was ramped at 8 °C per min to 250 °C for 10 min. For full details of this method, see Thomas & Simmons (2011). We analysed 96 profiles derived from 48 males (25 unsuccessful and 23 successful), and 38 profiles from 19 females.

For data analysis, peaks were labelled by peak number, which corresponded to their retention times. Hydrocarbon profiles of each cricket consisted of the relative abundances (peak areas) of individual compounds (23 male and 30 female compounds). These compositional data sets were transformed to logcontrasts (using peak 5 as the divisor for males and peak 7 as the devisor for females), as described previously for T. oceanicus cuticular hydrocarbon data (Thomas & Simmons, 2009b,c).

Results

Of the 48 mating pairs, 34 mated successfully, whereas the remaining 14 were unsuccessful. The mating success of randomly assigned pairs was associated with the similarity of their cuticular hydrocarbon profiles; pairs that did not mate shared more similar cuticular hydrocarbon profiles than pairs that did (anova: Bray–Curtis index, F1,46 = 4.256, P = 0.045 (Fig. 1); Euclidean, F1,46 = 4.287, P = 0.044; Canberra, F1,46 = 4.392, P = 0.042). Mating success was also associated with genetic relatedness; pairs that did not mate successfully were more genetically similar than pairs that did (anova, mean ± SE: genetic relatedness, F1,46 = 9.414, P = 0.036, unsuccessful = 0.144 ± 0.035, successful = 0.018 ± 0.022; DSA, F1,46 = 4.842, P = 0.033, unsuccessful = 0.725 ± 0.036, successful = 0.819 ± 0.023). We also undertook separate analyses on two different classes of compounds, alkenes and alkynes, but found that mating success of pairs was not associated with the similarity of a specific class of hydrocarbons (anovas: Fs < 3.769, Ps > 0.06, for all similarity indices), suggesting that it is the relative combination of the entire suite of compounds that is important for this type of discrimination.

Figure 1.

 Mean (± 95% confidence intervals) cuticular hydrocarbon similarity of successful (n = 34) and unsuccessful (n = 14) mating pairs. Cuticular hydrocarbon similarity is represented by the Bray–Curtis index. Small values indicate more dissimilar cuticular hydrocarbon profiles between mating pairs, and larger values indicate more similar cuticular hydrocarbon profiles.

The similarity of the mating pair’s cuticular hydrocarbon profiles was significantly correlated with their genetic distance (Pearson’s correlation with DSA; Bray–Curtis, R = −0.456, n = 48, P = 0.001; Euclidean, R = −0.425, n = 48, P = 0.003; Canberra, R = −0.497, n = 48, P < 0.001); mating pairs that were more genetically distinct displayed more dissimilar cuticular hydrocarbon profiles (Fig. 2). Removing the three very distantly related mating pairs (see Fig. 2) from the analysis reduced the significance (Pearson’s correlation with DSA; Bray–Curtis, R = −0.284, n = 45, P = 0.058; Euclidean, R = −0.269, n = 45, P = 0.074; Canberra, R = −0.248, n = 45, P = 0.101); however, the effect size was not influenced by their exclusion; the 95% confidence intervals on the Pearson’s correlation coefficient overlapped when these three data points were included (95% CI; Bray = −0.633 to −0.199; Euclidean = −0.611 to −0.160; Canberra = −0.6614 to −0.250) or excluded (95% CI; Bray = −0.518 to 0.005; Euclidean = −0.504 to 0.026; Canberra = −0.487 to 0.050). Similar results were obtained when genetic similarity was calculated in ML-Relate (Pearson’s correlation with ML-Relate; Bray–Curtis, R = 0.400, n = 48, P = 0.005; Euclidean, R = 0.365, n = 48, P = 0.011; Canberra, R = 0.512, n = 48, P < 0.001). Again, removing the three most distantly related pairs rendered these correlations nonsignificant (Pearson’s correlation; Bray–Curtis, R = 0.187, n = 45, P = 0.218; Euclidean, R = 0.155, n = 45, P = 0.309; Canberra, R = 0.186, n = 45, P = 0.222).

Figure 2.

 Relationship between genetic distance (DSA) and the Bray–Curtis index of similarity in cuticular hydrocarbon profiles between mating pairs. Mating pairs that displayed more similar cuticular hydrocarbon profiles were less genetically distinct from each other. Dotted lines represent 95% confidence intervals on the slope.

Finally, some compounds in the cuticular hydrocarbon profiles of these crickets exhibit sexual dimorphism, being present only in females (Thomas & Simmons, 2008b). We re-calculated our estimators of hydrocarbon similarity excluding peaks that occur only in females and re-analysed the correlations between genetic and hydrocarbon similarity. These analyses returned quantitatively similar results (Pearson’s correlation with DSA; Bray–Curtis, R = −0.448, n = 48, P = 0.001; Euclidean, R = −0.426, n = 48, P = 0.003; Canberra, R = −0.476, n = 48, P < 0.001: Pearson’s correlation with ML-Relate; Bray–Curtis, R = 0.394, n = 48, P = 0.006; Euclidean, R = 0.363, n = 48, P = 0.011; Canberra, R = 0.505, n = 48, P < 0.001). These results suggest that cuticular hydrocarbon similarity between pairs can serve as a cue for individuals to detect genetic similarity during mate choice.

Does exposure to mates influence cuticular hydrocarbon profiles?

To determine whether crickets change their hydrocarbon profile after exposure to mates, we first conducted principal component analyses using the peak areas from samples of each individual cricket before and after the experimental manipulation. We then calculated the change in a cricket’s CHC profile as the score on each PC prior to exposure to a mate minus the score on that PC after exposure. For males, the principal component analysis resulted in eight eigenvalues greater than one that collectively explained 77.56% of the variation in the CHC profiles. The percentages of variance explained were 28.08, 18.27, 9.23, 5.60, 5.15, 4.65, 3.63 and 2.97 for components 1–8, respectively. The mean change in PC scores following mating did not differ significantly from zero (t47 for PCs 1–8: −0.166, 0.272, 0.875, 0.408, 0.629, −1.388, 0.204, −0.532; Ps > 0.10).

For females, the principal component analysis resulted in five eigenvalues greater than one that collectively explained 88.7% of the variation in the CHC profiles. The percentages of variance explained were 44.0, 21.6, 12.8, 6.5 and 3.8 for PCs 1–5, respectively. The mean change in PC scores following mating did not differ significantly from zero (t18 for PCs 1–5: 0.68, 1.23, 0.18, 2.09, 0.22; Ps ≥ 0.051). PC4 showed the strongest deviation from zero but this was far from the family-wise critical P0.05/5 = 0.01. This PC was loaded most heavily by the shortest (C29) and longest (C35 and C37) hydrocarbons that are sexually dimorphic, being found predominantly or only in females (Thomas & Simmons, 2008b). As such, any change in these CHCs following mating could not account for the similarity between females and their mates. Indeed, as shown above, removing female specific peaks from our analyses yielded quantitatively similar results.

Discussion

By integrating chemical and genetic data, we provide evidence that the cuticular hydrocarbon profiles of male and female T. oceanicus contain olfactory information of underlying genetic similarity. Convergence in olfactory profiles between relatives has been reported previously in a number of species (e.g. Tsutsui et al., 2003; Dronnet et al., 2006; Boulet et al., 2009) and has been termed the ‘odour–genes covariance’ (Heth & Todrank, 2000). In T. oceanicus, we have previously shown, within a quantitative genetic framework, that male cuticular hydrocarbon profiles exhibit considerable additive genetic variance and that discriminant analysis based on hydrocarbon profiles could classify correctly all but 2% of individuals to their full-sibling families (Thomas & Simmons, 2008a). The predictable relationship between individual genotype and individual odour could enable animals to assess their degree of genetic relatedness to other individuals by comparing the degree of similarity between another individual’s odour and their own. Indeed, we found that pairs of T. oceanicus were more likely to mate when they shared more dissimilar odour profiles. This result is unlikely to be driven by phenotypic plasticity in hydrocarbon profiles, because we found no significant change in cuticular hydrocarbon profile within individual males or females after they were exposed to mating partners. Our data provide good evidence that cuticular hydrocarbon profiles offer a means of assessing genetic compatibility in T. oceanicus, enabling individuals to choose their most genetically suitable mate.

Compatibility models assume that to increase the fitness of their offspring, individuals choose partners with genotypes that will complement their own, rather than those that bear the largest or most elaborate signal (Tregenza & Wedell, 2000; Neff & Pitcher, 2005). Although mate choice may be exercised by either sex, empirical studies investigating compatibility models generally focus on female mating preferences, with few studies investigating or controlling for male mating preferences (Gillingham et al., 2009). Although our study was not designed to test the relative contributions of male and female mate choice to a pair’s mating success, we were able to attribute male mate choice to ten of our fourteen unsuccessful pairings: three unsuccessful matings were due to the male not producing a spermatophore for his allocated female, whereas seven were due to the male failing to produce a courtship song. In most populations of T. oceanicus, courtship song appears to be essential to induce mounting by females (Libersat et al., 1994; Balakrishnan & Pollack, 1996). One reason why male T. oceanicus should be hesitant to mate with closely related females is a paternity bias towards unrelated males that is found in this (Simmons et al., 2006) and other species of crickets (Stockley, 1999; Bretman et al., 2004). This paternity bias is most likely due to post-copulatory sperm–female interactions (Bretman et al., 2009).

Although we had no cases in which we could unequivocally allocate a failed mating to female choice, it would be unwise to conclude that there was a lack of female precopulatory mate choice in our study. In all four of the remaining unsuccessful matings, females mounted males, but the spermatophore was not successfully transferred. This failure could be due to mechanical issues with the male or refusal of the female to accept the spermatophore. Like males, there are reasons why female T. oceanicus should also be hesitant to mate with closely related partners. Female crickets that only mate with siblings have been reported to produce eggs with a reduced hatching success (Tregenza & Wedell, 2002; Jennions et al., 2004) and reduced fecundity in adult female offspring (Roff, 1998; Roff & DeRose, 2001). In T. oceanicus, inbred sons have reduced competitive fertilization success and inbred daughters have reduced body mass and fecundity (Simmons, 2010). Moreover, when mated to both a sibling and an unrelated male, females produce relatively more outbred offspring (Tregenza & Wedell, 2002; Simmons et al., 2006), indicating that females exercise post-copulatory inbreeding avoidance.

Theoretically, inbreeding is predicted to have asymmetric consequences for the two sexes owing to the relative disparities in mating and parenting investments of males and females (Parker, 2006). Work with red jungle fowl is consistent with this theoretical framework, revealing counteracting sex-specific responses to inbreeding; male jungle fowl ejaculate more sperm when mating with their sisters, whereas females retain fewer sperm following insemination by brothers (Pizzari et al., 2004). In contrast, work on the cockroach Blattella germanica demonstrates mutual mate choice responses to inbreeding; males preferentially court nonsibling females, whereas females also bias their choice of mates towards nonsiblings (Lihoreau et al., 2008). In T. oceanicus, although males and females appear to differ in their post-copulatory responses to inbreeding (Simmons & Thomas, 2008), our study provides evidence of male precopulatory inbreeding avoidance through preferential matings with conspecifics that are more genetically dissimilar. Our study therefore highlights the importance of considering not only both pre- and post-copulatory mechanisms but also male selectivity as a confounding variable in studies investigating female mate choice and inbreeding avoidance.

Although it is clear from our study that cuticular hydrocarbons can provide a mechanism by which individuals distinguish genetic similarity, the manner by which these chemical compounds come to reflect an individual’s genome remains unclear. In vertebrates, olfactory profiles that underlie conspecific recognition are known to be influenced by gene families such as the major histocompatibility complex (MHC) or mouse urinary proteins (MUPS; Willse et al., 2005, 2006; Sherborne et al., 2007), although the pathway from these gene families to individual odour profiles has not been completely resolved. In invertebrates, genetic polymorphisms in the enzymes involved in the biosynthesis of semiochemicals have been implicated. For example, olfactory diversity could result from the action of desaturases (Takahashi et al., 2001; Roelofs & Rooney, 2003) or elongase (Chertemps et al., 2007) that modify hydrocarbon-based semiochemicals. Certainly, a deeper understanding of the odour–genes covariance demands not only more studies such as ours, which investigate the ultimate function of this covariance, but also studies that integrate the proximate mechanisms.

Acknowledgments

We thank Ricarda Fenske for help with GCMS analysis, Maxine Beveridge for help with cricket maintenance and Bob Black and Jason Kensington for statistical advice. This study was supported by funding from the Australian Research Council, the University of Western Australia and the West Australian Centres of Excellence in Science and Innovation Program.

Ancillary