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O R I G I NA L A RT I C L E doi:10.1111/j.1558-5646.2010.00958.x THE QUANTITATIVE GENETICS AND COEVOLUTION OF MALE AND FEMALE REPRODUCTIVE TRAITS Rhonda R. Snook,1,2 Leonardo D. Bacigalupe,1,3,4 and Allen J. Moore5,6 1 5 Department of Animal and Plant Sciences, University of Sheffield, Sheffield S10 2TN, United Kingdom 2 E-mail: [email protected] 4 E-mail: [email protected] Centre for Ecology and Conservation, School of Biosciences, University of Exeter, Cornwall Campus, Penryn TR10 9EZ, United Kingdom 6 E-mail: [email protected] Received August 3, 2009 Accepted December 18, 2009 Studies of experimental sexual selection have tested the effect of variation in the intensity of sexual selection on male investment in reproduction, particularly sperm. However, in several species, including Drosophila pseudoobscura, no sperm response to experimental evolution has occurred. Here, we take a quantitative genetics approach to examine whether genetic constraints explain the limited evolutionary response. We quantified direct and indirect genetic variation, and genetic correlations within and between the sexes, in experimental populations of D. pseudoobscura. We found that sperm number may be limited by low heritability and evolvability whereas sperm quality (length) has moderate V A and CV A but does not evolve. Likewise, the female reproductive tract, suggested to drive the evolution of sperm, did not respond to experimental sexual selection even though there was sufficient genetic variation. The lack of genetic correlations between the sexes supports the opportunity for sexual conflict over investment in sperm by males and their storage by females. Our results suggest no absolute constraint arising from a lack of direct or indirect genetic variation or patterns of genetic covariation. These patterns show why responses to experimental evolution are hard to predict, and why research on genetic variation underlying interacting reproductive traits is needed. KEY WORDS: Experimental evolution, female reproductive tract, genetic architecture, indirect genetic effects, interacting phenotype, quantitative genetics, sperm. The evolutionary trajectories of male and female reproductive traits, including coevolution between them, are potentially affected by the postcopulatory sexual selection processes of sperm competition and cryptic female choice (for reviews see: Eberhard 1996; Simmons 2001; Pitnick et al. 2009a,b; Pizzari and Parker 2009). For example, both sperm quantity (Simmons 2001; Pizzari and Parker 2009) and quality (Snook 2005) are im3Current address: Instituto de Ecologı́a y Evolución, Facultad de Ciencias, Universidad Austral de Chile, Casilla 567, Valdivia, Chile. C 1926 portant determinants of male fertilization success. There is also substantial evidence of coevolution between sperm and the morphology of the female reproductive tract (for review see Pitnick et al. 2009a) such that these general classes of traits are an interacting phenotype in which male reproductive success depends on the social environment; that is, the rival male sperm phenotypes and the female reproductive tract phenotype (Moore et al. 1997; Moore and Pizzari 2005; Simmons and Moore 2009). The importance of an interaction is demonstrated by recent studies showing that male reproductive success during postcopulatory selection is C 2010 The Society for the Study of Evolution. 2010 The Author(s). Journal compilation Evolution 64-7: 1926–1934 M A L E A N D F E M A L E C O E VO L U T I O N nontransitive. Within a male genotype, the outcome of sperm competition is not repeatable if the rival male and/or female is altered (e.g., Clark et al. 2000; Bjork et al. 2007; Tregenza et al. 2009). Defining ejaculates as interacting phenotypes is valuable because such phenotypes influence the direction, magnitude, and rate of evolution along with the underlying genetic architecture of the focal traits differently than noninteracting traits (Moore et al. 1997; Simmons and Moore 2009). Experimental evolution, in which populations are allowed to evolve under manipulated mating systems or social interactions, has been used to test the patterns of ejaculate investment as a consequence of variation in the intensity of sexual selection (Snook et al. 2009). Experimental evolution should be especially effective for interacting phenotypes and traits targeted by sexual selection, as these are among those expected to respond most quickly and dramatically (West-Eberhard 1979, 1983; Moore et al. 1997). Experimental sexual selection studies allows evolution to operate within a species in multivariate space (i.e., without targeting any specific trait a priori, and without limiting the traits that may be targeted) and can be replicated, providing a more complete test of the traits that sexual selection consistently targets. Snook et al. (2009) review studies in which either the population sex ratio and/or population density is manipulated to establish treatments in which either monogamy is enforced with random mate assignment (which eliminates all forms of sexual selection) or the intensity of sexual selection is increased through male-biased sex ratio populations or large population sizes (which exaggerates pre- and postcopulatory sexual selection). Using this experimental protocol, males subjected to increased intensity of sexual selection treatment are predicted to invest more in sperm quantity and/or quality compared with males from the enforced monogamy treatment and to garner greater reproductive success as a consequence. Perhaps surprisingly, such experimental sexual selection studies have provided mixed results on the consistency with which sperm and ejaculate traits have been targeted (Hosken et al. 2001; Pitnick et al. 2001; Wigby and Chapman 2004; Crudgington et al. 2009). The unanswered question is why there should be unpredictable results with respect to sperm evolution. Patterns of genetic (co)variation can constrain evolution, but these are rarely studied with respect to reproductive traits and experimental evolution. There are multiple points where genetic variation could influence and constrain sperm evolution. The most fundamental is a lack of additive genetic variation (Blows and Hoffmann 2005). However, in a recent review of quantitative genetics studies on ejaculate evolution, the coefficients of additive genetic variation (CV A s; “evolvability,” Houle 1992) of sperm number (e.g., testes size) was relatively high compared to other ejaculate traits, such as sperm viability and sperm length, traits that also may influence male fertilization success (Simmons and Moore 2009). These results suggest that testes size may evolve more readily than sperm length. Genetic correlations between traits within a sex could also constrain evolution. For example, in the cockroach Nauphoeta cinerea, sperm number was genetically negatively associated with testes mass (Moore et al. 2004), a result that would not have been predicted a priori. Such significant genetic covariances between traits within a functional unit may limit responses to selection but we have no information on these general patterns. Indirect genetic effects may also matter. If the female environment influences the evolution of sperm (e.g., Pitnick et al. 2009a), a lack of genetic variation for female reproductive tract traits reduces indirect genetic effects and could slow sperm evolution. To date only one experimental sexual selection study has shown that changes in the intensity of sexual selection alter the female reproductive tract (Hosken et al. 2001) but this study did not examine the genetic architecture of these traits. Genetic correlations between the sexes may also constrain the evolution of reproductive traits. For example, artificial selection on the primary female sperm storage organ in Drosophila melanogaster, the ventral (seminal) receptacle, found that males from the elongated receptacle line showed a weak correlated change in sperm length (Miller and Pitnick 2002). A quantitative genetics study in the dung beetle, Onthophagus taurus, found that sperm length and spermathecal size are negatively genetically correlated whereas sperm length and spermathecal shape are not genetically correlated (Simmons and Kotiaho 2007). The paucity of quantitative genetics studies on such male and female traits means that we cannot develop generalizations regarding patterns of genetic architecture for the interacting phenotype that is reproduction. Such studies are needed to understand constraints and limitations to selection, particularly for experimental evolution. Here, we examine the genetic architecture of interacting male and female reproductive traits to test the extent to which evolution is constrained by the genetic (co)variance of sperm number and length and the morphology of the female reproductive tract. We perform this study in Drosophila pseudoobscura that has been subjected to experimental sexual selection and in which sperm quantity and quality have not responded to this selection (Crudgington et al. 2009). Understanding the genetic architecture of the ejaculate and female reproductive tract in this species is also of interest because D. pseudoobscura is sperm heteromorphic. Males produce two distinct sperm morphologies each having a specific role during fertilization; long sperm that fertilize eggs (Snook et al. 1994; Snook and Karr 1998; Snook and Markow 2001) and short sperm that protect their brother fertilizing sperm from the effects of female spermicide (Holman and Snook 2008). Sperm heteromorphism is widely distributed across taxa (Till-Bottraud et al. 2005) yet there have been no quantitative genetic analyses of this phenomenon. Such a study will inform our understanding of the extent of heritable genetic variance for sperm heteromorphism, and whether, and the extent to which, EVOLUTION JULY 2010 1927 R H O N DA R . S N O O K E T A L . trade-offs between either sperm numbers of each type, sperm size of each type, or sperm size and number constrain the evolution of this phenomenon. Moreover, by concurrently incorporating the genetic (co)variance with the female reproductive tract, we can elucidate the extent to which females might influence the evolution of this phenomenon. Materials and Methods EXPERIMENTAL REMOVAL AND ELEVATION OF SEXUAL SELECTION We manipulated opportunities for sexual selection by establishing three experimental evolution treatments: (1) enforced monogamy (M: one female and one male), (2) control promiscuity (C: one female and three males), and (3) elevated promiscuity (E: one female and six males). The establishment and maintenance of these lines has previously been described in full in Crudgington et al. (2005; 2009). Importantly, we have successfully controlled for variation in effective population size by increasing the number of families in the M treatment relative to C and E (Crudgington et al. 2005; Bacigalupe et al. 2008; Snook et al. 2009). BREEDING DESIGN We used a standard paternal half-sibling breeding design to quantify the genetic variance–covariance matrices for reproductive traits in replicate one of our experimentally evolved populations. Previous work (Crudgington et al. 2009) has shown that experimental evolution has not resulted in changes in male sperm traits and so we used individuals from a single replicate of each population (E, M, and C) and included experimental treatment as a fixed effect in a mixed model design in all of our analyses. Treatment was significant for many traits given the sample sizes of our studies, but there was no pattern as to which line differed for each trait and, as Crudgington et al. (2009) show, no biological importance of this effect. For each treatment, virgin flies used as parents in the breeding design were collected by CO 2 anesthesia and housed in single-sex groups, 10 flies per food vial for five days until reproductive maturity. At maturity, a breeding design was implemented in which sires were each housed with three dams for 24 h. The following day, sires were discarded and each female was transferred to an individual fresh food vial for five days to allow oviposition, after which they were also discarded. We set up 20 sires per treatment, for a total of 60 sires and 180 dams. On emergence, up to 10 randomly selected daughters and sons per dam were collected and housed together in single-sex vials for five days until reproductive maturity. After that period, daughters were frozen at −20◦ C and sons were mated with random virgin females from the same treatment population (see below). Males were then frozen (for wings), and sperm collected from their mates (see below). Morphological reproductive traits were measured on four sons and four daughters 1928 EVOLUTION JULY 2010 randomly chosen per dam. Because of logistic reasons, we spread out the number of paternal half-sibling families implemented across seven months representing flies collected from generations 45–51 of our selection study to contribute individuals that formed our breeding design. Flies from these collections were randomly mated to produce the sires and dams used. For each generation, three half-sibling families per treatment were set up. Therefore generation was also entered as a fixed effect in the model. REPRODUCTIVE TRAITS Male sperm numbers and length To measure sperm number and length, sons were mated to virgin unrelated females from the same experimental sexual selection treatment and the sperm within the females subsequently dissected for analysis. Following mating, sons were individually housed in fresh vials for 24 h and subsequently frozen at −20◦ C. Drosophila pseudoobscura is sperm heteromorphic, meaning males produce two types of sperm, short and long. We measured both sperm types as previously described (Crudgington et al. 2009) with mated females being ether-anaesthetized 2–4 h after mating and subsequently processed for sperm dissections. We controlled for any potential scaling effects by quantifying wing size (wing vein IV), a surrogate of body size, as previously described (Crudgington et al. 2009). Female reproductive tract morphology The reproductive tract of Drosophila consists of two types of sperm storage organs; two paired mushroom-shaped spermathecae and a ventral (seminal) receptacle that is an elongate blind-end sac. The reproductive tracts of female offspring from the halfsibling design were each dissected in 20 μL phosphate-buffered saline solution (PBS) on a glass slide. A glass coverslip was placed over the sample and a digital picture of one spermatheca was captured at 400× magnification (Leica DMLB Optics, Leica Microsystems, Wetzlar, Germany). This procedure allowed us to get intact spermathecae; we did not measure any spermatheca that was ruptured. To capture images of the ventral receptacle, slight pressure was applied to the corners of the coverslip, which allowed flattening of both structures without stretching them (Miller and Pitnick 2002; Holman et al. 2008). A digital image of the ventral receptacle was also captured at 200× magnification. Spermathecal area and ventral receptacle length (from its blind-end to the point where it joins the uterus) were obtained using the public software Image-J (http://rsb.info.nih.gov/ij/). A single researcher carried out all observations. We evaluated potential scaling effects on female reproductive tracts by measuring wing vein IV as described for males. QUANTITATIVE GENETIC ANALYSIS We analyzed our data with ASREML. We obtained the different variance components in the full model and nested submodel for M A L E A N D F E M A L E C O E VO L U T I O N each trait separately and between them (i.e., univariate and bivariate analysis, respectively) to determine significance of variance components. Nested submodels were obtained by constraining a variance (univariate) or covariance (bivariate) of the full model to zero, which gives a new likelihood value. The statistical significance was assessed through likelihood ratio tests (LRTs) between the models. The asymptotic null distribution of this test is a chisquare with one degree of freedom (i.e., the number of parameters constrained to zero in the nested submodel). Genetic correlation between the sexes Genetic correlations between sex-limited traits were estimated using the same procedure as cross-environmental genetic correlations, treating “sex” as an environment (Astles et al. 2006). We used ASREML as above to calculate variances, covariances, and significance. Results MALE TRAITS The sample sizes, means, and SD of the four male traits are presented in Table 1. Wing size and the lengths of both sperm types all had moderate-to-high narrow-sense heritabilities (Table 2a). The narrow-sense heritabilities of the numbers of both sperm types were low. Evolvabilities, calculated as CV A of each trait, followed a similar pattern to that of heritability. Coefficients of additive genetic variation were modest for wing size and the lengths of long and short sperm, but very low for the numbers of both sperm types. There were relatively few significant phenotypic and genetic correlations among male traits (Table 3a). The only significant genetic correlations were a negative correlation between the length of long sperm and wing length, a positive correlation between length of short sperm and wing length, and a positive correlation between numbers of both sperm types. This does not suggest any pattern of functional integration or obvious constraints arising from genetic correlations among any of the male traits. Of course, correlations between numbers of sperm and other traits would have low power given the low heritabilities of these traits. FEMALE TRAITS To ensure that we were justified in performing our analysis on the combination of the lines, we first compared our two most extreme lines, E and M, for response to experimental sexual selection. There was no significant difference between treatments for the size of the spermathecal area (F 1,504 = 0.8632, P = 0.3533) or the ventral receptacle length (F 1,472 = 0.6341, P = 0.4262). Therefore, as with sperm traits, we analyze and present combined quantitative genetic data for female traits across all treatments. Unlike sperm traits, there are no previous quantitative genetic studies examining differences in female reproductive tract in response to experimental evolution. The sample sizes, means, and SD for the three female traits are presented in Table 1. Females are somewhat larger than males but not more variable. A significant component of the variation in female traits reflects additive genetic variation (Table 3b), with very high and significant heritabilities for wing size and spermathecal area, and moderate and significant heritability for ventral receptacle length. Evolvability was modest for wing size and receptacle length and very low for spermathecal area. In contrast to male traits, female reproductive morphology showed reasonable integration with significant positive phenotypic and genetic correlations among all traits (Table 4b). Thus bigger females would be expected to have bigger reproductive tracts, and selection for larger size could result in correlated changes in the same direction in reproductive tract traits. INTERSEX CORRELATIONS Intersex genetic correlations suggest few shared genetic influences between male and female reproductive morphology. There were few significant genetic correlations (Table 4). There was a significant genetic correlation between female size and length of Sample sizes and descriptive data for male and female traits. All analyses included 60 sires mated to three dams each. Total number of offspring measured varied for each trait and is provided. Table 1. Sex Trait Offspring Mean SD Males Wing (mm) Number of short sperm Number of long sperm Length of short sperm (μm) Length of long sperm (μm) 689 712 712 716 715 2.11 20,583.10 10,983.40 91.58 314.81 0.07 6901.79 3117.99 11.68 12.96 Wing (mm) Spermathecal area (μm2 ) Length of ventral receptacle (μm) 779 764 708 2.36 5264.26 683.39 0.06 523.27 77.03 Females EVOLUTION JULY 2010 1929 R H O N DA R . S N O O K E T A L . Table 2. Additive genetic variances (V A ), phenotypic variances, heritabilites (h2 ), coefficient of additive genetic variation (CV A ), and residual coefficient of variation (CV R ) for (A) male traits and (B) female traits. Significance determined by a likelihood ratio test and denoted by bold (P < 0.05). Asymptotic standard errors are presented in parenthesis. Variance estimators for sperm numbers were obtained from raw data divided by 100. This procedure leaves unchanged estimators as a proportion of phenotypic variances. (A) Male reproductive traits VA VP h2 (SE) CV A CV R 0.0022 773.79 107.87 44.51 73.73 0.0026 3532.00 963.72 121.20 143.74 0.85 (0.23) 0.22 (0.15) 0.11 (0.14) 0.37 (0.14) 0.51 (0.20) 2.22 0.135 0.095 7.28 2.73 0.95 0.255 0.266 9.56 2.66 Trait VA VP h2 (SE) CV A CV R Wing (mm) Spermathecal area (μm2 ) Length of ventral receptacle (μm) 0.0023 15.89 988.86 0.0024 23.59 4850.97 0.93 (0.25) 0.67 (0.18) 0.20 (0.12) 2.03 0.076 4.60 0.42 0.053 9.09 Wing (mm) Number of short sperm Number of long sperm Length of short sperm (μm) Length of long sperm (μm) (B) Female reproductive traits long and short sperm, with an identical pattern to the relationship between male size and these traits. There was a significant negative correlation between spermathecal area and length of long sperm. No other genetic correlations were significant, although the correlation between female size and number of short sperm approached significance. Surprisingly, we found no genetic correlation between male and female size indicating they could evolve independently. Discussion Sexual selection theory predicts, and both macro- and microevolutionary studies generally support, that males should invest in the quantity and/or quality of sperm. Yet, experimental evolution studies provide mixed results on the response of these traits when selection operates in a multivariate environment (Crudgington et al. 2009). For example, neither long nor short sperm numbers, nor lengths, respond to differences in the intensity Phenotypic correlations (above the diagonal) and genetic correlations (below the diagonal) for (A) male traits and (B) female traits. Significant estimators are denoted in bold. Level of significance of pairwise phenotypic correlations are provided parenthetically below the estimate. Significance of genetic correlations (P<0.05) is determined by a likelihood ratio test in ASREML and denoted by bold. Table 3. SE of genetic correlations is given in parentheses below estimates. (A) Phenotypic and genetic correlations among male reproductive traits Wing (mm) Wing (mm) Number of short sperm Number of long sperm Length of short sperm (μm) Length of long sperm (μm) 0.18 (0.30) 0.42 (0.31) 0.40 (0.18) −0.40 (0.19) Number of short sperm Number of long sperm 0.27 (<0.001) 0.68 (0.31) 0.46 (0.30) −0.07 (0.30) −0.01 (0.775) 0.45 (<0.001) −0.10 (0.30) −0.32 (0.35) Length of short sperm (μm) −0.04 (0.365) 0.04 (0.232) −0.04 (0.292) 0.14 Length of long sperm (μm) −0.06 (0.090) 0.13 (<0.001) −0.08 (0.040) 0.13 (<0.001) - (B) Phenotypic and genetic correlations among female reproductive traits Wing (mm) Spermathecal area (μm2 ) Length of ventral receptacle (μm) 1930 EVOLUTION JULY 2010 Wing (mm) Spermathecal area (μm2 ) Length of ventral receptacle (μm) 0.39 (0.16) 0.29 (0.17) 0.24 (<0.001) 0.53 (0.16) 0.10 (<0.001) 0.24 (<0.001) - M A L E A N D F E M A L E C O E VO L U T I O N Table 4. Genetic correlations between traits across the sexes determined by ASREML. Significance of genetic correlations (P<0.05) is determined by a likelihood ratio test in ASREML. SE of genetic correlations is given in parentheses below estimates. Female traits Wing (mm) Male traits Wing (mm) Number of short sperm Number of long sperm Length of short sperm (μm) Length of long sperm (μm) 0.20 (0.18) 0.45 (0.28) 0.32 (0.28) 0.49 (0.16) −0.39 (0.18) of postcopulatory sexual selection in D. pseudoobscura (Crudgington et al. 2009). Here, we examined the genetic parameters that might influence the evolution of sperm investment in this sperm heteromorphic species and test the hypothesis that experimental evolution is constrained by patterns of genetic variation. Importantly, we also considered sperm evolution from an interacting phenotype perspective and quantify the indirect genetic variation of the female reproductive tract and the genetic correlations between the sexes. There does not appear to be much genetic constraint to sperm evolution in our D. psuedoobscura populations. Overall, we found that sperm and its components could evolve independently both within and between the two types, whereas the evolution of sperm number may be slowed due to low heritability and evolvability but these values were still nonzero. There is no evidence that genetic constraints arise from indirect genetic contributions of the female reproductive tract, or that intersexual genetic correlations might constrain sperm evolution. We discuss these results and their implications in detail below. Numbers of both short and long sperm have relatively low heritabilities and evolvabilities (Table 2a). Most other quantitative genetic studies in insects have used testes size as a proxy for sperm number (Simmons and Moore 2009), with the exception of a study of N. cinerea, which estimated both testes size and sperm number (Moore et al. 2004). Our heritability estimates of both short and long sperm number are at the low end of the range of values presented in these earlier studies. Likewise, CV A s in these other studies on testes size were substantially larger (15.15– 18.85; Simmons and Moore 2009) than what we report, but note that these values measure different aspects of sperm production. In the one quantitative genetic study in which both testes mass and sperm number have been examined, there was a negative genetic correlation between testes mass and sperm number in N. cinerea (Moore et al. 2004). Although CV A s of both traits were relatively high in this species, the negative genetic correlation between these traits indicates that if postcopulatory sexual selection targeted these traits, then they would evolve in opposite directions (Moore et al. 2004). Spermathecal area (μm2 ) −0.09 (0.20) 0.16 (0.31) 0.12 (0.31) −0.02 (0.20) −0.49 (0.19) Length of ventral receptacle (μm) −0.22 (0.19) −0.15 (0.29) 0.31 (0.31) −0.07 (0.19) −0.06 (0.21) In contrast with sperm number, our estimated heritabilities and CV A s of dimorphic sperm lengths are more commensurate with data from sperm monomorphic species (Simmons and Moore 2009). Thus, the extent to which sperm length could evolve in our populations of D. pseudoobscura, while limited, is greater than that of sperm number. Overall, our results suggest that sperm traits in D. pseudoobscura are closely associated with male fitness, perhaps associated with success in postcopulatory sexual selection, and are at or near their phenotypic optimum. One caveat, which is a common design element to other studies, is that we evaluated the phenotypic and quantitative genetic parameters of only the first ejaculate of reproductively mature but sexually naı̈ve males. In most species, including D. pseudoobscura (Snook et al. 1994; Snook and Markow 2001), males transfer many more sperm than females store and this pattern may be particularly pronounced in the first ejaculate in which the male has had time to accumulate ejaculate resources. As the number of ejaculates increases for a male, phenotypically the quantity (Montrose et al. 2004) and perhaps quality (Morrow and Gage 2001; Green 2003; Harris et al. 2007) of his ejaculate may change. Future studies should consider examining the patterns of genetic (co)variance across ejaculates, which will add tremendous insight into episodes of selection that may influence sperm evolution and test the assumption that all ejaculates are equal. Genetic correlations between sperm traits also could constrain evolution. Sexual selection theory predicts that, because ejaculates are expensive to produce, (Pitnick et al. 2009b; Pizzari and Parker 2009), there should be phenotypic trade-offs between sperm number and size (Parker 1982). For evolutionary trade-offs to such selection to occur, patterns of genetic correlations need to reflect this. Evidence is mixed on this point (Snook 2005). The one quantitative genetic study that addresses this found no genetic correlation between sperm length and either ejaculate volume or testis weight (Simmons and Kotiaho 2002). In this sperm heteromorphic species, we found no significant genetic correlations among sperm traits other than numbers of the two types (Table 3a). EVOLUTION JULY 2010 1931 R H O N DA R . S N O O K E T A L . The female reproductive tract can influence ejaculate evolution and coevolve (Presgraves et al. 1999; Miller and Pitnick 2002; Pitnick et al. 2009a) and it is therefore possible that indirect genetic effects might constrain sperm evolution. We found that both spermathecal area and ventral receptacle length exhibited high-to-moderate heritabilities, similar to D. melanogaster (Miller et al. 2001). Thus, there is no obvious constraint arising from a lack of indirect genetic variation in female traits providing the environment for sperm (Table 2b). Genetic correlations between the sexes might also act as a constraint, but again the patterns of correlations do not support this hypothesis (Table 4). It is interesting that length of long sperm was negatively genetically correlated with spermathecal area. This result is consistent with the intraspecific pattern found in the sperm monomorphic O. taurus (Simmons and Kotiaho 2007), but differs from comparative patterns in both the sperm heteromorphic Drosophila obscura group (Holman et al. 2008) and diopsid stalkeyed flies (Presgraves et al. 1999). In the obscura group, long sperm length positively covaries with the ventral receptacle length but does not with spermathecal area, and short sperm do not covary with either female sperm storage organs (Holman et al. 2008). In diopsids, long sperm positively covary with both spermathecal area and ventral receptacle length whereas short sperm positively covary only with spermathecal area (Presgraves et al. 1999). The intraspecific pattern suggests that long sperm are sexually selected (Simmons and Kotiaho 2007), but given that in our study we find few significant correlations, the genetic relationship between long sperm length and spermathecal length we find here requires a cautious interpretation, especially as we see no phenotypic change in our experimental evolution lines. As Houle and Rowe (2003) point out, understanding patterns of evolution from experimental evolution lines requires knowledge of selection as well as genetics. In our lines, selection does not appear to target sperm length (Crudgington et al. 2009). Overall, our results suggest few genetic constraints arising from genetic correlations within and between males and female traits exist in this species, but sperm numbers do show low levels of genetic variation. This pattern differs from many other sexually selected traits in which substantial genetic variation remains (Pomiankowski and Møller 1995) on which evolution may act. Why? Additionally, why would genetic variation for sperm components be greatly depleted in this species and not in other species in which responses to either experimental or artificial selection were found? Moreover, even within this species, why is the response of some, but not all, ejaculate traits evolutionarily constrained? We have previously found that male accessory gland size, but not sperm, responds to experimental sexual selection (Crudgington et al. 2009). Unfortunately, we did not measure accessory gland size here, as this study was begun before we had the data of Crudgington et al. (2009). 1932 EVOLUTION JULY 2010 The most obvious difference between our study and those of other species in which quantitative genetics of sperm have been examined is that D. pseudoobscura is sperm heteromorphic whereas all other species examined are sperm monomorphic. But why sperm heteromorphism would be more susceptible to loss of genetic variation when the ejaculate is not well-integrated is a conundrum. One popular explanation for the high levels of genetic variation associated with sexually selected traits is that they tend to be highly condition sensitive (e.g., Rowe and Houle 1996; Cotton et al. 2004). There are only two studies showing condition dependence (measured as male body size) of sperm in sperm monomorphic species (Amitin and Pitnick 2006; Skinner and Watt 2007), but there are no such studies for sperm heteromorphic species. However, a number of studies on diverse taxa report either no or a negative relationship between male body size and sperm length (Pitnick et al. 2009b). It would be valuable to examine whether heteromorphic sperm are less sensitive to condition than other species and whether the different sperm types differ in respect to such a response. Given that we found no genetic relationship between male size (measured by wing length) and either number or length of either sperm type, sperm in this species indeed may be less sensitive to condition dependence and therefore may be more susceptible to loss of genetic variation. In summary, our results show that extrapolating expected evolution from purely phenotypic studies, or directly from studies of selection, should be made with caution. Patterns of genetic variation are complex, and there are multiple ways that constraints can arise as a result of the multivariate nature of reproductive traits (Blows and Hoffmann 2005; Blows 2007), and as a result of potentially complicated responses to sexual conflict arising from indirect genetic effects (Moore and Pizzari 2005). The patterns reported here are not necessarily ones we would have predicted a priori, as has been true in other studies of genetics of male reproductive traits when their multivariate nature is considered (Moore et al. 2004). We iterate Simmons and Moore’s (2009) call for more studies of the genetic influences on male and female reproductive traits that may experience sexual selection. Experimental evolution studies are useful in defining what may happen, but not necessarily why something did not happen. For this we need quantitative genetics combined with studies of selection. ACKNOWLEDGMENTS We thank H. Crudgington, T. Turner, K. Hutchence, N. Badcock, and the Snook laboratory for help during the experiment and T. Moore for comments on the manuscript. We are especially grateful to J. Hunt, who ran all the ASREML analyses. Comments from two anonymous reviewers helped us to clarify our work. Grants from NSF (USA) to RRS and NERC (UK) to RRS and AJM funded this work. 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