For almost a century, researchers have gathered compelling evidence that sexual selection (i.e., selection arising from competition for mating partners and/or their gametes) constitutes the ultimate evolutionary force generating sexual dimorphism in a multitude of reproductive characters and life-history traits (Andersson, 1994; Clutton-Brock, 2007). Despite this progress, we are just beginning to understand the eco-evolutionary dynamics of sexual selection in terms of its impact on demography and adaptive potential of a population (Svensson and Deere, 2018). On the one hand, sexual selection has often been considered to promote the evolution of traits that have opposing fitness effects in both sexes, which manifests in sexual conflict (Arnqvist and Rowe, 2005). Such sexually antagonistic selection is predicted to have a demographic cost and therefore to reduce the population’s adaptive potential (Chapman, 2006; Rankin et al., 2011). More precisely, sexual selection may lead to interlocus sexual conflict by favoring the evolution of traits that are beneficial in one sex (also called persistence traits such as harassment and infanticide) but induce a fitness cost in the other sex and thereby promote the evolution of traits that mitigate these costs (resistance traits) (Arnqvist and Rowe, 2005). Furthermore, if the expression of a shared trait has different fitness consequences for males and females, intralocus sexual conflict may shift male and female phenotypes away from their evolutionary optimum. In particular, intralocus sexual conflict for fitness implies that sexual selection in one sex favors genotypes that have a low reproductive fitness when expressed in the other, which may manifest in negative cross-sex genetic correlations of fitness (Chippindale et al., 2001; Foerster et al., 2007) and thereby lower the population’s adaptive potential (Pischedda and Chippindale, 2006; Bonduriansky and Chenoweth, 2009).

In contrast to the predictions obtained from sexual conflict thinking, another line of theoretical and empirical work suggests that sexual selection can facilitate how populations cope with environmental change (Lorch et al., 2003; Candolin and Heuschele, 2008; Holman and Kokko, 2013; Martínez-Ruiz and Knell, 2017; Martinossi-Allibert et al., 2019; Rowe and Rundle, 2021). This latter school of thought is based on two main assumptions. First, we need to assume that intralocus sexual conflict is rare so that cross-sex genetic correlations of fitness are positive (Rowe and Rundle, 2021). This implies that sexual and natural selection are aligned, meaning that sexual selection favors alleles that also improve fecundity and survival − a process mediated by condition dependence of sexually selected traits (Rowe and Houle, 1997). Such an alignment is expected to manifest in a positive genetic correlation between male and female fitness as long as sexually antagonistic loci are rare and/or have minor fitness effects. Poissant et al., 2010 compiled cross-sex genetic correlations of various trait types and found that correlations are generally large and positive but tend to be lower for fitness components in comparison to morphological, behavioral, and developmental traits. Moreover, there is strong comparative and meta-analytic evidence supporting the idea that the expression of pre- and postcopulatory sexual traits depends on the overall condition of the male (Cotton et al., 2004; Macartney et al., 2019). This means that sexual selection may not only favor the evolution of prominent secondary sexual traits but also traits that confer health and vigor (Jennions et al., 2001), and therefore eventually purges deleterious alleles that are targeted by natural selection.

In contrast to the extensive support for an alignment of sexual and natural selection in many species (Whitlock and Agrawal, 2009; Cally et al., 2019; Rowe and Rundle, 2021), we still know very little on whether the second key assumption for sexual selection to empower evolutionary adaptation is generally fulfilled across a broad range of taxa. Namely, sexual selection enforces natural selection only if it gives rise to stronger net selection (defined as the sum of genome-wide selection against deleterious alleles) on males compared to females. In a landmark synthesis paper, Whitlock and Agrawal, 2009 explored the effect of sex-specific selection on the population’s mutation load, that is the overall reduction of absolute fitness due to deleterious alleles in a population. They expanded the foundational work of Haldane, 1937 by relaxing the assumption of random mating to demonstrate that the mutation load L is expected to be

where μ is the mutation rate from wild type to deleterious alleles and s is the average selection coefficient against deleterious alleles of females (s_f) and males. Hence, the mutation load is reduced whenever s_f < s, which arises if net selection is stronger on males compared to females. Given that the population’s productivity is typically governed by female fecundity, a population with stronger net selection on males can purge its mutation load and adapt faster to a new environment with a lowered demographic cost and thereby reduce its extinction risk. In other words, females benefit from being part of a gene pool that is purified primarily through stronger selection on males (Whitlock and Agrawal, 2009). It is important to note that this perspective assumes that the vast majority of deleterious alleles have negative fitness effects on both male and female fitness, which implies that sex-limited genes are relatively rare in the genome.

There is ample empirical evidence that typically (though not always) males undergo stronger sexual selection whereas females are primarily exposed to fecundity selection (Janicke et al., 2016) as predicted by Bateman’s principle (Bateman, 1948). However, our knowledge on whether such stronger sexual selection on males eventually translates into stronger net selection relative to females is still limited and equivocal (Whitlock and Agrawal, 2009; Hendry et al., 2018). This lack of evidence stems at least partially from difficulties in quantifying the strength of net selection in a framework that also allows comparisons among sexes and species. Potentially the most promising metric to contrast the strength of net selection across contexts is the mean-standardized variance in fitness, which is often expressed as the coefficient of variation (CV). Previous comparative studies provided evidence that the phenotypic variance in fitness (‘opportunity for selection’ [Crow, 1958]) is typically larger in males compared to females, suggesting that the upper opportunity for net selection is stronger in males (Janicke et al., 2016). However, it has been questioned whether the phenotypic variance in fitness provides a good proxy for the strength of net selection because environmental variation can substantially inflate this variance, which limits its explanatory power with respect to evolutionary responses and complicates the comparison across contexts and studies (Whitlock and Agrawal, 2009). Moreover, male reproductive success is often quantified with higher uncertainty due to measurement error during paternity analysis, which may lead to higher phenotypic variance in fitness of males compared to females caused by sex-specific methodology rather than by a biological mechanism. To overcome this problem, researchers have advocated to use the genetic rather than the phenotypic variance in fitness as a proxy for the strength of net selection (Jones, 2009; Hendry et al., 2018). This is also because the genetic variance in relative fitness corresponds to the rate of increase in mean fitness that results from selection on allele frequencies (Fisher, 1930). Therefore, the mean-standardized genetic variance (CV_G) in fitness provides a highly diagnostic metric to quantify the strength of net selection, but it has to our knowledge not yet been subjected to a systematic and global test for an overall sex difference.

Here, we addressed this key aspect of sexual selection theory by using a comparative approach to test whether net selection is generally stronger on males across a broad taxonomic range. Specifically, we ran a systematic literature search and compiled 101 pairwise estimates of male and female genetic variances for 2 main fitness components – reproductive success and lifespan – from a total of 26 species. Applying phylogenetically informed comparative analyses we tested (1) whether phenotypic variances are aligned to genetic variances as assumed by the phenotypic gambit (Grafen, 1991) and (2) whether males show larger genetic variance in reproductive success but not lifespan. According to the so-called genic capture hypothesis, an organism’s condition (defined as the pool of acquired resources that can be allocated into different fitness components such as survival and reproductive success) is a quantitative trait governed by a large array of loci and determines the fitness of an organism by affecting its survival, fecundity, and the outcome of pre- and postcopulatory sexual selection (Rowe and Houle, 1997). Importantly, if sexual selection operates on males, their reproductive success not only depends on gamete production and postzygotic investment (as predicted to constitute the primary determinants in females), but also on the outcome of pre- and postcopulatory mate choice and mate competition. Therefore, a deleterious allele with its negative effect on condition is predicted to have a disproportionately higher impact on reproductive success in males compared to females. This heightened condition dependence of male performance (Rowe and Houle, 1997) is expected to translate ultimately into a higher genetic variance in reproductive success in males (Whitlock and Agrawal, 2009; Hendry et al., 2018). By contrast, lifespan is considered to be unaffected by the outcome of sexual selection except in the relatively rare event of mortal combats for access to mates, mate harassment or indirectly by shaping life-history strategies (Bonduriansky et al., 2008). If correct, a deleterious allele is therefore expected to have a similar effect on lifespan in males and females so that we predict if anything, a much weaker male bias for genetic variance in lifespan. In this context, we also provide a preliminary test on the role of sexual selection for generating the hypothesized sex differences in net selection. Specifically, we contrasted socially monogamous and polygamous species, with the prediction that a male bias in genetic variance for reproductive success is primarily prevalent in polygamous species where sexual selection is most likely to be stronger compared to monogamous species (Shuster and Wade, 2003).

We found that the phenotypic coefficient of variation (CV_P) of reproductive success does not predict the genetic coefficient of variation (CV_G) in either males (linear regression: estimate ± SE = 0.085 ± 0.060, F_1,60 = 2.026, p = 0.160, R² = 0.03) or females (linear regression: estimate ± SE = 0.038 ± 0.064, F_1,60 = 0.350, p = 0.667, R² = 0.01; Figure 1). Similarly, phylogenetic independent contrasts (PICs) analyses suggest only weak though statistically significant relationships between CV_P and CV_G for reproductive success in males (linear regression: estimate ± SE = 0.113 ± 0.051, F_1,37 = 4.847, p = 0.032, R² = 0.07) and females (linear regression: estimate ± SE = 0.191 ± 0.065, F_1,37 = 8.615, p = 0.005, R² = 0.13). In contrast, CV_P was a strong predictor of CV_G for lifespan in both males (linear regression: estimate ± SE = 0.266 ± 0.068, F_1,60 = 15.3, p < 0.001, R² = 0.29) and females (linear regression: estimate ± SE = 0.204 ± 0.051, F_1,60 = 16.16, p < 0.001, R² = 0.30). These effects were stronger when accounting for phylogenetic nonindependence in males (linear regression: estimate ± SE = 0.552 ± 0.048, F_1,37 = 131.1, p < 0.001, R² = 0.78) and females (linear regression: estimate ± SE = 0.345 ± 0.036, F_1,37 = 91.14, p < 0.001, R² = 0.70).

Figure 1

Download asset Open asset

Most importantly, CV_P of reproductive success was generally larger in males compared to females, which translated into a male bias in CV_G, with sex explaining 7% and 4% of the observed variance, respectively (Table 1; Figure 2A-B and Figure 3). Interestingly, when running a preliminary test, we found that this sex difference could be detected in polygamous but not monogamous species, which manifested in a significant sex by mating system interaction (Table 1 and Supplementary file 1; Figure 3). These findings could be confirmed when analyses were run on a subset including only vertebrate species (Supplementary files 2 and 3). Contrary to the results for reproductive success, we did not observe consistent sex differences in CV_P and CV_G for lifespan (Table 1; Figure 2C-D). Finally, methodological heterogeneity among primary studies assessed in terms of study type, the estimate of genetic variance V_G, and the type of reproductive success metric did not predict CV_P and CV_G or the observed sex differences (Supplementary files 4–6).

Figure 2

Download asset Open asset

Figure 3

Download asset Open asset

Males and females share the vast majority of their genome but are often subject to fundamentally different selection pressures, which is predicted to impact the demography and the adaptive potential of a population when facing environmental change (Whitlock and Agrawal, 2009; Holman and Kokko, 2013; Svensson and Deere, 2018). In line with sexual selection theory, our study provides the first comparative evidence that genome-wide selection is generally stronger on males compared to females. More specifically, our results have two major implications. First, phenotypic variance is a good predictor of genetic variance for lifespan but not for reproductive success, providing limited support for the phenotypic gambit. Therefore, the opportunity for selection measured as the phenotypic variance in reproductive success is a poor proxy for the strength of net selection. Despite these results, our second major finding is that the previously observed male bias in the phenotypic opportunity for selection (Janicke et al., 2016) is also reflected in an overall higher male genetic variance in reproductive success. Interestingly, our preliminary analysis of the mating system revealed that this male bias can only be detected in polygamous species, in which sexual selection is likely to be stronger compared to monogamous species (Shuster and Wade, 2003). Hence, pre- and postcopulatory competition and/or mate choice may magnify the material that selection acts on in a sex-specific manner. In contrast, phenotypic and genetic variances of lifespan do not show sex differences in either polygamous or monogamous species. The sample size for lifespan (N_Estimates = 39; N_Species = 16) was smaller compared to reproductive success (N_Estimates = 62; N_Species = 21) meaning that we had less statistical power to detect a sex difference in phenotypic and genetic variances for lifespan. However, we did not observe any consistent trend for lifespan and our data suggest that even if there was a sex difference in genetic variance in lifespan, its effect size would be considerably smaller compared to reproductive success. Thus, we conclude that while sexual selection may promote sex differences in mean lifespan at an evolutionary scale (Lemaître et al., 2020), our results indicate that it does not generate sex-specific genetic variances in this fitness component. Hence, the strength of selection on survival appears to be similar among males and females.

Despite the overall strong sex difference in genetic variation of reproductive success, our results on the effect of the social mating system need to be considered with caution. This is because of the underrepresentation of monogamous species in our dataset and because of only three independent evolutionary changes from polygamy to monogamy in our phylogeny. Moreover, our binary classification of the mating system into socially monogamous and polygamous species fails to capture the continuum in the strength of sexual selection across the sampled taxa, likely limiting its explanatory power as a predictor variable. Ideally, one would use a standardized continuous estimate for the strength of sexual selection allowing inter- and intraspecific comparisons such as Bateman metrics, for example, the maximum intensity of precopulatory sexual selection s′_max (Jones, 2009; Henshaw et al., 2016). Unfortunately, such data are currently only accessible for a small fraction of the sampled species (i.e., 19% based on a database collected by Janicke and Morrow, 2018), which renders those measures as additional predictors for the strength of sexual selection unavailable at the current state of knowledge.

In essence, our findings provide support for the prediction that net selection is stronger on males compared to females. We conclude that one of the key assumptions required for sexual selection to assist natural selection and thereby to accelerate the adaptation to changing environments is often fulfilled in nature. For species with a positive cross-sex genetic correlation of fitness (i.e., limited intralocus sexual conflict), stronger net selection on males implies that populations may purge deleterious alleles across the genome primarily at the expense of males and thus at a low demographic cost (Agrawal, 2001; Siller, 2001). This has important eco-evolutionary consequences because stronger net selection on males will not only bolster local adaptation but will also reduce extinction risk when populations are coping with challenging environmental conditions (Lumley et al., 2015). Therefore, our findings support the idea that sexual selection can play a pivotal role in evolutionary rescue (Candolin and Heuschele, 2008; Holman and Kokko, 2013; Svensson and Deere, 2018) and are in line with a recent meta-analysis providing compelling evidence that sexual selection increases nonsexual fitness (Cally et al., 2019). Interestingly, environmental stress has repeatedly been found to elevate the effect of deleterious mutations and thereby increase genetic variation in fitness-related traits (Rowiński and Rogell, 2017). Thus, we extend the sexes-as-environments analogy (Rice and Chippindale, 2008) to say that an almost identical genome is expressed in a more stressful male environment versus a relatively more benign female environment.

Whether stronger net selection on males eventually promotes adaptation to a new environment, or even contributes to the evolution and maintenance of sexual over asexual reproduction (Agrawal, 2001; Siller, 2001), will also depend on whether sexual conflict impedes the alignment of natural and sexual selection. This includes another important aspect of the genetic architecture of male and female fitness components: intralocus sexual conflict and its impact on the cross-sex genetic covariance of fitness. Specifically, only if fitness in both sexes is condition dependent (i.e., positively affected by the amount of acquired resources) and therefore largely governed by a similar set of genes, will sexual selection on males purge deleterious alleles in females and thereby facilitate adaptation (Whitlock and Agrawal, 2009). Theoretical work predicts that cross-sex genetic correlations for fitness and fitness components are often low but positive (Connallon and Matthews, 2019). Negative genetic correlations between sexes are expected to be rare and only to prevail if two conditions are met: selection is highly antagonistic and that both sexes are strongly displaced from their optimum (Connallon and Matthews, 2019). This seems to be in line with empirical work, because studies reporting positive cross-sex genetic correlations outnumber those with negative correlations (Poissant et al., 2010; Connallon and Matthews, 2019; but see Foerster et al., 2007). Only a small fraction of the primary studies included in our analysis reported cross-sex genetic correlations, but exploratory analyses of this subset (Supplementary file 7) support an earlier finding of highly positive genetic correlations for lifespan with no consistent pattern for reproductive success (Hendry et al., 2018). In the context of adaptation, it is also crucial to understand how environmental change affects cross-sex genetic correlations. Theory predicts that directional selection erodes genetic variation at loci with concordant effects on males and females more efficiently compared to sexually antagonistic loci. As a consequence, sexually antagonistic loci may have a higher impact on fitness components in well-adapted populations compared to populations facing novel environmental conditions (Connallon and Hall, 2016). Therefore, we may expect that environmental changes are generally associated with more positive cross-sex genetic correlations for fitness components, but the few experimental studies provided rather mixed empirical support for this prediction (Delcourt et al., 2009; Berger et al., 2014; Holman and Jacomb, 2017). Further work on the genetic covariance between male and female fitness components is clearly needed to evaluate the overall potential of sexual selection to facilitate or constrain the adaptation to changing environments. Specifically, how genetic variances and covariances of male and female fitness components change with ecological conditions is largely unknown, though such knowledge is crucial to predict evolutionary trajectories when populations face environmental change. Moreover, for some taxa, sexual selection has been found to increase extinction risk (Doherty et al., 2003; Le Galliard et al., 2005) potentially as a consequence of intense sexual conflict but the quantitative genetics of male and female fitness of those species remain mostly unexplored.

Despite the detected sex differences and the effect of the social mating system, a large fraction of the intra- and interspecific variance in CV_G remained unexplained. This is potentially, at least in part, because genetic variances are often estimated with low precision, which may have introduced substantial noise into our analyses. Besides that, we suspect that another part of the unexplained variation stems from environmental effects influencing genetic (co)variances (i.e., genotype by environmental interactions) (Rowiński and Rogell, 2017), which limits comparisons of studies conducted under different experimental conditions. Moreover, there are also methodological differences between primary studies, which may have contributed to the unexplained variation in CV_G. This includes the application of different breeding designs used to quantify genetic variances (i.e., pedigrees, half-sib/full-sib breeding, parent-offspring regressions, and inbred lines), differences in the analytical approaches (latent-scale versus data-scale estimates of genetic variances), and disparity in the measurement of reproductive success (annual versus lifetime reproductive success; see Material and methods). While all these sources of uncontrolled variation are likely to have introduced noise into our dataset, we are not aware of any systematic biases that they might have created. Finally, our study covers a broad taxonomic range spanning flatworms, mollusks, arthropods, and vertebrates but is based on relatively few species. This is admittedly a limitation of our study but at the same time illustrates the clear need for more quantitative genetic studies measuring genetic variation of fitness components in both sexes.

Collectively, our analysis reveals a pervasive male bias in the strength of net selection and provides preliminary support for the role of sexual selection to promote this sex difference. However, we have just started to understand the eco-evolutionary consequences of sexual selection in terms of its impact on demography and the adaptive potential of populations to cope with changing environments.

All data associated with this study can be retrieved from the Zenodo data repository (https://zenodo.org/record/5529490#.YVFhNKB8Kqc) and R scripts can be accessed at https://lennartwinkler.github.io/Net_Selection_eLife_code/ and the GitHub repository at: https://github.com/LennartWinkler/Net_Selection_eLife_code copy archived at swh:1:rev:5dcb7b744d879bc1f80af8b3dff8184577f005bc.

1. 1. Agrawal AF

   (2001) Sexual selection and the maintenance of sexual reproduction

   Nature 411:692–695.

   https://doi.org/10.1038/35079590

   • PubMed
   • Google Scholar
2. Book

   1. Andersson M

   (1994) Sexual Selection

   Princeton University Press.

   https://doi.org/10.1515/9780691207278

   • Google Scholar
3. Book

   1. Arnqvist G
   2. Rowe L

   (2005) Sexual Conflict

   Princeton University Press.

   https://doi.org/10.1515/9781400850600

   • Google Scholar
4. 1. Bateman AJ

   (1948) Intra-sexual selection in Drosophila

   Heredity 2:349–368.

   https://doi.org/10.1038/hdy.1948.21

   • PubMed
   • Google Scholar
5. 1. Berger D
   2. Grieshop K
   3. Lind MI
   4. Goenaga J
   5. Maklakov AA
   6. Arnqvist G

   (2014) Intralocus sexual conflict and environmental stress

   Evolution; International Journal of Organic Evolution 68:2184–2196.

   https://doi.org/10.1111/evo.12439

   • PubMed
   • Google Scholar
6. 1. Bonduriansky R
   2. Maklakov A
   3. Zajitschek F
   4. Brooks R

   (2008) Sexual selection, sexual conflict and the evolution of ageing and life span

   Functional Ecology 22:443–453.

   https://doi.org/10.1111/j.1365-2435.2008.01417.x

   • Google Scholar
7. 1. Bonduriansky R
   2. Chenoweth SF

   (2009) Intralocus sexual conflict

   Trends in Ecology & Evolution 24:280–288.

   https://doi.org/10.1016/j.tree.2008.12.005

   • PubMed
   • Google Scholar
8. 1. Brouwer L
   2. Griffith SC

   (2019) Extra-pair paternity in birds

   Molecular Ecology 28:4864–4882.

   https://doi.org/10.1111/mec.15259

   • PubMed
   • Google Scholar
9. 1. Cally JG
   2. Stuart-Fox D
   3. Holman L

   (2019) Meta-analytic evidence that sexual selection improves population fitness

   Nature Communications 10:2017.

   https://doi.org/10.1038/s41467-019-10074-7

   • PubMed
   • Google Scholar
10. 1. Candolin U
    2. Heuschele J

    (2008) Is sexual selection beneficial during adaptation to environmental change?

    Trends in Ecology & Evolution 23:446–452.

    https://doi.org/10.1016/j.tree.2008.04.008

    • PubMed
    • Google Scholar
11. 1. Chapman T

    (2006) Evolutionary conflicts of interest between males and females

    Current Biology 16:R744–R754.

    https://doi.org/10.1016/j.cub.2006.08.020

    • Google Scholar
12. 1. Chippindale AK
    2. Gibson JR
    3. Rice WR

    (2001) Negative genetic correlation for adult fitness between sexes reveals ontogenetic conflict in Drosophila

    PNAS 98:1671–1675.

    https://doi.org/10.1073/pnas.041378098

    • PubMed
    • Google Scholar
13. 1. Clutton-Brock T

    (2007) Sexual selection in males and females

    Science 318:1882–1885.

    https://doi.org/10.1126/science.1133311

    • PubMed
    • Google Scholar
14. 1. Connallon T
    2. Hall MD

    (2016) Genetic correlations and sex-specific adaptation in changing environments

    Evolution; International Journal of Organic Evolution 70:2186–2198.

    https://doi.org/10.1111/evo.13025

    • PubMed
    • Google Scholar
15. 1. Connallon T
    2. Matthews G

    (2019) Cross-sex genetic correlations for fitness and fitness components: Connecting theoretical predictions to empirical patterns

    Evolution Letters 3:254–262.

    https://doi.org/10.1002/evl3.116

    • PubMed
    • Google Scholar
16. 1. Cotton S
    2. Fowler K
    3. Pomiankowski A

    (2004) Do sexual ornaments demonstrate heightened condition-dependent expression as predicted by the handicap hypothesis?

    Proceedings of the Royal Society of London. Series B 271:771–783.

    https://doi.org/10.1098/rspb.2004.2688

    • Google Scholar
17. 1. Crow JF

    (1958)

    Some possibilities for measuring selection intensities in man

    Human Biology 30:1–13.

    • PubMed
    • Google Scholar
18. Book

    1. de Villemereuil P
    2. Nakagawa S

    (2014)

    General quantitative genetic methods for comparative biology

    In: de Villemereuil P, editors. Modern Phylogenetic Comparative Methods and Their Application in Evolutionary Biology. Garamszegi, LZ: Springer-Verlag Berlin Heidelberg, p. pp. 1–552.

    • Google Scholar
19. 1. Delcourt M
    2. Blows MW
    3. Rundle HD

    (2009) Sexually antagonistic genetic variance for fitness in an ancestral and a novel environment

    Proceedings of the Royal Society B 276:2009–2014.

    https://doi.org/10.1098/rspb.2008.1459

    • Google Scholar
20. 1. Doherty PF
    2. Sorci G
    3. Royle JA
    4. Hines JE
    5. Nichols JD
    6. Boulinier T

    (2003) Sexual selection affects local extinction and turnover in bird communities

    PNAS 100:5858–5862.

    https://doi.org/10.1073/pnas.0836953100

    • PubMed
    • Google Scholar
21. 1. Dunn PO
    2. Whittingham LA
    3. Pitcher TE

    (2001) Mating systems, sperm competition, and the evolution of sexual dimorphism in birds

    Evolution; International Journal of Organic Evolution 55:161–175.

    https://doi.org/10.1111/j.0014-3820.2001.tb01281.x

    • PubMed
    • Google Scholar
22. Book

    1. Fisher RA

    (1930) The Genetical Theory of Natural Selection

    Oxford University Press.

    https://doi.org/10.5962/bhl.title.27468

    • Google Scholar
23. 1. Foerster K
    2. Coulson T
    3. Sheldon BC
    4. Pemberton JM
    5. Clutton-Brock TH
    6. Kruuk LEB

    (2007) Sexually antagonistic genetic variation for fitness in red deer

    Nature 447:1107–1110.

    https://doi.org/10.1038/nature05912

    • PubMed
    • Google Scholar
24. 1. Fox CW
    2. Bush ML
    3. Roff DA
    4. Wallin WG

    (2004) Evolutionary genetics of lifespan and mortality rates in two populations of the seed beetle, Callosobruchus maculatus

    Heredity 92:170–181.

    https://doi.org/10.1038/sj.hdy.6800383

    • PubMed
    • Google Scholar
25. 1. Gay L
    2. Brown E
    3. Tregenza T
    4. Pincheira-Donoso D
    5. Eady PE
    6. Vasudev R
    7. Hunt J
    8. Hosken DJ

    (2011) The genetic architecture of sexual conflict: male harm and female resistance in Callosobruchus maculatus

    Journal of Evolutionary Biology 24:449–456.

    https://doi.org/10.1111/j.1420-9101.2010.02182.x

    • PubMed
    • Google Scholar
26. Book

    1. Grafen A

    (1991)

    Modelling in behavioural ecology

    In: Krebs JR, Davies NB, editors. Behavioural Ecology. Blackwell Scientific Publications. pp. 5–31.

    • Google Scholar
27. 1. Hadfield JD

    (2010) MCMC methods for multi-response generalized linear mixed models: the MCMCglmm R package

    Journal of Statistical Software 33:1–22.

    https://doi.org/10.18637/jss.v033.i02

    • Google Scholar
28. 1. Haldane JBS

    (1937) The Effect of Variation of Fitness

    The American Naturalist 71:337–349.

    https://doi.org/10.1086/280722

    • Google Scholar
29. 1. Hendry AP
    2. Schoen DJ
    3. Wolak ME
    4. Reid JM

    (2018) The contemporary evolution of fitness

    Annual Review of Ecology, Evolution, and Systematics 49:457–476.

    https://doi.org/10.1146/annurev-ecolsys-110617-062358

    • Google Scholar
30. 1. Henshaw JM
    2. Kahn AT
    3. Fritzsche K

    (2016) A rigorous comparison of sexual selection indexes via simulations of diverse mating systems

    PNAS 113:E300–E308.

    https://doi.org/10.1073/pnas.1518067113

    • Google Scholar
31. 1. Holman L
    2. Kokko H

    (2013) The consequences of polyandry for population viability, extinction risk and conservation

    Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 368:12.

    https://doi.org/10.1098/rstb.2012.0053

    • PubMed
    • Google Scholar
32. 1. Holman L
    2. Jacomb F

    (2017) The effects of stress and sex on selection, genetic covariance, and the evolutionary response

    Journal of Evolutionary Biology 30:1898–1909.

    https://doi.org/10.1111/jeb.13149

    • PubMed
    • Google Scholar
33. 1. Houle D

    (1992) Comparing evolvability and variability of quantitative traits

    Genetics 130:195–204.

    https://doi.org/10.1093/genetics/130.1.195

    • PubMed
    • Google Scholar
34. 1. Janicke T
    2. Häderer IK
    3. Lajeunesse MJ
    4. Anthes N

    (2016) Darwinian sex roles confirmed across the animal kingdom

    Science Advances 2:e1500983.

    https://doi.org/10.1126/sciadv.1500983

    • PubMed
    • Google Scholar
35. 1. Janicke T
    2. Morrow EH

    (2018) Operational sex ratio predicts the opportunity and direction of sexual selection across animals

    Ecology Letters 21:384–391.

    https://doi.org/10.1111/ele.12907

    • PubMed
    • Google Scholar
36. 1. Janicke T
    2. Chapuis E
    3. Meconcelli S
    4. Bonel N
    5. Delahaie B
    6. David P

    (2021) Environmental effects on the genetic architecture of fitness components in a simultaneous hermaphrodite

    Journal of Animal Ecology 00:1–14.

    https://doi.org/10.1111/1365-2656.13607

    • Google Scholar
37. 1. Jennions MD
    2. Møller AP
    3. Petrie M

    (2001) Sexually selected traits and adult survival: A meta-analysis

    The Quarterly Review of Biology 76:3–36.

    https://doi.org/10.1086/393743

    • PubMed
    • Google Scholar
38. 1. Jones AG

    (2009) On the opportunity for sexual selection, the Bateman gradient and the maximum intensity of sexual selection

    Evolution; International Journal of Organic Evolution 63:1673–1684.

    https://doi.org/10.1111/j.1558-5646.2009.00664.x

    • PubMed
    • Google Scholar
39. 1. Kumar S
    2. Stecher G
    3. Suleski M
    4. Hedges SB

    (2017) TimeTree: A Resource for Timelines, Timetrees, and Divergence Times

    Molecular Biology and Evolution 34:1812–1819.

    https://doi.org/10.1093/molbev/msx116

    • PubMed
    • Google Scholar
40. 1. Kumar S
    2. Stecher G
    3. Li M
    4. Knyaz C
    5. Tamura K

    (2018) MEGA X: molecular evolutionary genetics analysis across computing platforms

    Molecular Biology and Evolution 35:1547–1549.

    https://doi.org/10.1093/molbev/msy096

    • PubMed
    • Google Scholar
41. 1. Le Galliard J-F
    2. Fitze PS
    3. Ferrière R
    4. Clobert J

    (2005) Sex ratio bias, male aggression, and population collapse in lizards

    PNAS 102:18231–18236.

    https://doi.org/10.1073/pnas.0505172102

    • PubMed
    • Google Scholar
42. 1. Lemaître J-F
    2. Ronget V
    3. Tidière M
    4. Allainé D
    5. Berger V
    6. Cohas A
    7. Colchero F
    8. Conde DA
    9. Garratt M
    10. Liker A
    11. Marais GAB
    12. Scheuerlein A
    13. Székely T
    14. Gaillard J-M

    (2020) Sex differences in adult lifespan and aging rates of mortality across wild mammals

    PNAS 117:8546–8553.

    https://doi.org/10.1073/pnas.1911999117

    • PubMed
    • Google Scholar
43. 1. Lorch PD
    2. Proulx S
    3. Rowe L
    4. Day T

    (2003)

    Condition-dependent sexual selection can accelerate adaptation

    Evolutionary Ecology Research 5:867–881.

    • Google Scholar
44. 1. Lukas D
    2. Clutton-Brock TH

    (2013) The Evolution of Social Monogamy in Mammals

    Science 341:526–530.

    https://doi.org/10.1126/science.1238677

    • PubMed
    • Google Scholar
45. 1. Lumley AJ
    2. Michalczyk Ł
    3. Kitson JJN
    4. Spurgin LG
    5. Morrison CA
    6. Godwin JL
    7. Dickinson ME
    8. Martin OY
    9. Emerson BC
    10. Chapman T
    11. Gage MJG

    (2015) Sexual selection protects against extinction

    Nature 522:470–473.

    https://doi.org/10.1038/nature14419

    • PubMed
    • Google Scholar
46. 1. Macartney EL
    2. Crean AJ
    3. Nakagawa S
    4. Bonduriansky R

    (2019) Effects of nutrient limitation on sperm and seminal fluid: a systematic review and meta-analysis

    Biological Reviews of the Cambridge Philosophical Society 94:1722–1739.

    https://doi.org/10.1111/brv.12524

    • PubMed
    • Google Scholar
47. 1. Martínez-Ruiz C
    2. Knell RJ

    (2017) Sexual selection can both increase and decrease extinction probability: reconciling demographic and evolutionary factors

    The Journal of Animal Ecology 86:117–127.

    https://doi.org/10.1111/1365-2656.12601

    • Google Scholar
48. 1. Martinossi-Allibert I
    2. Rueffler C
    3. Arnqvist G
    4. Berger D

    (2019) The efficacy of good genes sexual selection under environmental change

    Proceedings. Biological Sciences 286:20182313.

    https://doi.org/10.1098/rspb.2018.2313

    • PubMed
    • Google Scholar
49. 1. Moller AP

    (2008) Mating systems among European passerines: a review

    Ibis 128:234–250.

    https://doi.org/10.1111/j.1474-919X.1986.tb02671.x

    • Google Scholar
50. 1. Nakagawa S
    2. Schielzeth H
    3. O’Hara RB

    (2013) A general and simple method for obtaining R 2 from generalized linear mixed-effects models

    Methods in Ecology and Evolution 4:133–142.

    https://doi.org/10.1111/j.2041-210x.2012.00261.x

    • Google Scholar
51. 1. Nakagawa S
    2. Poulin R
    3. Mengersen K
    4. Reinhold K
    5. Engqvist L
    6. Lagisz M
    7. Senior AM
    8. O’Hara RB

    (2014) Meta‐analysis of variation: ecological and evolutionary applications and beyond

    Methods in Ecology and Evolution 6:143–152.

    https://doi.org/10.1111/2041-210X.12309

    • Google Scholar
52. Software

    1. Orme D
    2. Freckleton R
    3. Thomas G
    4. Petzoldt T
    5. Fritz S
    6. Isaac N

    (2018) Comparative Analyses of Phylogenetics and Evolution, version R. Version 1.0.1

    Caper.

    https://cran.r-project.org/web/packages/caper/index.html
53. 1. Pélissié B
    2. Jarne P
    3. David P

    (2012) Sexual selection without sexual dimorphism: Bateman gradients in a simultaneous hermaphrodite

    Evolution; International Journal of Organic Evolution 66:66–81.

    https://doi.org/10.1111/j.1558-5646.2011.01442.x

    • PubMed
    • Google Scholar
54. 1. Pischedda A
    2. Chippindale AK

    (2006) Intralocus sexual conflict diminishes the benefits of sexual selection

    PLOS Biology 4:e0040356.

    https://doi.org/10.1371/journal.pbio.0040356

    • PubMed
    • Google Scholar
55. 1. Poissant J
    2. Wilson AJ
    3. Coltman DW

    (2010) Sex-specific genetic variance and the evolution of sexual dimorphism: a systematic review of cross-sex genetic correlations

    Evolution; International Journal of Organic Evolution 64:97–107.

    https://doi.org/10.1111/j.1558-5646.2009.00793.x

    • PubMed
    • Google Scholar
56. 1. Rankin DJ
    2. Dieckmann U
    3. Kokko H

    (2011) Sexual conflict and the tragedy of the commons

    The American Naturalist 177:780–791.

    https://doi.org/10.1086/659947

    • PubMed
    • Google Scholar
57. 1. Rice WR
    2. Chippindale AK

    (2008) Intersexual ontogenetic conflict

    Journal of Evolutionary Biology 14:685–693.

    https://doi.org/10.1046/j.1420-9101.2001.00319.x

    • Google Scholar
58. 1. Rowe L
    2. Houle D

    (1997) The lek paradox and the capture of genetic variance by condition dependent traits

    Proceedings of the Royal Society of London. Series B 263:1415–1421.

    https://doi.org/10.1098/rspb.1996.0207

    • Google Scholar
59. 1. Rowe L
    2. Rundle HD

    (2021) The alignment of natural and sexual Selection

    Annual Review of Ecology, Evolution, and Systematics 52:499–517.

    https://doi.org/10.1146/annurev-ecolsys-012021-033324

    • Google Scholar
60. 1. Rowiński PK
    2. Rogell B

    (2017) Environmental stress correlates with increases in both genetic and residual variances: A meta-analysis of animal studies

    Evolution; International Journal of Organic Evolution 71:1339–1351.

    https://doi.org/10.1111/evo.13201

    • PubMed
    • Google Scholar
61. 1. Shuker DM

    (2010) Sexual selection: endless forms or tangled bank?

    Animal Behaviour 79:E11–E17.

    https://doi.org/10.1016/j.anbehav.2009.10.031

    • Google Scholar
62. 1. Shuker DM
    2. Kvarnemo C

    (2021) The definition of sexual selection

    Behavioral Ecology 32:781–794.

    https://doi.org/10.1093/beheco/arab055

    • PubMed
    • Google Scholar
63. Book

    1. Shuster SM
    2. Wade MJ

    (2003) Mating Systems and Strategies

    Princeton University Press.

    https://doi.org/10.1515/9780691206882

    • Google Scholar
64. 1. Siller S

    (2001) Sexual selection and the maintenance of sex

    Nature 411:689–692.

    https://doi.org/10.1038/35079578

    • PubMed
    • Google Scholar
65. 1. Svensson EI
    2. Deere J

    (2018) Eco‐evolutionary dynamics of sexual selection and sexual conflict

    Functional Ecology 33:60–72.

    https://doi.org/10.1111/1365-2435.13245

    • Google Scholar
66. 1. Uyeda JC
    2. Zenil-Ferguson R
    3. Pennell MW

    (2018) Rethinking phylogenetic comparative methods

    Systematic Biology 67:1091–1109.

    https://doi.org/10.1093/sysbio/syy031

    • PubMed
    • Google Scholar
67. 1. Webb CO
    2. Ackerly DD
    3. Kembel SW

    (2008) Phylocom: software for the analysis of phylogenetic community structure and trait evolution

    Bioinformatics 24:2098–2100.

    https://doi.org/10.1093/bioinformatics/btn358

    • PubMed
    • Google Scholar
68. 1. Wheelwright NT
    2. Keller LF
    3. Postma E

    (2014) The effect of trait type and strength of selection on heritability and evolvability in an island bird population

    Evolution; International Journal of Organic Evolution 68:3325–3336.

    https://doi.org/10.1111/evo.12499

    • PubMed
    • Google Scholar
69. 1. Whitlock MC
    2. Agrawal AF

    (2009) Purging the genome with sexual selection: reducing mutation load through selection on males

    Evolution; International Journal of Organic Evolution 63:569–582.

    https://doi.org/10.1111/j.1558-5646.2008.00558.x

    • PubMed
    • Google Scholar

Author details

1. Lennart Winkler

   Applied Zoology, Technical University Dresden, Dresden, Germany

   Contribution

   Data curation, Formal analysis, Investigation, Methodology, Visualization, Writing – original draft

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-3597-6540

2. Maria Moiron

   CEFE, CNRS, Univ Montpellier, EPHE, IRD, Montpellier, France

   Contribution

   Formal analysis, Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0991-1460

3. Edward H Morrow

   Department for Environmental and Life Sciences, Karlstad University, Karlstad, Sweden

   Contribution

   Conceptualization, Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-1853-7469

4. Tim Janicke

   1. Applied Zoology, Technical University Dresden, Dresden, Germany
   2. CEFE, CNRS, Univ Montpellier, EPHE, IRD, Montpellier, France

   Contribution

   Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Validation, Visualization, Writing – original draft

   For correspondence

   tim.janicke@cefe.cnrs.fr

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-1453-6813

• 2,220

  views

• 227

  downloads

• 23

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.