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A Rigorous Comparison of Sexual Selection Indexes Via Simulations of Diverse Mating Systems

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A Rigorous Comparison of Sexual Selection Indexes Via Simulations of Diverse Mating Systems A rigorous comparison of sexual selection indexes via simulations of diverse mating systems Jonathan M. Henshawa,1,2, Andrew T. Kahna, and Karoline Fritzscheb,1 aDivision of Evolution, Ecology and Genetics, Research School of Biology, The Australian National University, Canberra ACT 0200, Australia; and bInstitute of Zoology, University of Graz, Graz 8010, Austria Edited by Sarah P. Otto, University of British Columbia, Vancouver, BC, Canada, and approved December 7, 2015 (received for review September 10, 2015) Sexual selection is a cornerstone of evolutionary theory, but measur- mating success, respectively, whereas the use of partial selection ing it has proved surprisingly difficult and controversial. Various gradients allows one to control for indirect selection via corre- proxy measures—e.g., the Bateman gradient and the opportunity for lated traits (13, 15). Unfortunately, trait-based estimates serve sexual selection—are widely used in empirical studies. However, poorly in comparative studies of sexual selection, because there we do not know how reliably these measures predict the strength is no a priori method to determine which traits are sexually se- of sexual selection across natural systems, and most perform poorly lected (2, 16). Failure to include the primary targets of sexual in theoretical worst-case scenarios. Here we provide a rigorous com- selection in analyses will bias conclusions, particularly when the parison of eight commonly used indexes of sexual selection. We ease of identifying such traits covaries systematically with other simulated 500 biologically plausible mating systems, based on the factors of interest. As an example, human researchers may iden- templates of five well-studied species that cover a diverse range tify visually based targets of sexual selection more easily than of reproductive life histories. We compared putative indexes to olfactory traits, leading to biased inferences when taxa differ the actual strength of premating sexual selection, measured as in primary sensory modalities. Choosing a common trait, such as the strength of selection on a simulated “mating trait.” This method body size, as the basis for comparison does nothing to escape this sidesteps a key weakness of empirical studies, which lack an appro- problem, because it cannot be assumed that the trait’s relative priate yardstick against which proxy measures can be assessed. Our importance to sexual selection is constant across taxa. model predicts that, far from being useless, the best proxy measures reliably track the strength of sexual selection across biologically re- Measures Based on Variance in Mating or Reproductive alistic scenarios. The maximum intensity of precopulatory sexual Success Alone s′ selection max (the Jones index) outperformed all other indexes Due to the limitations of trait-based measures for comparative and was highly correlated with the strength of sexual selection. In studies, researchers have developed a host of proxy measures contrast, the Bateman gradient and the opportunity for sexual that depend only on generalized aspects of mating and re- selection were poor predictors of sexual selection, despite their production (Table 1) (17). Most measures are functions of the continuing popularity. variance in mating or reproductive success, under the assump- tion that higher variance correlates with higher sexual selection, opportunity for selection | index of resource monopolization | all else being equal. For instance, the opportunity for selection I Morisita index | selection gradient | distributional selection differential and opportunity for sexual selection Is represent the variance in relative reproductive success and relative mating success, re- ur understanding of sexual selection is obstructed by lack of spectively (11, 13, 18). These two measures have a clear con- Oprogress on a fundamental question: How do we measure nection to selection theory because they are strict upper bounds sexual selection rigorously, and how do we compare its strength on the linear selection and mating differentials on standardized between the sexes or across taxa (1–3)? Sexual selection under- lies some of the most extreme traits in nature, including spec- Significance tacular armaments and ornaments, and affects macroevolution via both speciation and extinction (4–8). It is thought to vary systematically with a species’ mating system (i.e., the degree of How does sexual selection differ between males and females? What is its role in the speciation process? Answering such multiple mating by each sex) and with sex-specific investment in questions requires a reliable method to measure sexual selec- offspring and mating partners (i.e., parental investment and di- tion, so that we can compare its strength between the sexes rect benefits) (9, 10). Testing such relationships requires a the- and across taxa. The development of appropriate measures has oretically well-justified method to estimate the strength of sexual led to sustained controversy, however, with no measure being selection within a sex and taxon. Although many potential “proxy widely accepted. We provide to our knowledge the first rig- measures” have been proposed—e.g., the opportunity for sexual orous comparison of indexes of sexual selection. Our results selection and the Bateman gradient (11–13)—none is widely unambiguously support the use of a recently defined measure, accepted and most perform poorly under theoretical worst-case the Jones index, over more established indexes of sexual se- scenarios (1, 14). Here we use simulation models to show that, far lection such as the Bateman gradient. from being useless, the best proxy measures track the strength of premating sexual selection faithfully under biologically relevant Author contributions: J.M.H., A.T.K., and K.F. designed research; J.M.H. and K.F. per- conditions. Our model explicitly specifies a focal “mating trait” that formed research; J.M.H., A.T.K., and K.F. contributed new reagents/analytic tools; is under sexual selection. This allows us to compare proxy measures J.M.H. analyzed data; and J.M.H. and K.F. wrote the paper. against the actual strength of sexual selection on the trait. We The authors declare no conflict of interest. thereby sidestep a key weakness of empirical studies, which lack an This article is a PNAS Direct Submission. appropriate yardstick against which proxy measures can be assessed. Data deposition: The data reported in this paper have been deposited in Dryad Digital Repository, datadryad.org (doi:10.5061/dryad.3p2j0). Sexual Selection on Traits 1J.M.H. and K.F. contributed equally to this work. The theoretical basis for measuring sexual selection on pheno- 2To whom correspondence should be addressed. Email: [email protected]. typic traits is well established. Selection and mating differentials This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10. measure how strongly a trait is associated with reproductive and 1073/pnas.1518067113/-/DCSupplemental. E300–E308 | PNAS | Published online January 6, 2016 www.pnas.org/cgi/doi/10.1073/pnas.1518067113 Downloaded by guest on September 24, 2021 Table 1. Proposed measures of the strength of sexual selection PNAS PLUS Measure Description Formula Variance-based measures σ2 Opportunity for selection, I Variance in relative reproductive success R μ2 R σ2 Opportunity for sexual selection, I Variance in relative mating success M s μ2 P MP ! X2 − X À i iÁ i i Morisita index for reproductive success, Observed variance in success relative to the expected variance N P 2 P , Xi − Xi Iδ−RS, or mating success, Iδ−MS if all individuals competed equally i i with X = R, M as appropriate σ2 − μ Monopolization index for reproductive Observed variance in success relative to the maximum X X , μ2 − μ N X X success, QRS, or mating success, QMS possible variance with X = R, M as appropriate μ σ Bateman gradient, β Slope of least-squares regression of relative reproductive M R,M ss μ σ2 R M success on relative mating success pffiffiffi ′ β β Jones index, smax Maximum strength of premating sexual selection m ss ss Is (see below) Trait-based measures σ ′ R,Z Linear selection differential, s Covariance between standardized trait values and relative μ reproductive success R σ ′ R,Z2 Quadratic selection differential, c Covariance between the square of standardized trait μ 2 R values Z and relative reproductive success R∞ Distributional selection differential, d′ Total change in the trait distribution due to selection (see SI) jFðzÞ − GðzÞj dz −∞ σ ′ M,Z Mating differential, m Covariance between standardized trait values and relative μ mating success M β ′ Strength of premating sexual selection Portion of the selection differential due to ssm premating sexual selection Formulas are shown for absolute reproductive success R, absolute mating success M, and a standardized quantitative trait Z, in terms of the number of μ σ2 σ ’ individuals N of the relevant sex, the mean X and variance X of any variable X, and the covariance X,Y between X and Y. We write Xi for the ith individual s value of X. The cumulative distribution functions of trait values before and after selection are given by F and G, respectively. phenotypic traits (10, 13). They have been criticized, however, tend to have more offspring. The Bateman gradient has often because their expected values depend on the number of com- been used as a standalone proxy for sexual selection, but the peting individuals and the mean success per individual, even information it provides is incomplete because it ignores the role when mating is entirely random (1, 19–22). The Morisita index of variance in mating success. The Jones index corrects for this (23, 24) and the monopolization index (20) aim to correct this by incorporating both the Bateman gradient and the opportu- problem by dividing the realized variance by the expected or nity for sexual selection. It measures both how much mating maximum possible variance, respectively. success varies and how this translates into variance in re- Measures based on variance in either mating or reproductive productive success.
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