B R I E F C O M M U N I C AT I O N doi:10.1111/evo.12132
EJACULATE QUALITY AND CONSTRAINTS IN RELATION TO SPERM COMPETITION LEVELS AMONG EUTHERIAN MAMMALS 1,2 ¨ Stefan Lupold 1
Department of Biology, Life Sciences Complex, 107 College Place, Syracuse University, Syracuse, New York 13244 2
E-mail:
[email protected]
Received October 20, 2012 Accepted April 5, 2013 Data Archived: Dryad doi: 10.5061/dryad.qj811 The outcome of sperm competition is influenced by the relative quantity and quality of sperm among competing ejaculates. Whereas it is well established that individual ejaculate traits evolve rapidly under postcopulatory sexual selection, little is known about other factors that might influence the evolution of ejaculates. For example, the metabolic rate is likely to affect the sperm production rate and the cellular activity or metabolism of sperm, and it has recently been suggested to constrain the evolution of sperm length in large but not small mammals. I thus examined in eutherian mammals how ejaculate quality traits vary with one another and with testis mass, body size, and metabolism. I found all ejaculate traits to covary positively with one another and to increase with relative testis mass. When controlling for testis mass, small-bodied species showed superior sperm quality (but not sperm number). Furthermore, sperm motility and viability were positively associated with the mass-corrected metabolic rate, but the percentage of morphologically normal and acrosome-intact sperm were not. These results indicate that body size and the energy budget may also influence the evolution of ejaculate quality, although these influences appear to vary among traits. KEY WORDS:
Basal metabolic rate, body size, energy budget, sperm number, sperm quality, postcopulatory sexual selection.
When more than one male mates with the same female and their sperm compete for fertilization, the resulting paternity share is typically biased toward the male transferring the most numerous and highest-quality sperm (e.g., Snook 2005; Pizzari and Parker 2009; Simmons and Fitzpatrick 2009). Consequently, sperm competition exerts selection on males to maximize both sperm quantity and quality (Snook 2005; Pizzari and Parker 2009; Simmons and Fitzpatrick 2012). Sperm quality is determined by a combination of variables, including sperm velocity, the proportions of motile, viable, structurally normal and acrosome-intact sperm, the sperm capacitation ability, and absolute and relative dimensions of different sperm components. These traits are essential for (1) successful migration of sperm to the egg(s) (particularly through the selective environment of the female reproductive tract in internal fertilizers); (2) sperm storage; (3) sperm capacitation; and (4) interaction with, and penetration of, the ovum (reviewed C
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in Florman and Ducibella 2006; Suarez 2008; Holt and Fazeli 2010). Polyandrous species tend to invest comparatively more in sperm production than monogamous species as indicated by their relatively larger testes (reviewed by Vahed and Parker 2012), denser sperm-producing tissue (L¨upold et al. 2009b; Rowe and Pruett-Jones 2011) and increased testicular function (Ramm and Stockley 2010; L¨upold et al. 2011). However, there is growing evidence that the production of sperm and ejaculates is energetically costly (e.g., Dewsbury 1982; Thomsen et al. 2006). Because the limited energy resources for sperm production have to be allocated between sperm quantity and different sperm-quality traits (e.g., sperm length), there are likely to be constraints in the evolution of ejaculates (Parker et al. 2010). Consistent with this idea, some studies have reported a trade-off between sperm size and number (Pitnick 1996; Oppliger et al. 1998; Immler et al. 2011),
C 2013 The Society for the Study of Evolution. 2013 The Author(s). Evolution Evolution 67-10: 3052–3060
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or among sperm-quality traits themselves, such as between sperm viability/longevity and sperm velocity/length (e.g., Birkhead and Fletcher 1995; Levitan 2000; Gage et al. 2002). However, several other studies have found no such trade-offs but rather positive covariation between different ejaculate traits (e.g., sperm length and velocity: Gomendio and Roldan 2008; Fitzpatrick et al. 2009; L¨upold et al. 2009a; sperm velocity and longevity: Kortet et al. 2004; sperm viability, motility, and morphological normality: Malo et al. 2005). More recently, two comparative studies across Muroid rodents and Australian fairy-wrens (Maluridae), respectively, have reported positive covariation among multiple measures of ejaculate quality or between ejaculate traits and relative testis mass (G´omez Montoto et al. 2011; Rowe and Pruett-Jones 2011). These results suggest that ejaculate quality traits may evolve in concert under sexual selection, possibly because the competitive fertilization success of an ejaculate is determined by a suite of different parameters (Pizzari and Parker 2009; Fitzpatrick et al. 2012; L¨upold et al. 2012). However, positive covariation among ejaculate traits could be found across species even if they were traded off within species. For example, species may vary in their overall resource allocation to ejaculates, with species under higher levels of sperm competition allocating relatively more resources toward ejaculates (Reznick 1985; van Noordwijk and de Jong 1986; Parker 1998). One way that physiology may influence the evolution of ejaculates is by variation in the basal metabolic rate (BMR), which is the daily energy expenditure for somatic maintenance (e.g., Glazier 2005). By reflecting maintenance costs, a relatively high BMR might indicate that little energy is available for other processes such as reproduction at a given energy budget (“compensation hypothesis”; Blackmer et al. 2005). However, because much of the BMR is explained by the metabolic activity of the digestive organs, a relatively high BMR might also indicate a higher level of food ingestion and processing, which would increase the potential total energy turnover and ultimately permit greater reproductive investments (“increased intake hypothesis”; Nilsson 2002). The energetic costs of ejaculates can be considerable. For example, the caloric content alone of the 1–6 daily ejaculates produced by male Japanese macaques (Macaca fuscata) has been estimated to represent 0.8–6% of the BMR (Thomsen et al. 2006). The total energetic expenses are expectedly even higher when the production of sperm and seminal fluid, or simply the maintenance of testes and accessory glands, are included (Thomsen et al. 2006). The testicular metabolic rate has been studied in only a handful of species (Setchell and Waites 1964; Ewing 1967; H¨ark¨onen and Kormano 1971), but under the assumption that it is proportional to whole-body BMR, Kenagy and Trombulak (1986) estimated the maintenance costs of the testes alone to range between about 0.2% and 10% of the total BMR. The sum of all metabolic costs related to ejaculate production thus might not be trivial, and as
outlined earlier, it seems plausible that individuals or species with a relatively high BMR may have a comparatively greater energy budget to allocate toward reproduction, thereby being better able to increase investments in ejaculates in response to postcopulatory sexual selection. Furthermore, the cellular metabolism of various highly active and rapidly dividing cell types is approximately proportional to the whole-body metabolism (Savage et al. 2007), mediated by the relative mitochondrial membrane surface area or oxidative enzyme activity in their cells (e.g., Porter 2001; Glazier 2005). A direct link between sperm and whole-body metabolism remains to be established but seems plausible given that sperm are also highly active and go through rapid cell divisions during spermatogenesis. A recent comparative study in birds suggests that sperm energy reserves (i.e., ATP) increase with the sperm midpiece length (Rowe et al. 2013; also see Vladi´c et al. 2002), whereas Tourmente et al. (2011) reported an increase in midpiece length (and other sperm dimensions) with the mass-corrected BMR among marsupial mammals. Although Rowe et al. (2013) did not find a direct relationship between ATP content to in vitro sperm swimming speed (but see Froman and Feltmann 1998), the combination of these studies provides at least some indirect evidence that species with a relatively high BMR might, on average, be better able to maintain high sperm motility and viability than others. Consistent with this prediction, Gomendio et al. (2011) and Tourmente et al. (2011) have shown that small mammalian species produce longer sperm than larger species and hypothesized that, due to more efficient energy uptake and transport or energy use by cells (e.g., Glazier 2005), small species with their higher mass-specific metabolic rate may be energetically less constrained to evolve longer sperm in response to sexual selection than larger species that exhibit a lower BMR relative to body size. However, further studies are needed to understand how different ejaculate characteristics might (co)evolve and how possible metabolic constraints might influence the evolution of ejaculates. Using data from the literature on a wide range of eutherian mammals, I tested the two hypotheses that (1) ejaculate quality traits covary positively with one another and with the degree of sperm competition and that (2) these traits are also positively associated with the metabolic rate controlled for body size.
Methods DATA COLLECTION AND TREATMENT
For a total of 173 eutherian species, I obtained published data on body mass, combined testis mass (CTM), BMR, and five ejaculate parameters, including the total number of sperm per ejaculate and the respective proportions of motile, structurally normal, viable, and acrosome-intact sperm (see Table S1). For ejaculate traits,
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each species was represented by 9.6 ± 0.1 males (± SE; range 1–168), and although species belonged to 10 mammalian Orders, most were Artiodactyla (N = 45), Carnivora (46), Primates (34), or Rodentia (27; Table S1). Although sperm-quality traits are typically measured in separate subsamples and assays, it is important to note that they are not necessarily independent. For example, the proportion of viable sperm, measured by the exclusion (live cells) or permeation (dead cells) of specific stains through the sperm membrane, sets a ceiling to the proportion of motile sperm, with motile sperm being all progressively and nonprogressively moving sperm as quantified manually or by computer-assisted sperm analysis. However, assay sensitivity or measurement error in the compiled studies may occasionally have resulted in a higher proportion of motile than viable sperm. Similarly, in the case of structural normality and acrosome integrity, both of which are typically assessed by visual inspection under a light microscope, the subset of abnormal sperm includes those with acrosome defects. Additional abnormalities include but are not limited to a bent midpiece or flagellum, two sperm heads or flagella, or a disproportionately large or small head. All data of the four sperm-quality traits were restricted to fresh as opposed to cryopreserved samples. The methods for these measures varied slightly among sources (e.g., manual versus automated, or different types of stains), which can be an issue in comparative studies (Amann 1981; Holman 2009). It was not feasible to evaluate the reliability of measurements in each study, but with nearly every species assayed by a different laboratory and each measurement technique represented in all major taxa (and thus in a range of size classes), I specifically assumed that variation due to assay reliability was unlikely to cause a systematic error that would jeopardize the conclusions. In contrast, I statistically controlled for the method of sample collection, because samples of 32 species were collected by dissection of the epididymis rather than by natural or electro-ejaculation, such that the absence or presence of seminal fluid could confound the results (see Analyses). Because epididymal samples were unlikely to reflect the amount of ejaculates, I restricted all analyses involving total sperm number to ejaculated samples. In some cases, the total sperm number was not provided but it could be calculated as the product of the ejaculate volume and sperm concentration. Overall, not all data were available for each species, but for each species I obtained data of at least two different ejaculate traits (for among-trait correlations) or at least one such trait along with data on CTM or BMR and body mass. If data were provided for breeding and nonbreeding males or for experimentally treated and control animals, I used those for the breeding or control individuals, respectively. Where applicable, data from multiple studies were combined by calculating a weighted mean.
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ANALYSES
I conducted all analyses using the statistical package R version 2.12.2 (R Foundation for Statistical Computing 2011) and transformed all nonnormal data distributions by logarithmic or arcsine transformations to meet the parametric requirements of the statistical models. I accounted for statistical nonindependence of data points by shared ancestry of species using phylogenetic general linear models (PGLM; Pagel 1999; Freckleton et al. 2002; script kindly provided by R. Freckleton). The phylogeny was based on a phylogenetic supertree of extant mammals (Bininda-Emonds et al. 2007), with additional species included following specific phylogenies (for details, see Figure S1). This PGLM estimates the phylogenetic scaling parameter λ, with values of λ not significantly different from 0 indicating phylogenetic independence, and λ not significantly different from 1 indicating a complete phylogenetic association of the traits. Using likelihood ratio tests, I established whether the model with the maximum-likelihood estimate of λ differed from models with values of λ = 0 or 1, respectively, and present the P-values of these tests as superscripts following λ. I report these results together with the (partial) correlation coefficients r and 95% noncentral confidence intervals (95% CI) calculated from the t-values of the PGLM (Nakagawa and Cuthill 2007). Assessing the strength of relationships based on r is also preferable to Bonferroni corrections for multiple testing, as it avoids the increased probability of committing type II errors (Nakagawa 2004). For analyses of sperm competition, I included both CTM and body mass as predictor variables. As a measure of male investments in sperm production, relative testis mass is a widely used proxy for the degree of sperm competition and often the best index available. Among mammals, relative testis mass is correlated with the level of multiple paternity (Soulsbury 2010), thus supporting the assumption that it generally reflects the level of sperm competition (although testes might occasionally be enlarged in response to a high mating frequency rather than sperm competition; Vahed and Parker 2012). I further examined whether ejaculate quality was explained by the BMR. Rather than using the mass-specific metabolic rate (i.e., BMR divided by body mass) as the predictor (see Gomendio et al. 2011; Tourmente et al. 2011), I conducted multiple regressions with BMR and body mass as two independent variables, for two reasons. First, if a trait is allometrically related with body mass (i.e., trait does not vary as a fixed proportion of body mass), expressing it as a ratio can introduce spurious correlations that are explained solely by variation in body mass (e.g., Tanner 1949; Nevill and Holder 1995; Packard and Boardman 1999). Basal metabolic rate exhibits a negatively allometric relationship with body mass (allometric coefficient approximately 0.75; Savage et al. 2004), so the use of the mass-specific metabolic rate as an independent variable can be statistically problematic and is therefore
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discouraged (e.g., Packard and Boardman 1999). Second, using BMR and body mass separately can reveal additional body size effects that are independent of metabolism. I further controlled all models on sperm quality for potential influences by the method of sample collection (dissection vs. ejaculate). In most cases, however, sampling method had no significant effect, and removing this factor resulted in a lower score of Akaike’s information criterion (AICc; controlled for small sample sizes). In these cases, I report the results of simplified models.
Results Controlling for phylogeny and the method of sample collection (dissection vs. ejaculate), all ejaculate quality traits were positively correlated with one another (Table 1) and with relative testis mass (Table 2A). In comparison to the positive relationships with relative testis mass, the above models also yielded negative body mass effects on all sperm-quality measures but not on sperm number (Table 2A). Thus, controlling for testis mass, relatively small species appear to produce higher-quality sperm than large species. Although CTM was strongly correlated with body mass (r = 0.72 [95% CI = 0.65– 0.78], df = 154, t = 13.0, P < 0.0001, λ = 0.95
