Behavioral Ecology Advance Access originally published online on June 11, 2004
Behavioral Ecology 2004 15(5):850-856; doi:10.1093/beheco/arh094
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Birth sex ratio and social rank: consistency and variability within and between primate groups
Istituto di Scienze e Tecnologie della Cognizione, Consiglio Nazionale delle Ricerche, Rome, Italy
Address correspondence to G. Schino. E-mail: gschino{at}casaccia.enea.it.
Received 12 May 2003; revised 9 January 2004; accepted 24 January 2004.
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ABSTRACT |
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Variations in birth sex ratios in primates are notoriously inconsistent and have been repeatedly suggested to be mainly owing to stochastic processes. An examination of temporal consistency within primate populations revealed that the effect of dominance rank on birth sex ratio tends to remains stable over time. Furthermore, a meta-analysis of published data on sex ratio variations in primates shows that although no overall effect was detectable, the relation between birth sex ratio and dominance rank was affected by level of resource availability and degree of sexual dimorphism. These results suggest that purely stochastic processes are unlikely to explain observed variations in primate birth sex ratios, and may explain why adaptive sex ratio variations in primates have been so difficult to demonstrate.
Key words: maternal investment, meta-analysis, population growth, sexual dimorphism.
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INTRODUCTION |
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Sex allocation theory is among the most debated topics of evolutionary ecology. Although the problem of population level sex ratio was basically solved by Fisher (1930)
, the adaptive value of interindividual variation in birth sex ratios remains open to debate. Thirty years ago, Trivers and Willard (1973) hypothesized that it could be adaptive for mothers to bias the birth sex ratio in favor of the sex of offspring that gives the greatest fitness return per unit investment. Support for this notion has come mostly from the study of invertebrates (West et al., 2000), whereas studies of vertebrates, and especially of mammals, have been highly inconsistent in their results (Clutton-Brock, 1991; Clutton-Brock and Iason, 1986; Hewison and Gaillard, 1999).
Part of the confusion that affects the literature on mammal sex ratios may derive from the difficulties that exist in making a priori unambiguous predictions about the direction of sex ratio bias (West and Sheldon, 2002). This is especially true for primates, in which opposite predictions have been made, and in which different a posteriori explanations for the variety of observed patterns have been proposed. For example, among polygynous mammals, and thus among most primates, mothers in the best physical condition (i.e., mothers that are presumably able to invest more in their offspring) were originally expected to overproduce males. In the following years, however, it was also suggested (Altmann, 1980; Silk, 1983) that because most primates show female philopatry, high-ranking females should produce an excess of females who remain in their natal group and inherit their mother's high rank and reproductive success.
Published data to test these contrasting predictions are highly inconsistent (Bercovitch, 2002; Brown, 2001; Hiraiwa-Hasegawa, 1993). Recently, Brown and Silk (2002) applied meta-analytical techniques to published data on the relation between maternal dominance rank and birth sex ratio in primates, and showed that the mean difference in the birth sex ratios of high- and low-ranking mothers is zero. They thus concluded that available evidence does not allow rejecting the null hypothesis that female primates do not facultatively adjust birth sex ratios.
Brown and Silk (2002) noted that their analysis could only test for the existence of a uniform effect of dominance on birth sex ratios that would apply regardless of interspecific or ecological differences. In fact, several investigators have suggested that the relation between dominance rank (or any other measure of the ability to invest in offspring) and birth sex ratio is likely to be affected by various independent factors. First, under conditions of low resource availability, females may not (regardless of their rank) be able to obtain the extra resources needed to positively influence their sons' reproductive success, so that male-biased sex ratios may be expected to occur in high-ranking females, but only under conditions of high resource availability (Kruuk et al., 1999). A similar prediction derives from the hypothesis that under conditions of low local resource availability, low-ranking females may need to avoid the production of female offspring that are the target of harassment from higher-ranking females (Van Schaik and Hrdy, 1991). Second, based on a similar reasoning, Byers and Moodie (1990) suggested that those species showing the highest levels of maternal investment may not show sex-biased birth sex ratios and investment simply because mothers cannot provide any extra resource for their male offspring. Biased sex ratios may thus be expected to occur more frequently in the high-ranking females of species characterized by overall lower levels of maternal investment. Finally, biased sex ratios may be particularly advantageous in those species undergoing strong sexual selection, because male variance in reproductive success would be particularly high (Clutton-Brock, 1988).
The aim of the present study is twofold. First, I tested the hypothesis that the observed variability in the relation between dominance rank and birth sex ratio is simply owing to stochastic processes associated to sampling. If such hypothesis is correct, no correlation should be observed between sex ratio data collected on the same population at different times. If, on the other hand, primate populations show temporal consistency in the relation between dominance rank and birth sex ratio, then it is unlikely that purely random effects are operating. Second, I applied meta-analytical techniques to published data on sex ratio variations in primates in order to evaluate the role of various factors in modulating the effects of dominance rank on birth sex ratio. Meta-analysis is a set of statistical techniques that allow quantitative syntheses of published primary data while controlling for the potential biases introduced by varying sample size, and is becoming increasingly popular in ecology (Gates, 2002).
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METHODS |
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To obtain data, I made a complete search in the following journals: American Journal of Primatology (1981–2002), Animal Behaviour (1975–2002), Behavioral Ecology (1992–2002) Behaviour (1973–2002), Behavioral Ecology and Sociobiology (1993–2002), Ethology (1999–2002), Ethology, Ecology and Evolution (1989–2002) Folia Primatologica (1973–2002), International Journal of Primatology (1980–2002), Journal of Animal Ecology (1973–2002), Journal of Mammalogy (1978–2002), and Primates (1973–2002). Then, I consulted the Institute of Scientific Information (ISI) Web of Science and the PrimateLit database. To be included in the analyses, an article had to report the number of male and female infants born to high- and low-ranking mothers. I took particular care in excluding duplicate studies from the same population (except in the analysis of temporal consistency, see below). In such cases I only included the study with the larger sample size. I also included a few studies that were not originally intended to test sex ratio variations but that nevertheless included (often in the methods section) data on number of male and female infants born to high- and low-ranking females. A total of 30 studies satisfied the selection criteria. These studies were carried out on 17 different species belonging to nine genera. My data set largely overlaps with that of Brown and Silk (2002)
, but I discarded a few of the studies they included for reasons explained in Table 1, and I was able to add a few more studies. The studies included in the meta-analysis are shown in Table 2.
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In most articles, mothers were divided in two or three rank classes. When an odd number of rank classes was used in the original study (e.g., high-, middle- and low-ranking mothers), I discarded data from the central rank class (i.e., from middle-ranking mothers). In a few cases mothers were classified as top-ranking and non–top-ranking. In such cases I considered top-ranking females as high-ranking and non–top-ranking females as low-ranking. These same procedures were used by Brown and Silk (2002)
. Raw data on number of male and female infants born to high- and low-ranking mothers were used to calculate effect size (the rate difference, i.e., the difference between the proportion of males born to high- and to low-ranking females). To evaluate the intrapopulation temporal consistency in effect size, I relied on duplicate data sets from the same population. Several primate populations have been studied for long periods, and investigators have presented birth sex ratio data twice in different studies. In those cases the data set of the first study was always a subset of that of the second larger study. For each pair of studies, I thus calculated effect size for the first study and then subtracted birth data reported in the first study from those reported in the second study, and calculated again effect size in this second now independent sample. In three more cases, birth sex ratio data were presented separately for different time periods by the original investigators. In these cases I simply calculated effect sizes relative to the different time periods. In this way, I obtained two temporally independent estimates of effect size for each population and calculated the Spearman's correlation between the two. Studies included in the evaluation of the intrapopulation temporal consistency in effect size are shown in Table 3.
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Data relative to population growth rate, maternal investment, and sexual dimorphism were taken from the literature (references in Table 2).
Annual population growth rate was calculated on the basis of the equation
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Species-specific degrees of maternal investment were calculated as the ratio litter weight0.75/maternal weight0.75 (Byers and Moodie, 1990). Sexual dimorphism was calculated as the male/female weight ratio (Weckerly, 1999). Use of an alternative measure of sexual dimorphism such as log10(male/female weight) did not alter the significance of the results.
I conducted random effect meta-analyses by using the MetaWin software (Rosenberg et al., 2000). Mean weighed effect sizes were calculated, as well as bias-corrected bootstrap 95% confidence intervals. Data structure was allowed by inserting continuous independent variables and testing for their significance by using randomization tests based on 5000 iterations. These are equivalent to linear regressions but weigh the different studies by using standard meta-analytical techniques (Rosenberg et al., 2000).
Effects of maternal investment and sexual dimorphism are potentially affected by phylogenetic relationships among species (Nunn and Barton, 2001). I am not aware of any method to incorporate phylogenetic information into a meta-analytic framework. The use of phylogenetically independent contrasts based on effect sizes (see Dubois and Cèzilly, 2002) is unsatisfactory as it cannot weigh the different studies in relation to the estimated variance in effect size. This is the main reason to conduct a meta-analysis and cannot be ignored. To explore the influence of phylogeny, I therefore simply ran the analyses using populations (i.e., studies), species or genera as data points. Results were essentially identical, and I will therefore only present results based on species (except when effect size was related to population growth).
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RESULTS |
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Intrapopulation temporal consistency
Effect sizes recorded in the two temporally independent samples were significantly positively correlated (rs =.833, N = 8, p <.03) (Figure 1). When a linear regression was fit through the data, the 95% confidence intervals of the slope of the regression overlapped one (0.00–1.48), and those of the intercept overlapped zero (–0.20–0.09). These results argue against purely random effects being at the basis of the observed variation in effect size.
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Overall effect size
Confirming the results of Brown and Silk (2002)
, a meta-analysis of the published data on the relation between dominance rank and birth sex ratio showed no overall significant effect (mean weighted effect size = 0.0098, 95% confidence intervals –0.0411 to 0.0663). Heterogeneity in effect sizes approached significance (Q = 40.90, df = 29, p =.07).
Modulation of effect sizes
Following the method of van Schaik and Hrdy (1991), population growth rate was considered as an index of the level of local resource availability and inserted as a moderator factor in the meta-analysis. Effect size was significantly affected by population growth rate (b = 0.41 ± 0.29 [regression coefficient ± SE], p <.05) (Figure 2). Figure 2 shows that the relation between population growth rate and effect size is heavily influenced by one possible outlier (the Amboseli baboon population studied by Altmann et al., 1988). Removal of this data point makes the influence of population growth nonsignificant (b = 0.16 ± 0.24, p = ns), and the slope of the regression is strongly reduced. It should be noted, however, that the Amboseli baboons represent one of the best-studied wild primate populations, and its exclusion solely on the basis of its being an outlier may be unwarranted.
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Effect size was not significantly related to degree of maternal investment as indexed by the ratio litter weight0.75/maternal weight0.75 (b = –0.49 ± 0.95, p = ns) (Figure 3). Effect size was significantly negatively related to the degree of sexual dimorphism as indexed by the male/female weight ratio (b = –0.26 ± 0.10, p <.002) (Figure 4). Sex ratio bias was more pronounced in the high-ranking females of low-dimorphic species. Removal of the possible outlier shown in Figure 4 (Ateles paniscus) does not alter the significance of the relation (b = –0.13 ± 0.08, p <.05), although the slope of the regression is somewhat reduced.
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Effect size of populations that were captive or artificially provisioned did not differ from that of wild populations (Q = 0.0004, df = 1, p = ns).
My sample included only one species characterized by male philopatry (A. paniscus, McFarland Symington, 1987). I could not, therefore, test for the influence of dispersal patterns. Excluding the data point from A. paniscus did not significantly alter any of the results.
The three variables tested in this study were unrelated to each other (sexual dimorphism versus population growth: rs = –.239, N = 19, p = ns; sexual dimorphism versus maternal investment: rs =.159, N = 17, p = ns; maternal investment versus population growth: rs = –.417, N = 18, p = ns), so that the effects I observed were not owing to covariation among the independent variables.
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONREFERENCES |
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This study had two main findings. First, within primate populations there was a temporally consistent relation between dominance rank and birth sex ratio. Second, although no overall relationship between birth sex ratio bias and dominance rank was observed, such a relationship became apparent under conditions of high resource availability and low sexual dimorphism (although the former effect was heavily influenced by a single population).
If purely random processes were operating, than no intrapopulation consistency in the relation between maternal dominance rank and birth sex ratio should be expected. My first result thus suggests that the interpopulation variability reported in the literature is likely owing to the effect of independent modulating factors and not, as suggested by Brown and Silk (2002), to stochastic variations in small samples.
The significant influence of modulating factors reported in the second part of the present study may explain why sex ratio biases have been so difficult to detect, and highlights the usefulness of meta-analytical techniques in tackling ecological/evolutionary questions that require very large data sets to be properly addressed.
That the relation between birth sex ratio and dominance rank can be affected by resource availability was originally suggested by Van Schaik and Hrdy (1991) on the basis of a smaller sample of studies, but their analysis did not employ meta-analytical techniques and was thus prone to publication bias and unbalanced weight given to the different studies. A study of a single population of red deer under variable conditions of population density (Kruuk et al., 1999) also reported a significant interaction between sex ratio bias and population density: high-ranking females produced significantly more male calves, but only when population density was low (and thus resource availability was presumably higher). More recently, Packer et al. (2000) criticized Van Schaik and Hrdy's analysis, noting that most of their data came from captive or artificially provisioned populations that may be experiencing growth rates well beyond those typical of their environment of evolutionary adaptedness. Such criticism may be less pertinent to the present study, as it includes data from several wild populations. It should again be noted that in the present study, the relation between population growth and extent of sex ratio bias was heavily influenced by a single population. Because, however, the outlier population (the Amboseli baboon studied by Altmann et al., 1988) presents no other anomalous characteristics that may suggest their sex ratio data are unreliable, its exclusion from analysis might be unwarranted. Clearly, the relation between resource availability and sex ratio bias needs further investigation.
Effect size was not significantly related to degree of maternal investment. This result does not support the hypothesis that, among primates, overall maternal investment may constrain the ability of females to provide extra resources for their male offspring and thus limit the extent to which male biased sex ratios may be advantageous for high-ranking females. Similar conclusions were reached by Pelabon et al. (1995) for polygynous ungulates.
Contrary to expectations, effect size was negatively related to degree of sexual dimorphism. Sex ratio bias was more pronounced in the high-ranking females of low-dimorphic species. It should be noted, however, that assuming that higher variances in male reproductive success (presumably associated with higher sexual dimorphism) would automatically select for male biased sex ratios in high-ranking females could be overly simplistic. Indeed, selective pressures favoring biased sex ratios depend on the extent to which the reproductive success of offspring is influenced by the amount of parental investment (Leimar, 1996). West and Sheldon (2002) recently emphasized how parental ability to predict the environment (and likely reproductive success) of their offspring may strongly limit the strength of selective pressures favoring precise sex ratio bias. Highly dimorphic species are generally larger (Weckerly, 1998) and their males take longer to reach reproductive age compared to females. It is possible that when the difference in the reproductive age of males and females is larger, then early maternal investment may be relatively less strongly related to male adult reproductive success, because more time between infancy and maturation presumably introduces more chances for random variation. A direct test of this hypothesis would require knowledge of the age at which significant reproductive success is reached in males and females. Such data are currently unavailable, so I used age of complete physical development (age at growth cessation in males and females, as compiled by Leigh, 1992), considering that significant reproductive success is unlikely before complete physical maturation. The differential in male-female age at growth cessation was not significantly related to effect size when species were used as data points (b = –0.002 ± 0.028, p = ns), but its effect was significant when genera were used as data points (b = –0.128 ± 0.116, p <.05). These results, although somewhat inconsistent, provide some support for the notion that differential predictability of the likely reproductive success of male and female offspring may constrain selective pressures favoring sex riatio bias. Also, they are not explained by a covariation of differential in age at growth cessation and sexual dimorphism, as these two variables were not significantly correlated (species as data points: rs = –.428, N = 12, p = ns; genera as data points: rs = –.009, N = 7, p = ns).
In conclusion, the present study emphasizes how the relation between dominance rank and birth sex ratio is affected by multiple factors, and suggests that purely stochastic processes are unlikely to explain the heterogeneity of results reported in the literature. The null hypothesis that nonhuman primate females do not adjust the sex ratio of their offspring in relation to their own rank is challenged again.
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ACKNOWLEDGEMENTS |
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I thank Michael Rosenberg for statistical advice; Charlie Nunn and Dario Maestripieri for sharing unpublished data; Gillian Brown, Tim Clutton-Brock, Ian Hardy, Sarah Hrdy, Charlie Nunn, Ben Sheldon, Joan Silk, Carel van Schaik, and Stuart West for critical comments; and Gloria Gianandrea for her unfailing support.
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