Behavioral Ecology Vol. 11 No. 3: 294-298
© 2000 International Society for Behavioral Ecology
Is variation in brood sex ratios adaptive in the great tit (Parus major)?
Edward Grey Institute of Field Ornithology, Department of Zoology, University of Oxford, South Parks Road, Oxford OX1 3PS, UK
Address correspondence to A. N. Radford at the Department of Zoology, University of Cambridge, Downing Street, Cambridge CB2 3EJ, UK. E-mail: ar255{at}hermes.cam.ac.uk .
Received 12 March 1999; revised 6 September 1999; accepted 15 September 1999.
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMETHODSRESULTSDISCUSSIONREFERENCES |
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Life-history theory predicts skewed offspring sex ratios in a range of situations in which the costs and benefits of producing the two sexes differ. In recent years, many studies have demonstrated biased sex ratios in a variety of bird species. However, many of these investigations have been based on small sample sizes, on data from a single year, or both. Using a recently developed polymerase chain reaction-based molecular DNA technique, 912 great tit (Parus major) nestlings from 118 broods in 5 different years were sexed. As found in a number of previous studies on the same species, there were significant predictors of offspring sex ratio in individual years. However, there were no consistent trends across years, and none of the measured variables significantly predicted sex ratio over all years combined. Furthermore, brood sex ratio of the population did not depart from the expected binomial distribution. Although there are theoretical advantages to manipulating the sex ratio in this and other species, the physiological mechanism by which it is achieved in birds remains obscure. We argue that data from several years are needed to confirm whether facultative sex ratio manipulation is a consistent breeding strategy used by birds.
Key words: great tits, molecular sexing, Parus major, sex ratios.
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONREFERENCES |
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Fisher (1958
) proposed that
frequency-dependent selection should result in the equal allocation of
reproductive resources to the production of male and female offspring. This
has been confirmed mathematically
(MacArthur, 1965;
Shaw and Mohler, 1953) and
further mathematical treatments have expanded the model (e.g., Charnov,
1975,
1982). Williams
(1979) suggested that sex
ratio variation among outcrossed vertebrates is mainly a result of automatic,
Mendelian segregation of sex chromosomes at meiosis, with little or no scope
for adaptive parental manipulation. To date, no physiological mechanism for
facultative sex ratio adjustment in birds has been identified
(Krackow, 1995). Despite this,
there has been a recent accumulation of empirical evidence indicating that
animals with chromosomal sex determination are capable of adjusting their
offspring sex ratios in an adaptive way in response to a variety of factors
(see, e.g., Bortolotti, 1986;
Clutton-Brock et al., 1984;
Lessells et al., 1996).
Deviations from Fisherian sex ratios have been found at a populationwide level
(e.g., Hamilton, 1967;
Koenig and Dickinson, 1996),
between individuals who have different resources available to them (e.g.,
Komdeur, 1996;
Komdeur et al., 1997;
Willson and Pianka, 1963), and
between separate breeding attempts of the same individual (e.g.,
Bednarz and Hayden, 1991;
Blank and Nolan, 1983;
Olsen and Cockburn, 1991).
Great tits (Parus major) exhibit a number of life-history traits
that may influence sex ratio variation. First, they are sexually dimorphic in
body size (males are slightly bigger, with 4% larger wing and tarsus
measurements than females; Perrins,
1963). The larger sex costs more to produce, requiring more
resources, so the sex ratio will be biased toward individuals of the smaller
sex to equalize overall expenditure
(Fisher, 1958). Therefore, a
female-biased population sex ratio is predicted in great tits.
Second, production of high-quality individuals of the more expensive sex
may be relatively more costly to mothers in poor condition than to those in
good condition (Wiebe and Bortolotti,
1992). Further, if maternal condition has an effect on offspring
condition that lasts into adulthood and the reproductive success (RS) of one
sex is more strongly dependent on condition than it is in the other sex,
mothers in better condition should bias offspring production toward the sex
that yields relatively greater fitness benefits
(Trivers and Willard, 1973).
Extrapair fertilizations also occur in this study population of great tits,
increasing variability in male RS relative to female RS still further
(Blakey and Norris, 1994).
Therefore, great tit females in good condition are expected to produce more
sons and those in poor condition are expected to produce more daughters.
Third, when the attractiveness of a male depends in part on paternally
inherited characteristics, and when those characteristics have a greater
effect on male fitness than on female fitness, it is theoretically adaptive
for females to adjust the sex ratio of their offspring in response to the
attractiveness of their mates. Females mated to attractive males might be
expected to favor the production of "sexy sons," who are likely to
father many grandchildren (Weatherhead and
Robertson, 1979). Previous work on the great tit has suggested
that female mate preference is related to the tarsus length of the male
(Blakey, 1994) and size of the
male breast stripe (Norris,
1990), rather than to the quality of his territory. Tarsus length
and breast stripe size are heritable
(Norris, 1993). Therefore,
great tit females mated to large males (in terms of tarsus length) and/or
those with wide stripes are predicted to "over-produce" sons.
Seasonality may also affect the survival and future RS of the sexes in
different ways, and several trends in sex ratio with date have been observed
(e.g., Dijkstra et al., 1990;
Howe, 1977;
Tella et al., 1996;
Zijlstra et al., 1992) and
modeled (Daan et al., 1996). A
seasonally biased sex ratio could be selected for in great tits if fledging
condition, which varies with hatch date, influences the likelihood of
dispersal success. As females disperse more widely they may be overproduced
early in the season when the feeding conditions are good, but males may be
favored later in the season when there is a decline in food quality,
especially in the proportion of caterpillars in the diet
(Perrins, 1990).
Alternatively, a greater abundance of food early in the season may favor
overproduction of the more expensive sex (i.e., males), which would result in
a seasonal decline in sex ratio.
In this study we tested the influence of all these parameters on offspring sex ratio using 5 years of data from a nest-box—breeding population of great tits.
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METHODS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONREFERENCES |
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Data collection
Data were collected in 1991, 1992, 1993, 1996, and 1998 in Marley Wood, Oxfordshire, UK. We monitored all nest-boxes regularly throughout each breeding season to determine clutch size and lay date for each egg. Breeding birds were trapped at the nest using automatic spring-powered traps, on day 7 (day 1 = hatching) or later. We determined the age of each adult from plumage (Svensson, 1992
), and in the
analysis birds were classified as 1 year old or older. The following
biometrics were determined for all captured birds: (1) wing length (maximum
chord, in millimeters); (2) tarsus length (from the nuchal notch to the
furthest extension of the leg with the foot held at a right angle, in
millimeters); and (3) mass (to 0.1 g on a Pesola balance). In addition, a
measurement (in millimeters) was taken of male breast stripe width across the
sternum, which was assumed to be an indicator of quality used by females in
mate choice (Norris, 1990). As
expected for a heritable trait (Norris,
1993), breast stripe width was significantly repeatable for male
birds that were measured in more than 1 year (r =.642,
F9,12 = 4.95, p =.006;
Lessells and Boag, 1987). One
person (J.K.B.) completed all measurements. We visited nests every 3 days after the first egg hatched. All chicks were weighed, measured, and ringed on day 15, when a blood sample was also obtained by brachial venipuncture (under license from English Nature and the Home Office). Blood samples were kept cool in the field and stored at -20°C in the laboratory until DNA extraction.
Molecular sexing
Because the sex of great tit nestlings cannot be determined from
morphological characteristics, we used a DNA test which uses the polymerase
chain reaction (PCR) to amplify part of the W-linked avian chromo-helicase-DNA
binding gene (CHD-W; unique to females) and part of its homologue,
the CHD-Z gene, which is linked to the Z chromosome (occurs in both
sexes; Griffiths et al.,
1998).
DNA was extracted using a commercial kit (Puregene, Gentra Systems, Minneapolis, Minnesota) following the manufacturer's protocol. PCR reactions comprised 1 µl DNA (~ 0.2 µg/ml), 0.2 mM of each dNTP (Pharmacia), 1.2 mM MgCl2, 60 ng each of primers P2.3364 (5'-TCTGCATCGCTAAA-TCCTTT-3') and P8.3221 (5'-CTCCCAAGGATGAGRAAYTG-3'), 0.4 units of Taq polymerase (Taq supreme, Helena Biosciences Ltd.), and 1 µl Taq buffer, in a total volume of 10 µl. All PCR mixtures were overlaid with 15 µl of mineral oil. Reactions were performed with the following temperature profile: initial denaturation at 95°C for 1 min; 30 cycles of denaturation at 94°C for 30 s, annealing at 52°C for 45 s, and template extension at 68°C for 45 s; and final annealing and extension at 52°C for 1 min and 68°C for 5 min, respectively.
We separated PCR products by electrophoresis at 7 V/cm for 1.5 h on 3% agarose gels. PCR products were visualized by UV transillumination after staining with 10 mg/ml ethidium bromide. We sexed birds according to the presence of the PCR products of CHD-Z (380 bases) and CHD-W (455 bases).
Statistical analysis
We examined population sex ratios using G tests for goodness of
fit and contingency. Most analyses were carried out using the brood as the
unit of analysis. Analyses of brood sex ratios were performed using the GLIM
package (version 4; NAG,
1993). The null model was specified with the number of males in a
brood as the dependent variable and brood size as the binomial denominator,
using binomial error distribution and a logit link. For linear models with
several predictor variables, the backward-deletion model simplification
procedure of Crawley (1993)
was followed; the statistical significance of a sex ratio bias in relation to
an independent variable was assessed from the change in deviance
(
D) when that variable was excluded from the model. Other
statistical tests were performed with MINITAB version 12
(Minitab, 1998).
We used tarsus length as an indicator of body size and the regression residual of weight on tarsus length as an indicator of body condition. To avoid pseudoreplication caused by the occurrence of the same individual in more than one year, one data point for each replicated adult (10 males, 12 females) was selected randomly for inclusion in the analysis across years. Brood size could not be entered into the model as a predictor because it was already present as the binomial denominator. Hence, we examined the effect of brood size on sex ratio using arcsine square-root transformed sex ratio data as the response variable in a general linear model.
The brood sex ratios were also tested for departure from binomial
expectation using the deviance in the null model and a randomization procedure
(supplied by C. M. Lessells as a GLIM macro routine). This randomly
reallocated sexed chicks across broods, given the original brood sizes,
thereby deriving binomial variation in sex ratio across broods. After each
randomization, the deviance from the null model was compared to that observed
in the original data; the proportion of randomizations (1000 were performed)
in which the deviance is greater than the null model equates with the
probability of obtaining the observed deviance by chance (e.g.,
Bradbury and Blakey, 1998).
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RESULTS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONREFERENCES |
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Analysis at the level of the individual
In total, 95.5% of the chicks that hatched (n = 912) in 118 broods from 5 different years were sexed; the remainder died before sampling. The chicks that died after hatching, but before being sampled, were evenly distributed between years (
2 = 4.70, df = 4, p
>.30). Overall, 88.2% of the eggs laid in these broods were sexed; no
unhatched eggs were collected for analysis. Unhatched eggs were also evenly
distributed between sample years (2 = 4.70, df = 4, p
>.10). Subsequent analyses are based on sampled, hatched chicks. Overall, 48.5% of 871 hatched chicks were male. This sex ratio did not differ significantly from unity (G1 =.837, p >.30). The highest proportion of males was observed in 1991 (0.587) and the lowest in 1993 (0.437; Table 1). Sex ratios in individual years did not differ significantly from unity, except in 1993 (see G1 values in Table 1). The between-year variation in sex ratios was not significant (G4 = 9.054, p >.05).
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Analysis at the level of the brood
Brood sex ratios varied between 0 and 1.0 (0.50 ± 0.21 SD,
n = 84), but the overall distribution
(Figure 1) was not
significantly different from the binomial expectation (randomization test:
p =.286).
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Combining data across years, the overall proportion of sons did not vary
with year (D4 = 7.431, p >.10), male
breast stripe size (D1 = 1.587, p
>.20), female condition (D1 = 1.262, p
>.20), male age (D1 = 1.229, p
>.20), female tarsus (D1 =.531, p
>.40), female age (D1 =.460, p
>.40), male tarsus (D1 =.216, p
>.60), or male condition (D1 =.005, p
>.90). There was also no trend in sex ratio with laying date
(D1 =.176, p >.60), and the proportion
of male offspring was not related to brood size (F1,83
=.03, p =.87).
A similar analysis was carried out for individual years (Table 2). Although the odd predictor was significant in individual years, no single predictor was consistently significant across all years. The directional trends of individual correlates were never consistent across all years in the study (Table 2).
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONREFERENCES |
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Despite the theoretical prediction that even small relative fitness differences between the sexes may result in large shifts in sex ratio (Charnov, 1993
), most studies
of bird populations have found, at best, only weak evidence that this is the
case. In practice, biased sex ratios are frequently found only in subsets of
data, in studies where sample sizes are small (e.g.,
Burley, 1981) or in data from a
single year (e.g., Ellegren et al.,
1996;
Kölliker et
al., 1999; Lessells et al.,
1996; Svensson and Nilsson,
1996). Comparing our study with others also conducted on the great
tit shows the importance of investigating the effects of variables over a
number of breeding seasons. For example, Lessells et al.
(1996) found a significant
correlation between hatching date and offspring sex ratio, but their findings
were not corroborated by Kölliker et al.
(1999). Our study found a
significant relationship with lay date in only 1 of the 5 years (a seasonal
increase in sex ratio in 1993) but not across years. The number of unhatched
eggs and dead nestlings were equally distributed among the sample years, and
comparisons between the years were not, therefore, confounded. A significant
seasonal effect on sex ratio might only occur when laying dates of the
population are relatively asynchronous, thereby increasing variability in lay
and hatch dates. Nevertheless, in 1993 we found a significant seasonal
increase in sex ratio but the most synchrony in lay dates of any of the years
in our study.
The influence of lay date on sex ratio of offspring may vary between years,
depending on fluctuations in food availability, the likelihood of procuring a
breeding territory in the subsequent season
(Smith and Arcese, 1989), or
the level of local recruitment (Verboven
and Visser, 1998). However, any facultative manipulation of brood
sex ratio in response to parental size and quality should be detected across
all years. Previous studies showing a significant influence of male quality
(e.g., Ellegren et al., 1996;
Kölliker et
al., 1999; Svensson and
Nilsson, 1996) have been based on data from a single year. We
suggest that the findings from such studies should be treated with some
caution. We found no significant effect on sex ratio of any parental
biometrics across all years of our study. Also, no variable significantly
predicted sex ratio in more than 2 of the 5 years when they were considered
separately. Although this lack of significance might be explained by low
sample sizes, it is much more difficult to account for a lack of consistency
in directional trends of sex ratio correlates in the same way. For example, a
significant effect of female tarsus length on sex ratio was found in 2 years;
in 1991 an increase in female tarsus resulted in an increased sex ratio,
whereas the trend was negative in 1998. In addition, our results include
trends in measures that differ from those of previous studies; although
Kölliker et al.
(1999) found a significant
positive effect of male tarsus length on offspring sex ratio, we showed that
male tarsus length was significantly negatively correlated with sex ratio in
1991. Also, despite the measured correlation between male stripe size and male
tarsus length
(Kölliker et
al., 1999), the trends in 1991 and 1992 for these two measures
were in opposite directions. Although this and previous studies have shown
significant effects of measures on sex ratio in some years, the direction of
the effects are not consistent across years, making it impossible to predict
offspring sex ratio in any given year and making adaptive interpretation
difficult.
Although we can identify possible advantages to manipulating offspring sex
ratio in the great tit, no single parameter we have studied is sufficiently
potent to overcome the frequency-dependent selection tending to keep the sex
ratio at unity. At a population level, more of the smaller sex (i.e., females)
were produced overall, but the bias was not significant. However, as the size
dimorphism is only 4%, a large sample would be needed to find a significant
difference in production of the sexes on the basis of this size differential
alone. Also, potential competition between locally recruited sons and their
parents, which might result in local resource competition
(Clark, 1978), is rare in
great tits (Greenwood et al.,
1979). Brood sex ratio variation in any one individual is diluted
out across the population and does not seem to be consistently important.
Although adaptive strategies would result in manipulation of primary sex
ratios, and so differential mortality of the sexes could have influenced the
brood sex ratios determined here, it is the fledging sex ratio that is
important in terms of future RS and survival. Also, there is no evidence of
differential nestling mortality in this population (Blakey JK, unpublished
data).
Given the variety of results found for different populations of the same
species (see Dhondt, 1970;
Drent, 1984;
Kölliker et
al., 1999; Lessells et al.,
1996; Slagsvold and Amundsen,
1992; Smith et al.,
1989), further studies are needed to evaluate whether adaptive sex
ratio manipulation is a consistently important breeding strategy in birds.
Furthermore, despite speculation about putative mechanisms, the physiological
basis of sex ratio adjustment in birds remains a mystery
(Krackow, 1995). Apart from
unusual circumstances, perhaps population sex ratios of 1:1 should not be
surprising given the strong frequency dependence of the character
(Fisher, 1958;
Frank, 1990).
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ACKNOWLEDGEMENTS |
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We thank C. M. Lessells for supplying the GLIM macro routine and R. B. Bradbury, C. M. Perrins, S. J. Reynolds, and three anonymous referees for comments on earlier drafts of the manuscript. A.N.R. was funded by a Biotechnology and Biological Sciences Research Council (BBSRC) studentship, and J.K.B. was supported by a BBSRC research grant.
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