Download 1844 genetic constraints and sexual dimorphism in immune defense

Evolution, 59(8), 2005, pp. 1844–1850
GENETIC CONSTRAINTS AND SEXUAL DIMORPHISM IN IMMUNE DEFENSE
JENS ROLFF,1,2 SOPHIE A. O. ARMITAGE,1,3,4 AND DAVID W. COLTMAN1,5,6
and Plant Sciences, University of Sheffield, Sheffield, S10 2TN, United Kingdom
2 E-mail: [email protected]
3 Institute of Biology, Department of Population Biology, University of Copenhagen, Universitetsparken 15, 2100 Copenhagen, Denmark
4 E-mail: [email protected]
5 Department of Biological Sciences, University of Alberta, Edmonton, Alberta T6G 2E9, Canada
6 E-mail: [email protected]
1 Animal
Abstract. The absence of continued evolutionary change despite the presence of genetic variation and directional
selection is very common. Genetic correlations between traits can reduce the evolvability of traits. One intriguing
example might be found in a sexual conflict over sexually dimorphic traits: a common genetic architecture constrains
the response to selection on a trait subjected to sexually asymmetric selection pressures. Here we show that males
and females of the mealworm beetle Tenebrio molitor differ in the quantitative genetic architecture of four traits related
to immune defense and condition. Moreover, high genetic correlations between the sexes constitute a genetic constraint
to the evolution of sexual dimorphism in immune defense. Our results suggest a general mechanism by which sexual
conflict can promote evolutionary stasis. We furthermore show negative genetic correlations, strong indications of
trade-offs, between immune traits for two pairs of traits in females.
Key words.
Conditional evolvability, ecological immunology, genetic correlation, sexual conflict, Tenebrio molitor.
Received December 10, 2004.
Females and males share most of their genes, yet take
different routes to fitness (Bateman 1948; Clutton-Brock
1988). Consequently, genetic correlations between the sexes
constitute an intriguing set of plausible constraints on the
potential for evolution (Lande 1980; Chippindale et al. 2001)
and have the potential to spark intralocus sexual conflict over
many fitness-related traits (Lessells 1999; Chippindale et al.
2001). A classic example of sexual differences in maximizing
fitness is the study by Bateman (1948) that shows that female
Drosophila maximize fitness through longevity, whereas
males maximize fitness through increasing the mating rate.
Such sex differences in life-history strategy predict differences in the optimal investment in immunity: to achieve longevity, females invest more in immune function (Rolff 2002;
Zuk and Stoehr 2002). Males invest more in immediate mating success because in many species longevity does not translate into higher male mating success (Clutton-Brock 1988).
This hypothesis is consistent with phenotypic studies ranging
from insect immune function to immunocompetence in humans (Grossman 1989; Kurtz et al. 2000; Rolff 2002; Zuk
and Stoehr 2002). Molecular genetic studies have demonstrated the gender-dependent expression of genes associated
with bacterial infections in Drosophila (Lazarro et al. 2004).
Therefore, a difference in the quantitative genetics of resistance-related traits between the sexes and constraints on the
sex-specific evolution of investment into immune function
and condition are highly likely, but have not been demonstrated.
To investigate the quantitative genetic basis of sexual dimorphism in immune function, and to determine whether
gender-specific life-history strategies constitute potential
constraints on the evolvability of immune function, we studied the quantitative genetics of three traits that are related to
immune defense and pathogen resistance (cuticular darkness,
hemocyte density and phenoloxidase [PO] activity), fat content as a measure for condition (Rolff and Joop 2002) and
size at maturity in the mealworm beetle Tenebrio molitor. In
T. molitor, as in many other insects, females gain fitness
through longevity: the number of eggs produced per day is
low (Drnevich 2003), therefore a high lifetime reproductive
success can only be achieved through longevity. Males can
mate every 20 minutes and thereby achieve a high lifetime
reproductive success within a short period of time. Moreover,
older males are likely to produce low-quality sperm because
de novo mutations in the male germline occur at much higher
rate than in any other tissue (Radwan 2003 and references
therein).
The insect immune system is composed of different components, and the first line of defense is the cuticle that forms
a physicochemical barrier (Siva-Jothy et al. 2005). Darker
cuticles infer higher pathogen resistance not only in T. molitor
(Barnes and Siva-Jothy 2000), but also in other insect species
(Reeson et al. 1998). The next line of defense is composed
of specialized cells (hemocytes), enzymes, and antimicrobial
peptides in the hemolymph. Higher hemocyte density is positively correlated with higher resistance against invaders (Fellowes and Godfray 2000; Kurtz et al. 2000; Kraaijeveld et
al. 2001). An important enzyme involved in resistance against
several parasites is phenoloxidase. Higher PO activity has
been shown to confer resistance against a variety of parasites
(Nigam et al 1997; Braun et al. 1998). Fat is the chief form
of energy storage in insects and is related to fecundity and
immune defense in a positive manner (Feder et al. 1997; SivaJothy and Thompson 2002).
We estimated the additive genetic components of body
size, condition, and immune defense using a full-sib/half-sib
breeding design and animal model variance component analyses (Lynch and Walsh 1998). From sex-specific variance
components we present sex-specific heritability, evolvability
(Hansen et al. 2003), and genetic correlation estimates. We
also present estimates of the amount of additive genetic variation in each trait ‘‘conditioned’’ upon the additive genetic
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q 2005 The Society for the Study of Evolution. All rights reserved.
Accepted June 6, 2005.
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variance for the same trait in the other sex and their genetic
covariance (Hansen et al. 2003; Jensen et al. 2003). Estimates
of the ‘‘conditional’’ genetic variance, which measures the
amount of additive genetic variance independent of the other
sex (Hansen et al. 2003), enable us to address the question
of how much genetic variation is available for sex-specific
evolution. Moreover, we investigated the nature of genetic
correlations between components of the immune system to
unravel trade-offs.
to reflux with chloroform for 8 h. After fat extraction, beetles
were left in the thimbles, and allowed to air dry overnight.
The following morning, the beetles were removed from the
thimbles, put back into 1.5-ml reaction vials and dried to
constant weight in a drying oven for 6 h at 658C. The beetles
were then weighed to obtain lean (fatless) weight. Fat mass
was calculated as: dry weight plus fat 2 dry weight lean 5
fat mass.
Immune Assays
MATERIAL
AND
METHODS
Breeding Design and Analysis
All sire and dam beetles were taken from a large outbred
stock culture maintained at the University of Sheffield for
five years. We used a full-sib/half-sib breeding design (Lynch
and Walsh 1998). One male (sire) was mated to four females
(dams). We analyzed at least two male and two female offspring per dam (if possible). Nsires 5 45, Noffspring5 870. The
beetles were reared at low densities (20 larva per box, all
from one family, width: 115 mm, length 175 mm, height 50
mm) with ad libitum access to food (10 g of rat chow [special
diet services: 77% cereal, 15% soya bean meal, 5% fish meal,
and 3% vitamins] per larva and a slice of fresh apple biweekly) and water (a 20-ml vial with cotton wool plug, refilled once a week) and kept at a constant temperature of
268C.
Cuticular Darkness, Size, and Fat Content
Cuticular darkness. Tenebrio molitor shows variation in
color from light brown to black (Thompson et al. 2002). The
color of the cuticle is a result of two processes: melanization
and sclerotization (Sugumaran 1991). The term melanization
is frequently used to describe the coloration of an insect’s
cuticle; however, strictly speaking this is not correct. We
prefer to use the term cuticular darkness. To measure cuticular darkness, beetles were defrosted and placed inside a
lightproof box under diffuse indirect illumination from a constant light source (Thompson et al. 2002). A digitized image
of the beetle was taken with a CCD camera (PULNiX TM/
765, San Jose, CA). Cuticular darkness was then measured
using Optimas 6 software (Optimas Corp., Bothell, WA) following the protocol of Thompson et al. (2002). In short,
Optimas 6 produces a weighted average luminance on a grayscale between 0 (darkest) and 255 (lightest).
Size. Using the same equipment, a 10-mm line was drawn
inside the lightproof box, this line was calibrated, and the
elytra length (the length of the hardened fore wings) was
measured using Optimas 6. Elytra length is a reliable and
standard measurement of body size in Coleoptera (Colegrave
1995). We used elytra length as a proxy for body length and
hence size.
Fat extraction. Beetles were put in 1.5-ml reaction vials
and dried to constant weight in a drying oven for 24 h at
658C. The beetles were weighed for dry weight plus fat
weight to the nearest 0.01 mg (Mettler PM 480, MettlerToledo, Leicester, U.K.). Dried beetles were placed in cellulose thimbles (10 mm 3 50 mm, Whatman, Clifton, NJ).
The thimbles were put into soxhlett apparatus, and allowed
Beetles were perfusion-bled with 1-ml sodium cacodylate
(0.01 M NaCac, 0.005 M CaCl2, pH 6.5) and 10 ml kept for
immediate use in a hemocyte count. The rest of the hemolymph/sodium cacodylate solution was frozen (2908C) for
PO analysis. The beetles were also frozen for further analyses.
From each beetle, 10 ml hemolymph/sodium cacodylate solution was pipetted onto a poly-d-lycine-coated slide. We
then added 1 ml of paraformaldehyde (4%) to each well. After
10 min, 1 ml of the DNA stain DAPI was added to each slide.
A Leitz Diaplan microscope (203 magnification; Leica, Wetzlar, Germany), with a UV light attached to enable visualization of the DAPI-stained cells, was used to view the hemocytes. An image of the cells was captured by a digital
camera (PULNiX TM/765) and the number was counted using Optimas 6. Three hemocyte counts were made per individual sample because the whole well could not be observed
in one digitized image.
Phenoloxidase was assayed using a standard protocol
(Moret and Siva-Jothy 2003); in short, using a solution of
140 ml of distilled water, 20 ml of phosphate buffered saline,
20 ml of each sample for PO analysis, and 20 ml of L-DOPA/
sodium cacodylate buffer (1.2 mg/ml). The reaction was allowed to take place in a prewarmed (308C) microplate reader
(Versamax, Molecular Devices, Sunnyvale, CA) and was followed at 308C for 30 min. Softmax PRO 4.0 software (Molecular Devices) calculated the absorbency of the samples
every 11 sec. After 30 min the enzyme activity was determined by examining the slope of the linear phase of the
reaction. The slopes we present are therefore PO activities
during the linear phase of the reaction when the reaction
proceeds at Vmax, the maximum conversion of substrate per
unit time (Barnes and Siva-Jothy 2000).
Quantitative Genetic Analysis
Because our data were unbalanced (i.e., we did not always
have complete data from exactly two offspring of each sex
from each dam), we used a mixed effects ‘‘animal model’’
approach to estimate genetic variance components instead of
least-squares ANOVA to maximize our statistical power
(Lynch and Walsh 1998). Genetic variance components and
heritability were estimated using a restricted estimate maximum likelihood (REML) procedure and an animal model
implemented by the programs PEST and VCE (Groeneveld
and Kovacs 1990; Groeneveld 1995). An animal model was
fitted in which the phenotype of each animal (y) was broken
down into components of additive genetic (a) and residual
value (e): y 5 m 1 a 1 e, where m was the population mean
intercept. The total phenotypic variance (VP) is thus described
as the sum of the REML estimates of the additive genetic
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(1)
where VA(y) denotes the additive genetic variance in y, VA(x)
denotes the additive genetic variance in x, and CovA(xy) is the
additive genetic covariance between traits x and y (Hansen
et al. 2003; Jensen et al. 2003). Conditional heritability and
evolvability (evolvability [Ia] is the additive genetic variance
divided by the square of the trait mean and can be interpreted
as the response of the studied trait to selection if selection
rG
0.91 (0.09)
0.924 (0.08)
1
0.847 (0.07)
1
0.14
0.10
0.15
0.24
0.02
0.13
0.10
0.16
0.14
0.01
(0.03)
(0.17)
(4.45)
(11.82)
(9.77)
9.66
7.62
59.9
115.28
272.33
(0.04)
(0.16)
(4.34)
(6.33)
(11.95)
1.40
6.11***
0.33
6.12***
1.67
0.25
5.18
26.27
2.16
0.78
0.30
4.41
29.6
2.01
1.06
1.21*
1.17*
1.14
1.07
1.35***
0.47
0.54
0.94
0.49
0.30
0.56
0.56
0.91
0.81
0.45
20.87
20.20
0.56
22.84**
21.39
h2m,c
h2f,c
Mean (SE)
Males (m)
t
VAf
VAm
F
h2f
h2m
z
9.73
9.1
57.82
199.6
247.94
[Cov A(xy) ] 2
,
VA(x)
Length
Fat
Cuticular darkness
Hemocytes
PO
VA( y z x) 5 VA( y) 2
Mean (SE)
Females (f)
variance (VA) and the residual variance (VR) as VP 5 VA 1
VR. The narrow sense heritability of each trait was calculated
as h2 5 VA/VP. The VCE program returns standard errors for
composite parameter estimates (i.e., heritability, genetic correlations, and maternal effects) for which statistical significance was assessed by t-tests. We also fitted an animal model
for each trait that included a maternal random effect m to
estimate the variance explained due to maternal identity,
which enabled a partition of the total variance as VP 5 VA
1 VM 1 VR. The environmental maternal effect m2 was then
calculated as m2 5 VM/VP. Because we did not find significant
maternal effects on any trait (P . 0.05 for all) the final genetic
parameters presented are from simple animal models not including the maternal effect. Our design did not allow calculating genetic maternal effects; however, we have calculated a sire model by treating the data as a full-sib/half-sib
ANOVA design. The estimated heritabilities can than be
compared against the estimates of the animal model: the sire
model yields estimates close to the estimates from the animal
model (data not shown) and indicates that maternal genetic
effects are weak. Genetic covariances Cov[x,y] and genetic
correlations were estimated between each pair of traits using
bivariate simple animal models.
We compared the additive genetic variance-covariance G
matrices between the sexes using the program CPC (P. C.
Phillips, CPC: Common Principal Component Analysis program available via http://darkwing.uoregon.edu/;pphil/
programs/cpc/cpc.htm). Here, the genetic variance-covariance matrices are compared by deriving their eigenvalues and
eigenvectors (principal components; Phillips and Arnold
1999). CPC tests the similarity of two matrices in a hierarchical fashion and allows testing not only for the equality of
the matrices but also for other relationships such as proportionality where the matrices have identical eigenvectors. We
used the ‘‘jump-up’’ approach, starting with a test of whether
the matrices have a common principal component or are unrelated (Phillips and Arnold 1999).
The additive genetic variance for a character does not reflect the potential for that character to evolve when selection
acts on genetically correlated characters (Lande 1979; Lande
and Arnold 1983). Hansen et al. (2003) proposed the use of
estimates of conditional genetic variance to better reflect the
evolutionary potential of genetically correlated traits. This
method assesses the ability of a trait to evolve without perturbing a genetically correlated trait that is presumed to be
constrained by strong stabilizing selection. We also estimated
the conditional additive genetic variance (VA(yzx)) for each trait
y that would be available for selection to act upon assuming
stabilizing selection on each other measured trait x as
TABLE 1. Phenotypic means, additive genetic variances (VA), heritabilities (h2), and conditional heritabilities (h2c ) of the traits under study and the genetic correlations between
the sexes (rG). The significance tests are between the sexes for the means, the additive genetic variances (F-test), and the heritabilities (z-test). Significance tests for genetic
and phenotypic correlations indicate whether they are significantly different from zero (* P , 0.05, ** P , 0.01, *** P , 0.001). All genetic correlations between the sexes
were not significantly different from one (note that the optimization for PO between the sexes did not finish and therefore did not obtain a very good estimate for the covariance).
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TABLE 2. Heritability (diagonal), additive genetic correlations (above diagonal), and phenotypic correlations (below diagonal) for the
study traits in (A) females and (B) males. Standard errors of the estimates are in parentheses. Significance tests for genetic and phenotypic
correlations indicate whether they are significantly different from zero (* P , 0.05, ** P , 0.01, *** P , 0.001). Results for significance
tests are bold if they are valid after correction for multiple testing. All heritabilities were significantly different from zero.
Length
Fat
Cuticular darkness
(A) Females
Length
Fat
Cuticular darkness
Hemocytes
PO
0.469 (0.071)
0.39***
20.06
20.05
0.08
0.270 (0.123)**
0.537 (0.070)
20.12**
0.01
0.09*
0.008 (0.170)
20.071 (0.076)
0.937 (0.024)
20.21***
20.17***
20.007 (0.119)
20.165 (0.125)
20.286 (0.082)***
0.490 (0.082)
0.045
0.320
0.328
20.471
0.236
0.298
(0.141)***
(0.130)***
(0.119)***
(0.151)
(0.068)
(B) Males
Length
Fat
Cuticular darkness
Hemocytes
PO
0.558 (0.073)
0.46***
0.06
0.11*
0.10
0.230 (0.117)*
0.556 (0.068)
20.06
0.13**
0.06
0.188 (0.111)*
20.137 (0.096)
0.905 (0.052)
0.07
20.01
20.003 (0.109)
20.120 (0.070)*
20.162 (0.092)*
0.811 (0.078)
0.01
0.255
0.248
20.122
0.100
0.452
(0.136)*
(0.132)*
(0.122)
(0.130)
(0.088)
were as strong as on fitness itself; Hansen et al. 2003) were
calculated following Hansen et al. (2003). Standard errors
for conditional additive genetic variances were obtained by
dividing the estimates by the degrees of freedom (T. F. Hansen, pers. comm.). The degrees of freedom were calculated
by taking the square root of N 2 1 2 dim(x). N denotes the
number of families, dim(x) is the number of variables upon
which the estimates are conditioned (T. F. Hansen, pers.
comm.).
To compare two heritability or genetic correlation estimates i and j we calculated z scores:
z5
xi 2 xj
,
(s 2i 1 s 2j ) 0.5
(2)
where x denotes the parameter estimate and the respective
standard error (Jensen et al. 2003). The statistical significance
was then evaluated by comparing against a large sample standard normal distribution. We compared variance components
using F-tests (Sokal and Rohlf 1995). Standard errors for
phenotypic correlations were calculated following Sokal and
Rohlf (1995). To account for multiple testing we calculated
the P-values following Benjamini and Hochberg (1995).
RESULTS
We found differences in the phenotypic values and in the
quantitative genetics of resistance traits between the sexes
(Tables 1 and 2). Females had a significantly higher fat content and significantly more hemocytes than males. The magnitude of the genetic variances differed between the sexes
(Table 1). The additive genetic variances of length and PO
were significantly larger in males, whereas the additive genetic variance of fat content was larger in females. All heritabilities presented in Table 2 differed significantly from
zero. We detected negative genetic correlations between cuticular darkness and hemocytes in males and females, and
between cuticular darkness and PO in females alone, which
suggests trade-offs between those traits (note that the weak
genetic correlations in males became insignificant when controlled for multiple testing). The negative genetic correlations
between PO and cuticular darkness and hemocytes and cuticular darkness were mirrored in negative phenotypic cor-
Hemocytes
PO
relations in females (Table 2A). This strongly suggests tradeoffs between these traits. In females, body size measured as
elytra length was positively correlated with fat content and
PO, and PO and fat were positively genetically correlated.
Overall, females showed stronger genetic correlations between the study traits than males (Table 2). There was a
gender difference in the G-matrices (test of one principal
component versus unrelated matrices: x2 5 14.4, df 5 4, P
5 0.006).
The evolvabilities (and heritabilities) of all traits under
study were reduced by about 80% when conditioned by additive genetic variation in the other sex (Table 1, Fig. 1). The
genetic correlations for all five traits between the sexes did
not differ significantly from one. Conditional additive genetic
variances did not differ between the sexes (all P . 0.15).
DISCUSSION
We found gender differences in the quantitative genetic
architecture of traits related to immune function. Taken at
face value, this suggests the opportunity for sex-specific investment in immune defense and condition. However, because of strong genetic correlations between the sexes and
the resulting low conditional evolvabilities, the potential for
independent trait evolution in one sex is strongly reduced.
Given the tenet that immunity is costly (Rolff and Siva-Jothy
2003; Schmid-Hempel 2003), females are predicted to allocate more to immune function than males as they gain fitness
through longevity. However, genetic constraints might prevent females from reaching their optimal investment in immune function.
Constraints on sex-specific evolution imposed by a common genetic architecture and sexual conflict over investment
in immune defense potentially bear on host-parasite co-evolution. For example, the reduced evolvability of immune defense components potentially hampers the evolution of increased host investment into costly pathways. This can become important if parasites exert asymmetric selection pressures where one sex, usually the females, pays higher costs
(Braune and Rolff 2001). Recently, Gandon (2004) investigated the impact of different host quality on the life history
of parasites. If the host sexes differ in their level of immune
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FIG. 1. Evolvabilities in the five study traits. Open bar height represents total evolvabilities; filled bars depict the evolvabilities
independent from the other sex.
defense, then they constitute different host types sensu Gandon (2004), thereby exerting asymmetric selection pressures
on the parasites.
We detected negative genetic correlations between resistance-related traits consistent with the idea of a trade-off
(Roff 2002) in the constitutive components of immune defense. The cuticle constitutes the first line of defense, and
darker cuticles confer higher resistance against fungal invasions (Barnes and Siva-Jothy 2000). Our data are consistent
with trade-off between investment in the first line of defense,
cuticular darkness, and defense mechanisms in the hemolymph, the second line of defense, represented by PO and
hemocyte count (Table 2). Recently, Cotter et al. (2004) reported a negative genetic correlation between antibacterial
(lysozyme-like) activity and hemocyte count in the cotton
leafworm, Spodoptera littoralis: a trade-off between two components of immune defense within the insect’s hemolymph.
However, contrasting to our findings, Cotter et al. (2004)
reported positive genetic correlations between hemocyte density and cuticular melanization (note that they measured cuticular darkness) and PO activity and cuticular melanization.
Here we also analyzed gender differences in genetic variance
and found that the strength of genetic correlations in Tenebrio
is gender dependent, because the trade-off between cuticular
darkness and PO only existed in females. Genetic constraints
imposed by sex-dependent genetic architecture therefore have
the potential to shape the structure as well as nature of tradeoffs between different components of immune defense and
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other traits. Our heritability estimates for hemocyte count
were considerably higher than in Cotter et al. (2004), note
that these estimates are not separated for the sexes, and much
higher than the heritability of hemocyte counts in male crickets (Ryder and Siva-Jothy 2001), indicating a potentially
stronger response to selection in Tenebrio. The heritabilities
for cuticular darkness were also higher in Tenebrio (this
study) than in the moth Spodoptera (Cotter et al. 2004); however, the opposite was observed for the heritabilities of PO
activity. Maternal environmental effects, as far as our design
allowed us to account for them, were negligible. However,
this could be caused by the common environment.
We also found additive genetic variance in fat content (Table 1), an important component of condition (Rolff and Joop
2002). The existence of such genetic variation for conditionrelated traits is one of the two major premises for the resolution of the lek paradox, the simultaneous persistence of
female choice in the absence of direct benefits, proposed by
Rowe and Houle (1996). Again, as shown for the immune
traits, the evolvabilities and heritabilities of fat (condition)
are significantly reduced if conditioned on the other sex.
Moreover, defense traits such as PO and hemocyte count are
potentially condition dependent (Feder et al. 1997; Siva-Jothy
and Thompson 2002). We therefore also fitted models that
included fat as a covariate to reflect condition (Rolff and
Joop 2002). However, the additive genetic variances of PO
and hemocytes controlled for fat (data not shown) were reduced by less than 5%. It therefore seems unlikely that gender-specific condition-dependency explains our results.
As recently shown by Jensen et al. (2003), sexual differences in genetic architecture seem to be common in birds, at
least for morphological traits. In a meta-analysis, they report
higher heritabilities in females, and they concluded that females show greater evolutionary responses than males, as
already suggested by Cheverud et al. (1985). As discussed
by Cheverud et al. (1985) and Merilä et al. (1998), gender
differences in heritability or genetic variance can be sufficient
to allow the evolution of sexual differences even with the
genetics correlations being one. Here, we found that we obtained a phenotypic difference in fat content and hemocyte
count despite very high genetic correlations, but we found
gender differences for heritability or additive genetic variance
for these traits (see Table 1). However, our data are inconsistent with the idea that evolutionary responses will be generally larger in females if one only considers the conditional
additive genetic variances that did not differ between the
sexes.
The concept of conditioning the genetic variance is a very
useful tool to examine the role of constraints hampering evolution. As recently pointed out by Hansen and Houle (2004),
many fields of biology (and this clearly applies to behavioral
ecology) are built on the notion that traits are readily evolvable. However, asymmetric selection pressures can result in
gender-specific fitness peaks (Lande 1980). Sex differences
in T. molitor life-history strategies are consistent with a sexual conflict over investment in immune defense; however, as
in almost all species studied in the laboratory we lack lifetime
reproductive success data from the wild.
As shown here, with the anticipated sexual conflict over
immune defense, and elsewhere (Price and Burley 1994), the
genetic architecture is sex dependent and therefore likely to
constitute a constraint on evolvability of sexually dimorphic
traits. Intralocus conflicts such as the one envisaged by Bateman’s principle in immunity are potentially widespread (Lessels 1999).
ACKNOWLEDGMENTS
We thank K. Reinhardt, J. Slate, and two anonymous reviewers for insightful comments that greatly improved the
manuscript and R. Naylor and J. Linklater for help with the
assays. We also thank T. Hansen for statistical advice. JR
was supported by a Natural Environmental Research Council
fellowship while writing and a Marie Curie Fellowship during
the data collection.
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Corresponding Editor: T. Day