INTRODUCTION
Animal taxa that engage in sexual communication typically show high
among-species diversity in sexual signals (Andersson, 1994; Coyne and
Orr, 2004; Ritchie, 2007; Schaefer and Ruxton, 2015; Wiens and
Tuschhoff, 2020). Sexual signals are generally hypothesized to diversify
due to directional or disruptive selection (Ritchie, 2007; Schaefer and
Ruxton, 2015; West-Eberhard, 2014; Wilkins et al., 2013), although their
evolution is still a mystery in many species. Since sexual signals play
an important role in the origin and maintenance of species and
contribute to biodiversity (Coyne and Orr, 2004), it is important to
assess whether there are constraints to their selection response and
identify the mechanisms that can mitigate those constraints.
Understanding selection responses in sexual signals is challenging,
because signals are often composed of multiple components (Candolin,
2003; Higham and Hebets, 2013; Rowe, 1999). For example, mating songs
can vary both in pitch and in rhythm (Blankers et al., 2015; Tanner et
al., 2017; Wilkins et al., 2015), color signals can be composed of
multiple, functionally distinct patches (Cole and Endler, 2015; Grether
et al., 2004), and sex pheromones are often blends of multiple chemical
compounds (Ferveur, 2005; Linn et al., 1987). To understand how
multicomponent signals evolve, we thus need to consider the selection
response in multiple dimensions simultaneously. Signal components can
have a shared genetic or developmental basis, or can be subject to
correlated selection pressures (Armbruster et al., 2014; Cheverud,
1996). The resulting genetic correlations among signals components can
influence how selection on the phenotype translates to changes in the
underlying genotypes (Chenoweth and Blows, 2006). To understand how the
genotype-phenotype map of sexual signals influences the selection
response, we thus need to determine the genetic correlations between the
different signal components.
Statistical frameworks in quantitative genetics, in particular the
(multivariate) breeder’s equation, allow us to predict and
quantitatively understand selection responses in correlated traits. In
this framework, the response to selection is a function of the genetic
(G) and phenotypic (P) variance-covariance matrix and the selection
gradient: selection acts on the P matrix and the resulting response is
constrained by the G matrix (Lande, 1979; Lande and Arnold, 1983; Lynch
and Walsh, 1998). The difficulty in predicting selection responses of
multivariate traits is that selection acting on multiple components may
be counterbalancing, e.g. directional selection on one trait, but
stabilizing selection on correlated traits, resulting in evolutionary
constraints (Barton and Turelli, 1989). Counterbalancing selection is
likely prevalent in the evolution of sexual signals, as choosing
individuals may favor higher or lower values of some component of the
signal, while changes in correlated components may result in reduced
mate attraction.
These evolutionary constraints can be overcome if genetic variances and
covariances themselves respond to selection, thus reshaping the G matrix
(Arnold et al., 2008; Barton and Turelli, 1989; Eroukhmanoff, 2009;
Jones et al., 2003; Melo and Marroig, 2014; Revell, 2007; Roff and
Fairbairn, 2012). Theory predicts that the G matrix will vary through
time, because on one hand selection erodes genetic variance (Barton and
Turelli, 1989; Estes and Arnold, 2007), while on the other hand mutation
and introgression add new variation, albeit more slowly. Moreover,
genetic correlations can respond to selection directly, especially if
they result from interactions among unlinked genetic loci affecting the
co-expression of multiple traits (Wolf et al., 2005), or from selection
acting on correlations directly (Armbruster et al., 2014; Roff and
Fairbairn, 2012; Svensson et al., 2021). Interestingly, directional
selection can change genetic covariances and increase modularity,
meaning that groups (modules) of traits become genetically independent
from other modules, in a way that facilitates the phenotypic response to
selection (Melo and Marroig, 2014). Empirical work has shown that
patterns of covariation can indeed evolve both across populations and
species in nature (Bégin and Roff, 2003; Berner et al., 2010; Blankers
et al., 2017; Garant et al., 2008; Gosden and Chenoweth, 2014) and
during artificial selection experiments (Careau et al., 2015; Hine et
al., 2011; Uesugi et al., 2017). However, it is still unclear how
changes in the phenotypic selection response are related to changes in
genetic (co)variance through time.
In this study, we explored the response in phenotypic means and genetic
(co)variances to artificial selection on the female sex pheromone of the
moth Heliothis subflexa (Lepidoptera, Noctuidae). Like many other
moths, H. subflexa females secrete a sex pheromone blend to which
conspecific males are attracted. These sex pheromone blends are
species-specific and vary among species in both the presence/absence of
components as well as in relative amounts (or ratios) of the components
(Cardé and Haynes, 2004; Schneider, 1992).
The sex pheromone blend of H. subflexa females consists of 11
compounds, with the following components that are critically important
for conspecific male attraction: (Z)-11-hexadecenal (Z11-16:Ald) as the
major sex pheromone component, (Z)-9-hexadecenal (Z9-16:Ald) and
(Z)-11-hexadecenol (Z11-16:OH) as the two secondary sex pheromone
components, without which H. subflexa males are not attracted
(Groot et al., 2007; Vickers, 2002). Interestingly, the acetate esters
(Z)-7-hexadecenyl acetate (Z7-16:OAc), (Z)-9-hexadecenyl acetate
(Z9-16:OAc), and (Z)-11-hexadecenyl acetate (Z11-16:OAc), from here on
referred to as “acetates”, have a dual role: these acetates attract
conspecific males, while simultaneously repelling males of H.
virescens (Groot et al., 2006; Vickers and Baker, 1997). In geographic
regions where H. virescens is present, acetates are more abundant
in the H. subflexa pheromone compared to where this species is
absent (Groot et al., 2009a). This suggests that the acetates are
subject to divergent selection across a geographic cline. The relative
amounts of the other components are hypothesized to be under stabilizing
selection across the range, as in general in moth pheromone
communication the mean blend is preferred over deviations from the mean
(Allison and Cardé, 2008; Groot et al., 2010; Kárpáti et al., 2013; Linn
et al., 1997; Löfstedt, 1990; Zhu et al., 1997).
To explore the selection response of the acetates, we selected for
higher and lower amounts of acetates during 10 generations of truncation
selection. Since the acetates vary geographically (Groot et al., 2009a)
and have a genetic basis that is partially independent of other
components (Groot et al., 2009b), we hypothesized that the relative
amount of acetates can evolve in response to univariate selection for
higher/lower acetates but that genetic variance will be reduced after
selection. However, since acetates also partially share their genetic
basis with other components and since all sex pheromone compounds are
produced through the same biosynthetic pathway, we also hypothesized
that there will be indirect selection responses in other pheromone
components, specifically in the unsaturated aldehydes, Z9-16:Ald and
Z11-16:Ald, and alcohol, Z11-16:OH (Fig 1). Since some of the
correlations among components may reduce the selection response, we also
expected genetic covariances to change during selection, specifically in
a way that facilitates the phenotypic selection response.