Statistical analyses
All analyses were carried out using the R statistical package (v.
3.5.2). The same model structure was followed for the analysis of all
traits: the substrate cues (i.e., cues left on the patch by virgins or
by mated females that were removed prior to the beginning of the mating
sessions) and the female mating status (i.e., virgin or mated females
present on the patch during the mating session) were fitted as fixed
explanatory variables, whereas block (the day and time of the day at
which the experiment was done) was fitted as a random explanatory
variable (see Table S1).
Copulation duration was examined as “copulation duration of the first
mating only” and “copulation duration across mating events”. In the
analysis of the latter variable, the order of each copulation (i.e.,
whether it was the first, second, third mating, etc) was added as a
covariate. All possible interactions between fixed factors were
included.
The number of mating attempts and the number of mating events were
analysed using a Poisson distribution (glmer , lme4 package; . The
frequency of female acceptance was analysed using a binomial
distribution (glmer , lme4 package), with the formulation of the
dependent variable including the number of female rejections and
acceptances within a cbind function. The duration of the first
mating and the copulation duration across events were tested for
normality and analysed using linear mixed-effect models (lmer ,
lme4 package; . Male survival was analysed using a Cox proportional
hazards mixed-effect model (coxme , coxme package; , with the
death of males being classified as natural or censored.
All maximal models were simplified by sequentially eliminating
non-significant terms from the highest- to the simplest-order
interaction . The significance of the explanatory variables was
determined using Wald F tests, for continuous distributions and
χ2 tests for discrete distributions .