Abstract
Sexual selection and sexual conflict play central roles in driving the evolution of male and female traits. Experimental evolution provides a powerful approach to study the operation of these forces under controlled environmental and demographic conditions, thereby allowing direct comparisons of evolutionary trajectories under different treatments such as mating systems. Despite the rapid progress of experimental and statistical techniques that support experimental evolution studies, we still lack clear theoretical predictions on the effects of different mating systems beyond what intuition suggests. For example, polygamy (several males and females in a mating group) and polyandry (one single female and multiple males in a mating group) have each been used as treatments that elevate sexual selection on males and sexual conflict relative to monogamy. However, polygamy and polyandry manipulations sometimes produce different evolutionary outcomes, and the precise reasons why remain elusive. In addition, the softness of selection (i.e., scale of competition within each sex) is known to affect trait evolution, and is an important factor to consider in experimental design. To date, no model has specifically investigated how the softness of selection interacts with different mating systems. Here, we try to fill these gaps by generating clear and readily testable predictions. Our set of models were designed to capture the most important life cycle events in typical experimental evolution studies, and we use simulated changes of sex-specific gene expression profiles (i.e., feminization or masculinization) to quantify trait evolution under different selection schemes. We show that interactions between the softness of selection and the mating system can produce results that have been identified as counterintuitive in previous empirical work such as polyandry producing stronger feminization than monogamy. We conclude by encouraging a stronger integration of modelling in future experimental evolution studies and pointing out remaining knowledge gaps for future theoretical work.