Statistical analyses
We analysed differences in mating success between female-exposed and control males using a one-sample Wilcoxon signed rank test with continuity correction; each female was assigned a binary response representing mate choice (“0” for control male, “1” for female-exposed male), with mu set at 0.5 as females should have no preference for either male under H0. We analysed mating latency and duration of female-exposed versus control males using Kruskal-Wallis rank sum tests in which treatment (female-exposed vs control) was the sole categorical predictor. We analysed remating latency of treatment vs control females using a Cox proportional hazard model (Cox, 1972) with treatment as predictor, and we right-censored females that did not remate after the 4 days. We graphically and statistically verified the assumptions of the Cox proportional hazard model using Schoenfeld residuals diagnostics (Schoenfeld, 1982). We analysed female early-life reproductive success (daily offspring production over 7 days) using a general linear mixed model ( “lme4” R package; Bates et al. , 2015) with treatment, day and their interaction as categorical fixed effects, and female ID as random effect. We extracted the absolute values of the residuals-vs-fitted from an initial heteroskedastic model and used them as weights in order to meet the homoskedasticity assumption of the linear model (Midi, Rana and Imon, 2009, 2013). We analysed the effects of sexual perception on female lifetime reproductive success in a general linear model including treatment as the sole categorical fixed effect. Finally, we analysed sperm-offense data in a generalized linear mixed model with a beta-binomial error distribution using the “glmmTMB” R package (to deal with under-dispersion; Brooks et al. , 2017). We transformed this data in order to meet the beta distribution range (i.e. y’= (y*(N- 1)+0.5)/N ; Smithson and Verkuilen, 2006). Treatment was the sole fixed effect predictor included in this model, and batch was the only random effect.
We ran all statistical tests, and produced all figures in R studio 1.1.456 (R Core Team, 2020). For all tests, we set α=0.05, ran type III ANOVA and checked model assumptions using the “performance” R package (Lüdecke et al. , 2021). We corrected for multiple comparison using the Benjamini-Hochberg procedure (1995) for a false discovery rate of 0.05; outcome of this procedure is detailed when relevant (cases of false positive) in the result and discussion sections. We produced all figures using the “ggplot2” R package (Wickham, 2016)