The absolute difference between them — sexual dimorphism (grey) — can stay nonzero for many generations, changing rapidly when rfm is lower (a, b) and more slowly when rfm is higher (c, d)

Not typically. Supplementary Fig. 6 shows example trajectories of female (red) and male (green) trait means over time at different intersex correlations (rfm). In panel c, for instance, with rfm = 0.95, the two means drift in a correlated fashion near the shared optimum (at 0)

The relationship between sexual dimorphism and intersex correlation: do models support intuition? Abstract. The evolution of sexual dimorphism (the difference in average trait values between females and males, SD), is often thought to be constrained by

Full paper now out in GENETICS:
The relationship between sexual dimorphism and intersex correlation: do models support intuition?
🔗 academic.oup.com/genetics/art...
#Evolution #QuantGenetics @GeneticsGSA

A central focus of the paper is the classic expectation that sexual dimorphism and the intersex genetic correlation should be negatively related.
This intuition is widespread in #QuantitativeGenetics — but our models show it’s not guaranteed. Sometimes the trend can even be positive.

Here’s Fig. 1d: for certain parameter choices, just by chance, the mean trait values in females and males can differ by about half to almost a full phenotypic standard deviation.
👉 Drift alone can create substantial dimorphism.

Why do males and females often differ in traits?
The expected answer: selection.
But our new paper in GENETICS shows that genetic drift alone can generate sexual dimorphism — even when male & female optima are the same