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)

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