Avoiding misinterpretation of regression lines in allometry: is sexual dimorphism in digit ratio spurious?

Abstract

The statistical analysis of allometry (size-dependence of traits) is fraught with difficulty that is often underestimated. In light of some recent controversies about statistical methods and the resulting biological conclusions, I here discuss the interpretation of regression lines and show how to avoid spurious effects. General linear models based on ordinary least square (OLS) regression are often used to quantify sexual dimorphism in a trait of interest that is modelled as a function of sex while controlling for size as a covariate. However, an analysis of artificially generated data where males and females differ in size only, but are otherwise built according to the same principles, shows that the OLS method induces a spurious dimorphism where there is none. Hence, OLS-based general linear models should not be regarded as a fail-proof tool that automatically provides the correct answer to whatever question one has in mind. Here I show how to avoid misinterpretation and how to best proceed with answering the recent debate about sexual dimorphism in digit ratio, a trait that is thought to reflect sex-hormone levels during development. The limited data, currently available to me, suggests that the widely accepted sexual dimorphism in digit ratio might well be only a by-product of an allometric shift in shape, urgently calling for a re-examination in larger data sets on humans and other vertebrates.