Handling mean–variance scaling in intraspecific trait variability and implications for trait–environment analyses

Trait‐based ecology increasingly relies on measures of intraspecific trait variability to estimate how trait diversity within species varies across species and environmental gradients. Yet variance and related metrics are often interpreted without considering their intrinsic dependence on the mean, as described by the empirical Taylor's power law. Several approaches have been considered to remove the effect of trait mean, for which we hypothesize that their effectiveness will depend on the skewness in traits. Here we used a hierarchical plant trait dataset with intensive sampling campaign (>5500 individuals), including three key functional traits (leaf area, specific leaf area and flower size), to test mean–variance scaling across scales and regression methods, from mean and variance within individuals to across species within communities. We focussed on alternative variability metrics: variance and standard error, coefficient of variation metrics (CVs), slope of Taylor's power law. We assessed the relative influence of trait mean on trait variability metrics against predictors associated with environmental and biotic gradients. Results show a consistent mean–variance scaling across traits, scales and regression methods. Coherent with expectations for skewed distributions, log transformation already substantially reduced mean–variance dependence, but its effectiveness depends on the variability metric used. CVs on untransformed data and variance of log‐transformed traits showed the lowest dependence on the mean. Our results show that the mean–variance scaling of the Taylor's power law controls patterns of intraspecific trait variability and should be taken into account when assessing trait diversity within species. However, results also highlight that the choice of variability metrics and transformation may help strengthening ecological conclusions.