One dataset, four meta-analyses: synthesising mean effects and heterogeneity within and among populations.

Ecological syntheses (meta-analyses) usually ask 'what is the average effect?' But many questions also depend on whether outcomes become more or less variable and whether effects are predictable across contexts. We show how one dataset can support a workflow that synthesises both mean effects and within-group variability while also separating heterogeneity among study-level effects from residual heterogeneity among effect sizes within studies. Using an organic versus conventional crop-yield dataset, we analyse mean effects with the log response ratio (lnRR; Model 1) and within-group variability with the log variability ratio (lnVR) and log coefficient of variation ratio (lnCVR; Model 2), all calculated from the same summary statistics (means, s.d.s. and sample sizes). We then use multilevel location-scale meta-regression to test whether fertiliser type shifts expected lnRR and lnVR/lnCVR effects (Models 3 and 4) and whether residual and study-level heterogeneity differ between contexts. These heterogeneity components respectively inform consistency among represented comparisons and transferability to a new study. Analysing mean effects, within-group variability and context-dependent heterogeneity together can provide a more informative description of ecological responses than focusing on average effects alone.