Abstract Measuring heterogeneity, or inconsistency, among effect sizes is a crucial step for interpreting meta‐analytic evidence across diverse taxonomic groups and spatiotemporal contexts. However, ecologists and evolutionary biologists often interpret overall mean effects (mean population effects) as consistent across contexts, either explicitly or implicitly, without properly quantifying and interpreting heterogeneity. Here, we present a pluralistic approach that aims to quantify heterogeneity by introducing complementary metrics, each of which decomposes heterogeneity into within‐study, between‐study and between‐species (species and phylogenetic) variances. These metrics include the traditional I 2 (variance‐standardized metric), the newly derived coefficient of variation for heterogeneity ( CVH family; mean‐standardized metric), the second‐order coefficient of variation ( M family; variance–mean‐standardized metric) and their stratified variants. To demonstrate the benefits of the combined use of these measures, we synthesize heterogeneity estimates from 512 ecological and evolutionary meta‐analyses. We show that total heterogeneity (variance of true effects) is, on average, 10 times larger than statistical noise (sampling error variance), contributing to 91% of the observed variance (median I 2 = 91%). This amount of heterogeneity is nearly twice the size of the mean population effect (median CVH = 1.8 and M = 0.6), indicating substantial variation among studies within a meta‐analysis. Moreover, different effect size types yield different values of heterogeneity metrics because they are inherently influenced by statistical properties of their effect size estimators. As such, comparisons of heterogeneity across effect size types should be made with caution, albeit the proposed heterogeneity metrics are unit‐free. Our large‐scale synthesis also provides new benchmarks for the interpretation of heterogeneity and recommendations on how to quantify and report heterogeneity. New extensions for stratifying heterogeneity metrics will clarify our understanding of the generalisability, and at what level of meta‐analytic effects in ecology and evolution.
Yang et al. (Tue,) studied this question.