Illustrating the importance of meta‐analysing variances alongside means in ecology and evolution

• DOI: 10.1111/jeb.13661
• Publication Date: 2020-06-08T21:49:17Z
• Authors: Alfredo Sánchez‐T...; Nicholas P. Moran; Rose E. O’Dea; Klaus Reinhold; Shinichi Nakagawa
• Keywords: 0106 biological sciences; 570; Coefficient of variation; Parental effects; Ecology; Evolution; Opportunity for selection; bepress|Life Sciences|Ecology and Evolutionary Biology; Biological Evolution; 01 natural sciences; bepress|Life Sciences; Early-life effects; Phenotype; Behavior and Systematics; Variance ratio; Meta-Analysis as Topic; Stress, Physiological; Animals; Genetic Fitness; Variability; bepress|Life Sciences|Ecology and Evolutionary Biology|Evolution

AbstractMeta‐analysis is increasingly used in biology to both quantitatively summarize available evidence for specific questions and generate new hypotheses. Although this powerful tool has mostly been deployed to study mean effects, there is untapped potential to study effects on (trait) variance. Here, we use a recently published data set as a case study to demonstrate how meta‐analysis of variance can be used to provide insights into biological processes. This data set included 704 effect sizes from 89 studies, covering 56 animal species, and was originally used to test developmental stress effects on a range of traits. We found that developmental stress not only negatively affects mean trait values, but also increases trait variance, mostly in reproduction, showcasing how meta‐analysis of variance can reveal previously overlooked effects. Furthermore, we show how meta‐analysis of variance can be used as a tool to help meta‐analysts make informed methodological decisions, even when the primary focus is on mean effects. We provide all data and comprehensive R scripts with detailed explanations to make it easier for researchers to conduct this type of analysis. We encourage meta‐analysts in all disciplines to move beyond the world of means and start unravelling secrets of the world of variance.