Fig. 5.

Examples of forest plots used in a biological meta-analysis to represent effect sizes and their associated precisions. a A conventional forest plot displaying the magnitude and uncertainty (95% confidence interval, CI) of each effect size in the dataset, as well as reporting the associated numerical values and a reference to the original paper. The sizes of the shapes representing point estimates are usually scaled based on their precision (1/Standard error). Diamonds at the bottom of the plot display the estimated overall mean based on both fixed-effect meta-analysis/‘common-effect’ meta-analysis (FEMA/CEMA) and random-effects meta-analysis (REMA) models. b A forest plot that has been augmented to display a phylogenetic relationship between different taxa in the analysis; the estimated d seems on average to be higher in some clades than in the others. A diamond at the bottom summarizes the aggregate mean as estimated by a multi-level meta-analysis accounting for the given phylogenetic structure. On the right is the number of effect sizes for each species (k), although similarly one could also display the number of individuals/sample-size (n), where only one effect size per species is included. c As well as displaying overall effect (diamond), forest plots are sometimes used to display the mean effects from different sub-groups of the data (e.g., effects separated by sex or treatment type), as estimated with data sub-setting or meta-regression, or even a slope from meta-regression (indicating how an effect changes with increasing continuous variable, e.g., dosage). d Different magnitudes of correlation coefficient (r), and associated 95% CIs, p values, and the sample size on which each estimate is based. The space is shaded according to effect magnitude based on established guidelines; light grey, medium grey, and dark grey correspond to small, medium, and large effects, respectively