The orchard plot: Cultivating a forest plot for use in ecology, evolution, and beyond

Classic forest plots show the effect sizes from individual studies and the aggregate effect from a meta-analysis. However, in ecology and evolution, meta-analyses routinely contain over 100 effect sizes, making the classic forest plot of limited use. We surveyed 102 meta-analyses in ecology and evolution, finding that only 11% use the classic forest plot. Instead, most used a forest-like plot, showing point estimates (with 95% confidence intervals [CIs]) from a series of subgroups or categories in a meta-regression. We propose a modification of the forest-like plot, which we name the orchard plot. Orchard plots, in addition to showing overall mean effects and CIs from meta-analyses/regressions, also include 95% prediction intervals (PIs), and the individual effect sizes scaled by their precision. The PI allows the user and reader to see the range in which an effect size from a future study may be expected to fall. The PI, therefore, provides an intuitive interpretation of any heterogeneity in the data. Supplementing the PI, the inclusion of underlying effect sizes also allows the user to see any influential or outlying effect sizes. We showcase the orchard plot with example datasets from ecology and evolution, using the R package,orchard, including several functions for visualizing meta-analytic data using forest-plot derivatives. We consider the orchard plot as a variant on the classic forest plot, cultivated to the needs of meta-analysts in ecology and evolution. Hopefully, the orchard plot will prove fruitful for visualizing large collections of heterogeneous effect sizes regardless of the field of study.

Automated summary

Classic forest plots are often not suitable for meta-analyses in ecology and evolution due to the large number of effect sizes involved. A survey of 102 meta-analyses found that only 11% used classic forest plots, with most opting for a forest-like plot instead. The proposed orchard plot, a modification of the forest-like plot, includes prediction intervals and scaled individual effect sizes, providing a more intuitive interpretation of data heterogeneity and influential outliers.