1BRIEF METHOD NOTE 2The Orchard Plot: Cultivating a Forest Plot for Use in Ecology, 3Evolution and Beyond 4Shinichi Nakagawa1, Malgorzata Lagisz1,5, Rose E. O’Dea1, Joanna Rutkowska2, Yefeng Yang1, 5Daniel W. A. Noble3,5, and Alistair M. Senior4,5. 61 Evolution & Ecology Research Centre and School of Biological, Earth and Environmental 7Sciences, University of New South Wales, Sydney, NSW 2052, Australia 82 Institute of Environmental Sciences, Faculty of Biology, Jagiellonian University, Kraków, Poland 93 Division of Ecology and Evolution, Research School of Biology, The Australian National 10University, Canberra, ACT, Australia 114 Charles Perkins Centre and School of Life and Environmental Sciences, University of Sydney, 12Camperdown, NSW 2006, Australia 135 These authors contributed equally 14* Correspondence: S. Nakagawa & A. M. Senior 15e-mail:
[email protected] 16email:
[email protected] 17Short title: The Orchard Plot 18 1 19Abstract 20‘Classic’ forest plots show the effect sizes from individual studies and the aggregate effect from a 21meta-analysis. However, in ecology and evolution meta-analyses routinely contain over 100 effect 22sizes, making the classic forest plot of limited use. We surveyed 102 meta-analyses in ecology and 23evolution, finding that only 11% use the classic forest plot. Instead, most used a ‘forest-like plot’, 24showing point estimates (with 95% confidence intervals; CIs) from a series of subgroups or 25categories in a meta-regression. We propose a modification of the forest-like plot, which we name 26the ‘orchard plot’. Orchard plots, in addition to showing overall mean effects and CIs from meta- 27analyses/regressions, also includes 95% prediction intervals (PIs), and the individual effect sizes 28scaled by their precision.