Fig. 5: Determinant analysis showing the variable importance based on random forest models.
From: Positive feedbacks and alternative stable states in forest leaf types
Seven key covariates of forest leaf phenology composition were used to train the random forest models, whereby we ran each model 100 times on 100 bootstrapping training sets and then computed the mean and standard deviation of the variable permutation importance. A, Variable permutation importance (mean ± std, n = 100, individual data points are overlayed on the bar charts) for global random forest analysis using the bimodality index as response variable. Variables along y-axis in A are ordered by their mean importance. The continuous color scale represents the variable importance from high (yellow) to low (dark blue). B, Variable permutation importance (mean ± std, n = 100) for random forest analysis of plots within deciduous-dominated forests (left panel), bimodal forests (middle panel, including both bistable-deciduous and bistable evergreen forests) and evergreen-dominated forests (right panel). Analyses in panel B all use plot-level relative evergreen abundance as response variable. Source data are provided as a Source Data file.
