Summary Statistics

The ABC-SMC model-fitting algorithm used the following nine metrics. The values for each metrics for the 60 genera are provided. The ABC-SMC algorithm, in particular the box-cox correction and PLS regression, performs best on continuous metrics, which is why we did not use the number of species in each ploidal class as metrics. Despite the reduced performance of categorical metrics, we included the highest observed ploidal level because it was highly informative for parameter inference. (1) Total genus size (2) Proportion diploids (3) Proportion tetraploids (4) Proportion species > = 4n (5) Proportion species > = 6n (6) Highest ploidal level observed (7) Proportion highest observed ploidal level (8) Average ploidy level (9) Variance in ploidy level.本次所采用的ABC-SMC模型拟合算法共包含以下九项指标，针对60个属的各项指标数值均已提供。该ABC-SMC算法——尤其是其Box-Cox校正与偏最小二乘（PLS）回归模块——在连续型指标上表现最优，这也是为何我们未将每个倍性类群的物种数量作为指标纳入考量的原因。尽管分类指标的表现有所下滑，但我们仍纳入了观测到的最高倍性水平这一指标，因其对参数推断具有极高的信息量。具体指标如下： (1) 属内总物种数 (2) 二倍体物种占比 (3) 四倍体物种占比 (4) 四倍体及以上倍性物种占比 (5) 六倍体及以上倍性物种占比 (6) 观测到的最高倍性水平 (7) 最高观测倍性水平物种占比 (8) 平均倍性水平 (9) 倍性水平方差