Fig. 2

β-diversity of analyzed samples represented by significantly altered (p < 0.05) bacterial taxa, selected by Random Forest machine learning analysis, before and after 7 weeks of the high-intensity training phase (HIT and HITB) as visualized by PCA. SVD with imputation was used to calculate principal components. The X and Y axis show principal component 1 and principal component 2 that explain 21% and 17.4% of the total variance, respectively. Prediction ellipses are such that, with a probability of 0.95, a new observation from the same group will fall inside the ellipse (n = 48 data points). HIT-pre variables before high-intensity training, HIT-post variables after high-intensity training