Gut microbiota ecology: Biodiversity estimated from hybrid neutral-niche model increases with health status and aging

S2 Fig. Example of Preston plot and fit. Preston plot of the sample SRR3679961 of the Kong data set (gray histogram). The box-plots represent the predicted values obtained over the ABC iterations. The magenta and blue lines are two Negative Binomial distributions obtained using the median of the parameters of the ABC iterations respectively concerning “rare” and “abun- dant” species. (EPS) S3 Fig. AIC and BIC of natural cubic spline model. AIC and BIC (y-axis) of the natural cubic spline model adjsuted for sex and scaled total number of reads, when varying the num- ber of degrees of freedom (x-axis). Results for θrare are shown on the left and those for θabundant on the right. For both AIC and BIC, lower values are better. (EPS) S4 Fig. Biodiversity variability within subjects. For each subject of the ELDERMET data set for which data at the 3 time points were available, we plot the values of θrare and θabundant. Sub- jects are sorted according to the minimum average biodiversity. The plots show that the between-samples variability of both θrare and θabundant is higher than the within-sample vari- ability. (EPS) S5 Fig. Principal Component Analysis of the ELDERMET T0 health state. Representation of the ELDERMET elderly subjects (at T0) in the space defined by the first two components of the PCA computed using the FIM score, the Barthel index, the MMSE score and residence set- ting as covariates. Subjects are colored according to the healthy and unhealthy classification that was defined based on the same parameters used for the PCA. (EPS)

S6 Fig. Relationship between the variables of the ELDERMET T0 data set. Heat map of the Pearson’s r correlation matrix computed for the ELDERMET data set at T0. (EPS) S7 Fig. Results of the predictive model of health status in old age. Empirical and predicted values of the first PC. The plotted model was evaluated using as explanatory variables θrare and θabundant, plus calf circumference, BMI and the inflammatory markers IL-6, IL-8, IL-10, TNFa and CRP, and adjusting for sex and standardized total number of reads. AUC of ROC was computed after dichotomizing the subjects health status in healthy/unhealthy. Bisector repre- sents perfect prediction. AUC of ROC results for the alternative predictive models are reported in S2 Table. (EPS) S8 Fig. DS results obtained with classical biodiversity indices. (EPS) S9 Fig. ELDERMET results obtained with classical biodiversity indices. (EPS) S1 Table. Summary of the data sets. For each data set, we summarize the number of individu- als for each decade of age, divided by sex and health status. Here, we considered only subjects that were included in the statistical analysis. (XLSX) S2 Table. Summary of sequencing and bioinformatic processing. Title. For each data set, we report the main sequencing and bioinformatic methodologies applied to samples, including PLOS ONE Gut microbiota biodiversity and healthy aging PLOS ONE | https://doi.org/10.1371/journal.pone.0237207 October 30, 2020 19 / 23