Inferring stability and persistence in the vaginal microbiome: A stochastic model of ecological dynamics

Abstract

The vaginal microbiome is dynamic, yet the ecological and stochastic forces shaping its stability and persistence remain poorly understood. We developed a multi-species stochastic population model to analyze time-series data from 135 individuals sampled daily over 70 days, integrating ecological theory with microbial dynamics. Our framework explicitly incorporates stochasticity, ecological feedback, and sampling error to quantify community stability. We show that intra- and inter-species interactions and environmental fluctuations critically shape microbial population trajectories. This approach enabled the estimation of species competition coefficients and the identification of distinct stability regimes across individuals. We also introduce a Risk Prediction Monitoring (RPM) tool to track persistence probabilities of key taxa, particularly Lactobacillus spp., mirroring extinction risk models in conservation biology. Our findings challenge static "healthy" vs. "dysbiotic" categorizations and offer a quantitative framework for assessing microbiome resilience. This has direct implications for microbiome-targeted therapies aimed at promoting ecological stability in vaginal bacterial communities.