The human gut microbiome is a complex community that harbors substantial ecological diversity at the species level, as well as at the strain level within species. In healthy hosts, species abundance fluctuations in the microbiome community are thought to be stable, and these fluctuations can be described by macroecological laws. However, it is less clear how strain abundances change over time. An open question is whether individual strains behave like species themselves, exhibiting stability and following the macroecological relationships known to hold at the species level, or whether strains have different dynamics, perhaps due to the relatively close phylogenetic relatedness of co-colonizing lineages. In this study, we sought to characterize the typical strain-level dynamics of the healthy human gut microbiome on timescales ranging from days to years. We show that genetic diversity within almost all species is stationary, tending towards a long-term typical value within hosts over time scales of several years, despite fluctuations on shorter timescales. Moreover, the abundance fluctuations of strains can be sufficiently described by a stochastic logistic model (SLM), a model previously used to describe abundance fluctuations among species around a fixed carrying capacity, in the vast majority of cases, suggesting that strains are dynamically stable. Lastly, we find that strain abundances follow the same macroecological laws known to hold at the species level. Together, our results suggest that macroecological properties of the human gut microbiome, including its stability, emerge at the level of strains.

An ecological theory of microbial biodiversity has yet to be developed. This shortcoming leaves patterns of abundance, distribution, and diversity for the most abundant and diverse organisms on Earth without a predictive framework. However, because of their high abundance and complex dynamics, microbial communities may be underpinned by lognormal dynamics, i.e., synergistic interactions among complex stochastic variables. Using a global-scale compilation of 20,456 sites from a diverse set of natural and host-related environments, we test whether a lognormal model predicts microbial distributions of abundance and diversity-abundance scaling laws better than other well-known models, including the most successful macroecological theory of biodiversity, i.e., maximum entropy theory of ecology. We found that the lognormal explains the greatest percent variation in abundance, that the success of the lognormal increased with abundance while other models decreased, and that the lognormal was the only model to reproduce recently documented diversity-abundance scaling laws. Our unifying ecological theory of microbial biodiversity explains and predicts macroecological patterns based on dynamics that capture the complex large number dynamics of microbial life.