The recent realization that human-associated microbial communities play a crucial role in determining our health and well-being 1, 2 has led to the ongoing development of microbiome-based therapies 3 such as fecal microbiota transplantation 4, 5 . Thosemicrobial communities are very complex, dynamic 6 and highly personalized ecosystems 3, 7 , exhibiting a high degree of inter-individual variability in both species assemblages 8 and abundance profiles 9 . It is not known whether the underlying ecological dynamics, which can be parameterized by growth rates, intra- and inter-species interactions in population dynamics models 10 , are largely host-independent (i.e. “universal”) or host-specific. If the inter-individual variability reflects host-specific dynamics due to differences in host lifestyle 11 , physiology 12 , or genetics 13 , then generic microbiome manipulations may have unintended consequences, rendering them ineffectual or even detrimental. Alternatively, microbial ecosystems of different subjects may follow a universal dynamics with the inter-individual variability mainly stemming from differences in the sets of colonizing species 7, 14 . Here we developed a novel computational method to characterize human microbial dynamics. Applying this method to cross-sectional data from two large-scale metagenomic studies, the Human Microbiome Project 9, 15 and the Student Microbiome Project 16 , we found that both gut and mouth microbiomes display pronounced universal dynamics, whereas communities associated with certain skin sites are likely shaped by differences in the host environment. Interestingly, the universality of gut microbial dynamics is not observed in subjects with recurrent Clostridium difficile infection 17 but is observed in the same set of subjects after fecal microbiota transplantation. These results fundamentally improve our understanding of forces and processes shaping human microbial ecosystems, paving the way to design general microbiome-based therapies 18 .