Emergent simplicity in microbial ecosystems

Microbes carry out essential functions for global climate, human health, and industry. Investigations into natural microbial communities have uncovered a surprising amount of functionally-relevant diversity at all levels of taxonomic resolution, making predicting community-level function from composition difficult. Nevertheless, recent studies suggest that simple regularities, such as reproducible proportions of functional groups, can emerge from complexity itself. A deeper understanding of such "emergent simplicity" could enable new approaches for predicting the behaviors of the complex ecosystems in nature. However, most examples described so far afford limited predictive power, as identifying features that are approximately reproducible across examples of ecosystems does not necessarily entail an ability to predict functional properties of interest. Here, we propose an information-theoretic framework for quantifying emergent simplicity in empirical data based on the ability of simple models to predict community-level functional properties. Using this framework to analyze two published datasets of synthetic microbial communities, we reveal that as community diversity increases, simple models become more predictive rather than less.