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Emergent predictability in microbial ecosystems

Microbial ecosystems carry out essential functions for global climate, human health, and industry. These complex communities exhibit a surprising amount of functionally relevant diversity at all levels of taxonomic resolution, presenting a significant challenge for most modeling frameworks. A long-standing hope of theoretical ecology is that some patterns might persist despite community complexity -- or perhaps even emerge because of it. 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 they focused on reproducibility rather than prediction. Here, we propose an information-theoretic framework for defining, nuancing and quantifying emergent simplicity in empirical data based on the ability of simple models to predict community-level functional properties. Applying this framework to two published datasets, we demonstrate that the majority of properties measured across both experiments exhibit robust evidence of emergent predictability: surprisingly, as community richness increases, simple compositional descriptions become more predictive. We show that this behavior is not typical within the standard modeling frameworks of theoretical ecology, and argue that improving our ability to predict and control natural microbial communities will require a shift of focus: away from complexity of _ecosystems_, and towards prediction complexity of _properties_ of ecosystems.

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