Learning the functional landscape of microbial communities
Microbial consortia exhibit complex functional properties in contexts ranging from soils to bioreactors to human hosts. Understanding how community composition determines emergent function is a major goal of microbial ecology. Here we address this challenge using the concept of community-function landscapes - analogs to fitness landscapes - that capture how changes in community composition alter collective function. Using datasets that represent a broad set of community functions, from production/degradation of specific compounds to biomass generation, we show that statistically-inferred landscapes quantitatively predict community functions from knowledge of strain presence or absence. Crucially, community-function landscapes allow prediction without explicit knowledge of abundance dynamics or interactions between species, and can be accurately trained using measurements from a small subset of all possible community compositions. The success of our approach arises from the fact that empirical community-function landscapes are typically not rugged, meaning that they largely lack high-order epistatic contributions that would be difficult to fit with limited data. Finally, we show this observation is generic across many ecological models, suggesting community-function landscapes can be applied broadly across many contexts. Our results open the door to the rational design of consortia without detailed knowledge of abundance dynamics or interactions.