Predicting coexistence in experimental ecologicalcommunities

Abstract

The study of experimental communities is fundamental to the development of ecology. Yet, for most ecological systems, the number of experiments required to build, model, or analyze the community vastly exceeds what is feasible using current methods. Here, we address this challenge by presenting a statistical approach that uses the results of a limited number of experiments to predict the outcomes (coexistence and species abundances) of all possible assemblages that can be formed from a given pool of species. Using three well-studied experimental systems--encompassing plants, protists, and algae with grazers--we show that this method predicts with high accuracy the results of unobserved experiments, while making no assumptions about the dynamics of the systems. These results suggest a fundamentally different study design for building and quantifying experimental systems, requiring a small number of experiments relative to traditional approaches. By providing a scalable method for navigating large systems, this work provides an efficient way to study highly diverse experimental communities.