Abstract

Keystone species in ecological communities are native species that play an especially important role in the stability of their ecosystem and can also be potentially used as its main drivers. However, we still lack an effective framework for identifying these species from the available metagenomic data without the notoriously difficult step of reconstructing the detailed network of inter-specific interactions. Here we propose a top-down identification framework, which detects keystones by their total influence on the rest of the species. Our method does not assume pairwise interactions or any specific underlying dynamics and is appropriate to both perturbation experiments and metagenomic cross-sectional surveys. When applied to real metagenomic data of the human gastrointestinal microbiome, we detect a set of candidate keystones and find that they are often part of a keystone module - multiple candidate keystones species with correlated occurrence. The keystones analysis of single-time-point cross-sectional data is also later verified by evaluation of two-time-points longitudinal sampling. Our framework represents a necessary advancement towards the reliable identification of these key players of complex, real-world microbial communities.