Deciphering microbial interactions and detecting keystone species with co-occurrence networks.

Area of Science:

• Microbial ecology
• Ecological network analysis
• Bioinformatics

Background:

• Co-occurrence networks are widely used to infer microbial interactions from sequencing data.
• Validation of these networks is limited due to the complexity of microbial ecosystems.

Purpose of the Study:

• To evaluate the accuracy of co-occurrence networks in recapitulating known microbial interactions.
• To assess the impact of ecological parameters, such as habitat filtering, on network inference.
• To provide a framework for interpreting microbial co-occurrence networks.

Main Methods:

• Simulated multi-species microbial communities using generalized Lotka-Volterra dynamics.
• Constructed co-occurrence networks from simulated data.
• Analyzed network topology and compared inferred interactions with known patterns.
• Investigated the influence of habitat filtering and hub species on network interpretability.

Main Results:

• Co-occurrence networks can accurately represent interaction networks under specific conditions.
• Network interpretability decreases significantly with increasing habitat filtering.
• Spurious correlations create local hotspots around hub species, complicating interpretation.
• Identified topological features indicative of keystone species in co-occurrence networks.

Conclusions:

• Co-occurrence network analysis is a valuable tool for microbial ecology but requires careful interpretation.
• Habitat filtering is a major confounder that can obscure true ecological interactions.
• Understanding network limitations, such as spurious correlations and the impact of hub species, is crucial for accurate ecological inference.