Inferring resource competition in microbial communities from time series

TL;DR

This paper introduces spectral analysis methods to infer resource competition structures in microbial communities from time series data, overcoming limitations of simple correlation approaches.

Contribution

It demonstrates that spectral methods like CPSD and coherence effectively reveal resource competition and guild structures in microbial communities using temporal abundance data.

Findings

Spectral methods outperform simple correlations in detecting competition.

Time-delayed effects are crucial for accurate inference.

Application to oceanic plankton data reveals interaction structures.

Abstract

The competition for resources is a defining feature of microbial communities. In many contexts, from soils to host-associated communities, highly diverse microbes are organized into metabolic groups or guilds with similar resource preferences. The resource preferences of individual taxa that give rise to these guilds are critical for understanding fluxes of resources through the community and the structure of diversity in the system. However, inferring the metabolic capabilities of individual taxa, and their competition with other taxa, within a community is challenging and unresolved. Here we address this gap in knowledge by leveraging dynamic measurements of abundances in communities. We show that simple correlations are often misleading in predicting resource competition. We show that spectral methods such as the cross-power spectral density (CPSD) and coherence that account for time-delayed effects are superior metrics for inferring the structure of resource competition in communities. We first demonstrate this fact on synthetic data generated from consumer-resource models with time-dependent resource availability, where taxa are organized into groups or guilds with similar resource preferences. By applying spectral methods to oceanic plankton time-series data, we demonstrate that these methods detect interaction structures among species with similar genomic sequences. Our results indicate that analyzing temporal data across multiple timescales can reveal the underlying structure of resource competition within communities.