Interacting Hosts with Microbiome Exchange: An Extension of Metacommunity Theory for Discrete Interactions

TL;DR

This paper extends metacommunity theory to model microbiome exchange between hosts with discrete interactions, revealing how interaction frequency and exchange strength jointly influence microbiome dynamics.

Contribution

It introduces a new modeling framework that captures discrete host interactions and separates control of interaction frequency and microbiome exchange amount.

Findings

Both interaction frequency and exchange amount are crucial for microbiome convergence.

Analytical approximations are accurate in specific parameter regimes.

Discrepancies between continuous and discrete models highlight the importance of interaction timing.

Abstract

Microbiomes, which are collections of interacting microbes in an environment, often substantially impact the environmental patches or living hosts that they occupy. In microbiome models, it is important to consider both the local dynamics within an environment and exchanges of microbiomes between environments. One way to incorporate these and other interactions across multiple scales is to employ metacommunity theory. Metacommunity models commonly assume continuous microbiome dispersal between the environments in which local microbiome dynamics occur. Under this assumption, a single parameter between each pair of environments controls the dispersal rate between those environments. This metacommunity framework is well-suited to abiotic environmental patches, but it fails to capture an essential aspect of the microbiomes of living hosts, which generally do not interact continuously with each other. Instead, living hosts interact with each other in discrete time intervals. In this paper, we develop a modeling framework that encodes such discrete interactions and uses two parameters to separately control the interaction frequencies between hosts and the amount of microbiome exchange during each interaction. We derive analytical approximations of models in our framework in three parameter regimes and prove that they are accurate in those regimes. We compare these approximations to numerical simulations for an illustrative model. We demonstrate that both parameters in our modeling framework are necessary to determine microbiome dynamics. Key features of the dynamics, such as microbiome convergence across hosts, depend sensitively on the interplay between interaction frequency and strength.