Artificial selection of communities drives the emergence of structured interactions

TL;DR

This paper develops a theoretical model to understand how artificial selection on microbial communities leads to structured interactions, revealing that selection for community functions fosters mutualism and diversity.

Contribution

It introduces a general model for the evolution of complex communities and shows how selection induces low-dimensional structured interactions from initially unstructured systems.

Findings

Selection for community functions creates structured, low-dimensional interaction matrices.

Artificial selection for larger abundance promotes mutualism and diversity.

Interaction structure emergence can be inferred from accessible community measures.

Abstract

Species-rich communities, such as the microbiota or microbial ecosystems, provide key functions for human health and climatic resilience. Increasing effort is being dedicated to design experimental protocols for selecting community-level functions of interest. These experiments typically involve selection acting on populations of communities, each of which is composed of multiple species. If numerical simulations started to explore the evolutionary dynamics of this complex, multi-scale system, a comprehensive theoretical understanding of the process of artificial selection of communities is still lacking. Here, we propose a general model for the evolutionary dynamics of communities composed of a large number of interacting species, described by disordered generalised Lotka-Volterra equations. Our analytical and numerical results reveal that selection for scalar community functions leads to the emergence, along an evolutionary trajectory, of a low-dimensional structure in an initially featureless interaction matrix. Such structure reflects the combination of the properties of the ancestral community and of the selective pressure. Our analysis determines how the speed of adaptation scales with the system parameters and the abundance distribution of the evolved communities. Artificial selection for larger total abundance is thus shown to drive increased levels of mutualism and interaction diversity. Inference of the interaction matrix is proposed as a method to assess the emergence of structured interactions from experimentally accessible measures.