Evolutionary graph theory derived from eco-evolutionary dynamics

TL;DR

This paper develops a biologically motivated framework for evolution in network-structured populations, integrating eco-evolutionary dynamics and analyzing their impact on mutation spread and selection.

Contribution

It introduces a new eco-evolutionary network model that couples birth and death processes, bridging ecological feedback with evolutionary graph theory.

Findings

Negative ecological feedback suppresses ecological dynamics

Fitness relates to individual life-history traits in birth and death rates

Star networks can inhibit mutation spread under ecological considerations

Abstract

A biologically motivated individual-based framework for evolution in network-structured populations is developed that can accommodate eco-evolutionary dynamics. This framework is used to construct a network birth and death model. The evolutionary graph theory model, which considers evolutionary dynamics only, is derived as a special case, highlighting additional assumptions that diverge from real biological processes. This is achieved by introducing a negative ecological feedback loop that suppresses ecological dynamics by forcing births and deaths to be coupled. We also investigate how fitness, a measure of reproductive success used in evolutionary graph theory, is related to the life-history of individuals in terms of their birth and death rates. In simple networks, these ecologically motivated dynamics are used to provide new insight into the spread of adaptive mutations, both with and without clonal interference. For example, the star network, which is known to be an amplifier of selection in evolutionary graph theory, can inhibit the spread of adaptive mutations when individuals can die naturally.