Evolutionary graph theory derived from eco-evolutionary dynamics. (21st June 2021)

• Record Type: Journal Article
• Authors: Pattni, Karan; Overton, Christopher E.; Sharkey, Kieran J.
• Is Part Of: Journal of theoretical biology. Volume 519(2021)
• Publication Date: 2021-06-21
• Subjects: Networks -- Markov process -- Individual-based model -- Ecological dynamics -- Evolutionary dynamics; Biology -- Periodicals; Biological Science Disciplines -- Periodicals; Biology -- Periodicals; Biologie -- Périodiques; Theoretische biologie; Biology; Periodicals; 571.05
• DOI: 10.1016/j.jtbi.2021.110648
• Languages: English
• ISSNs: 0022-5193

Abstract

Highlights: Evolutionary graph theory (EGT) is derived from an eco-evolutionary dynamics model. Extreme assumptions leading to departure from ecological processes are highlighted. EGT dynamics with both birth–death and death-birth components are obtained. Selection amplification in the star network requires dynamics that allow source sites. Clonal interference leads to the failure of key results in EGT. 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.