Long‐term evolutionary conflict, Sisyphean arms races, and power in Fisher's geometric model

Abstract

Evolutionary conflict and arms races are important drivers of evolution in nature. During arms races, new abilities in one party select for counterabilities in the second party. This process can repeat and lead to successive fixations of novel mutations, without a long‐term increase in fitness. Models of co‐evolution rarely address successive fixations, and one of the main models that use successive fixations—Fisher's geometric model—does not address co‐evolution. We address this gap by expanding Fisher's geometric model to the evolution of joint phenotypes that are affected by two parties, such as probability of infection of a host by a pathogen. The model confirms important intuitions and offers some new insights. Conflict can lead to long‐term Sisyphean arms races, where parties continue to climb toward their fitness peaks, but are dragged back down by their opponents. This results in far more adaptive evolution compared to the standard geometric model. It also results in fixation of mutations of larger effect, with the important implication that the common modeling assumption of small mutations will apply less often under conflict. Even in comparison with random abiotic change of the same magnitude, evolution under conflict results in greater distances from the optimum, lower fitness, and more fixations, but surprisingly, not larger fixed mutations. We also show how asymmetries in selection strength, mutation size, and mutation input allow one party to win over another. However, winning abilities come with diminishing returns, helping to keep weaker parties in the game.

The geometry of evolutionary conflict

“…B 290: 20222423 drive the phenotype outwith the battleground into realms that are mutually agreed to be deleterious to fitness, including into orthogonal phenotypic dimensions within which no conflict actually exists. These results contrast with those of a recent simulation analysis [32] of a similar model, inspired by host-pathogen coevolution, in which the phenotype space was limited to the one-dimensional interval between the two optima, which understated the scope for hyper-maladaptation and para-maladaptation. Our finding regarding the propensity for conflict to drive maladaptation in relation to non-conflicted traits has been previously emphasized by Wilkins [33], but for a different reason: his model assumes particular pleiotropic relationships between traits such that optimization in one trait dimension necessarily drives maladaptation in others, whereas our model requires no such assumption and has para-maladaptation arising for purely statistical reasons.…”

Loss of altruism in the social amoebaDictyostelium discoideumis associated with the G protein-coupled receptorgrlG

“…But given their differential expression in chimeras, a time at which cheating or the resistance to cheating would be adaptive (Noh et al 2018), they are likely to include genes involved in conflict and cheating. In support of this reasoning, the 79 genes from Noh et al (2018) show signatures of rapid molecular evolution, consistent with conflict driven escalating arms races (Queller and Strassmann 2018;Scott and Queller 2019). For simplicity, we will refer to these four sets of genes as candidate cheater genes.…”

“…In support of this reasoning, the 79 genes from Noh et al . (2018) show signatures of rapid molecular evolution, consistent with conflict driven escalating arms races (Queller and Strassmann 2018; Scott and Queller 2019). For simplicity, we will refer to these four sets of genes as candidate cheater genes.…”

Evolution of enzyme levels in metabolic pathways: A theoretical approach

“…One part of the RNV corresponds to mutations of small effect, another part to mutations of large effect; the latter push the enzyme concentrations beyond the top of the dome, where flux values are similar to the resident flux. This optimum overshoot is inherent to Fisher's geometric model of adaptive landscapes (Orr 1998), and is more common when phenotypes are close to the optimum (Scott and Queller 2019).…”