9AEGIS (Ageing of Evolving Genomes In Silico) is a versatile population-genetics numerical-simulation tool 10 that enables the evolution of life history trajectories under sexual and asexual reproduction and a wide variety 11 of evolutionary constraints. By encoding age-specific survival and reproduction probabilities as discrete ge-12 nomic elements, AEGIS allows these probabilities to evolve freely and independently over time. Simulation 13 of population evolution with AEGIS demonstrates that ageing-like phenotypes evolve in stable environments 14 under a wide range of conditions, that life history trajectories depend heavily on mutation rates, and that sexual 15 populations are better able to accumulate high levels of beneficial mutations affecting early-life survival and 16 reproduction. AEGIS is free and open-source, and aims to become a standard reference tool in the study of 17 life-history evolution and the evolutionary biology of ageing.
18Species in nature vastly differ in life histories, with dramatic variation in maturation rate, lifespan, and fecun-20 dity. In general, age-dependent mortality increases as a function of age while age-dependent fecundity declines, 21 a phenomenon known as ageing or senescence. However, in some organisms mortality decreases or remains 22 constant through life, while fecundity remains constant or increases (Jones et al., 2014). These difference in 23 demography can have important effects on fitness, giving rise to dramatic differences in lifetime reproductive 24 output between species.
25The evolution of age-dependent changes in mortality and reproduction has been an important object of the-26 oretical investigation since the dawn of population genetics, giving rise to a number of theories to explain the 27 widespread occurrence of senescence in nature. Work from Haldane, Medawar, Hamilton and others predicts 28 that the declining force of natural selection after reproductive maturation should inevitably lead to the accumu-29 lation of deleterious gene variants, resulting in increased mortality later in life (Haldane, 1941; Medawar, 1952; 30 Hamilton, 1966; Charlesworth, 2000). While these mutation-accumuation theories of ageing explain ageing as 31 a fundamentally non-adaptive process, other evolutionary theories of ageing suggest senescence could evolve 32 1 as an antagonistic side-effect of positively-selected traits (Williams, 1957), or even as a kin-or group-selected 33 adaptation in its own right (Longo et al., 2005; Lohr et al., 2019).
34Up to now, the evolution of life-history traits, including age-dependent changes in survival and reproduc-35 tion, has primarily been performed using analytical approaches (Hamilton, 1966; Charlesworth, 1994; Fisher, 36 1930); while some simple numerical models exploring the evolution of ageing have been proposed (Penna, 37 1995; Dzwinel et al., 2005; Werfel et al., 2015), there remains a need for a flexible simulation tool to model the 38 evolution of ageing. In particular, a model which permits independent evolut...