ArticlePLoS computational biology2026
AEGIS: Individual-based modeling of life history evolution.
Article in PLoS computational biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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4 authors.
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Abstract
Nature presents a staggering diversity of life history strategies, ranging from rapid to slow onset of sexual maturity, short or long life, low or high number of offspring, and much more. Each species-specific life history trait reflects on the one hand specific adaptations to unique environments, e.g., nutrient availability, predation, parasite load, seasonality; and on the other hand, depends on past demographic constraints, such as population bottlenecks, migrations, etc. Studying life history diversity in nature and in the laboratory ultimately aims to identify the ecological, demographic, and intrinsic causes contributing to species-specific growth rate distributions, lifetime reproductive outcomes, as well as lifespans. However, for most species, we cannot rewind the evolutionary and demographic past to identify the causal chain of events leading to the present life history traits. We can infer past events only by sampling extant populations. In silico evolution has the advantage of providing complete time resolution for the events driving life history evolution and enables to directly test the impact of ecological and demographic variables on the evolution of life history traits. We developed AEGIS (Aging of Evolving Genomes In Silico), a software for individual-based modeling of life history trait evolution at the genotype and phenotype level. AEGIS models life history traits evolution in response to a set of factors, including resource availability, extrinsic mortality induced by predators or parasites, different levels of germline mutation rates, population size, sexual vs. asexual reproduction, and more. AEGIS serves as a powerful tool to model life history evolution and allows for parameter inference against ground truths. AEGIS can help generate estimates for the evolution of different life history traits, such as age-dependent mortality and reproduction, in response to different selective pressures and intrinsic genetic constraints.
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