# AEGIS: Individual-based modeling of life history evolution

## TL;DR

AEGIS is a computational tool that simulates how life history traits like lifespan and reproduction evolve in response to ecological and genetic factors.

## Contribution

AEGIS introduces a flexible, individual-based modeling framework for studying life history evolution with detailed demographic and genetic outputs.

## Key findings

- AEGIS models life history traits in response to factors like resource availability, predation, and mutation rates.
- The framework enables analysis of population-level patterns such as aging and lifespan variation.
- AEGIS supports parameter inference and provides transparent, reproducible simulations of evolutionary processes.

## 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.

Life history traits - such as lifespan, age at reproduction, and fertility - vary widely across species and environments and are shaped by evolutionary processes acting over long timescales. Because these processes cannot be directly observed, computational models play a key role in understanding how ecological pressures, demographic constraints, and genetics interact to shape life histories. Here, we introduce AEGIS, an individual-based simulation framework designed to study the evolution of life history traits and aging. In AEGIS, populations are composed of individuals with heritable genomes that determine age-specific survival and reproduction. Population-level patterns, including aging, lifespan variation, and demographic dynamics, emerge from interactions between individuals and their environment rather than being imposed by predefined equations. AEGIS allows users to explore how different mortality sources, resource limitation, reproductive strategies, and genetic architectures influence evolutionary outcomes. The framework produces detailed demographic, phenotypic, and genetic outputs, enabling direct analysis of both population averages and individual variation. By combining flexibility, transparency, and reproducibility, AEGIS provides a general platform for investigating the evolutionary biology of aging and life history in silico.