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Ageing as an Adaptive Evolutionary Mechanism: Mathematical Model Reveals Convergence of Fertility and Mortality


Core Concepts
Ageing is an adaptive evolutionary mechanism that emerges from the mathematical constraints of a system with reproduction and homeostasis, rather than a byproduct of evolution.
Abstract
The content presents a mathematical model that explores the evolutionary dynamics of ageing. Key insights: The model shows that the end of fertility and the onset of senescence necessarily converge over evolutionary time, regardless of the initial trait values. This explains the observed trade-offs between fertility and lifespan. Populations with a transgenerational "Lansing effect" that transmits ageing information to offspring are more successful than non-Lansing populations, despite the individual fitness cost of ageing. This is because Lansing populations exhibit higher evolvability, generating more genetic variation. The model demonstrates that ageing is not a byproduct of evolution, but an adaptive mechanism that increases a population's ability to explore genotypic space and respond to environmental changes. The mathematical constraints of the model lead to the selection and maintenance of mechanisms coupling fertility and senescence. The analysis provides a formal explanation for the biphasic pattern of ageing, with a clear transition from a healthy phase to a senescent phase. This is observed across diverse organisms, including the evolutionarily conserved "Smurf" phenotype. The model is agnostic to the specific nature of the transgenerational effect, suggesting that the mere transmission of any negative effect from old to young would be sufficient to confer the adaptive benefits of increased evolvability.
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Key Insights Distilled From

by Roget,T., Jo... at www.biorxiv.org 03-14-2022

https://www.biorxiv.org/content/10.1101/2022.03.11.483978v4
A scenario for an evolutionary selection of ageing

Deeper Inquiries

How might the model's predictions change if we incorporate more complex fertility and mortality functions, rather than the binary functions used here

Incorporating more complex fertility and mortality functions into the model would likely lead to more nuanced and realistic predictions about the evolution of ageing. By using binary functions, the model simplifies the dynamics of reproduction and mortality, which may not fully capture the complexities of real-life biological systems. More complex functions could introduce factors such as age-specific fertility rates, varying mortality risks at different ages, and interactions between reproductive success and longevity. These additions could provide a more accurate representation of how ageing evolves in response to different selective pressures and environmental conditions.

What other evolutionary mechanisms or constraints might lead to the selection of ageing as an adaptive trait, beyond the mathematical constraints identified in this model

Beyond the mathematical constraints identified in the model, several other evolutionary mechanisms or constraints could lead to the selection of ageing as an adaptive trait. One potential mechanism is antagonistic pleiotropy, where genes that have beneficial effects early in life but detrimental effects later in life are favored by natural selection. This trade-off between early-life fitness and late-life survival could drive the evolution of ageing. Additionally, environmental factors such as predation pressure, resource availability, and competition within populations could also influence the adaptive value of ageing. For example, in environments with high predation risk, individuals that age rapidly and reproduce quickly may have a selective advantage.

How could experimental evolution studies be designed to further test the model's predictions about the adaptive value of ageing and the role of transgenerational effects in driving the evolution of senescence

Experimental evolution studies could be designed to test the model's predictions about the adaptive value of ageing and the role of transgenerational effects in driving the evolution of senescence. One approach could involve subjecting populations of organisms to different selection pressures that mimic natural environmental conditions. By manipulating factors such as reproductive rates, mortality risks, and the presence of transgenerational effects, researchers could observe how populations evolve over multiple generations. Comparing the outcomes of populations with and without ageing-related traits could provide insights into the adaptive significance of senescence. Additionally, studying the genetic basis of ageing and conducting breeding experiments to select for or against specific ageing-related traits could help elucidate the mechanisms driving the evolution of senescence.
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