Directional and disruptive selection in populations structured by class and continuous ontogeny under incomplete plasticity

Many organisms undergo ontogeny, whereby individuals change in state (e.g., in size, morphology, or condition) as they age. Understanding the evolution of traits influencing ontogeny is challenging because their fitness effects unfold across an individual’s lifetime and may differ between classes such as sexes. Here, we analyse selection on non-plastic traits (e.g., fixed resource allocation strategies) that determine the development of dynamical states throughout life (e.g., body size), with consequences for fecundity and survival in age- and class-structured populations. Using invasion analysis, we derive expressions for directional and disruptive selection that decompose into age- and class-specific components. This allows us to identify convergence stable trait values, assess whether they are uninvadable or potentially experience evolutionary branching, and pinpoint the age and class pathways through which correlational and disruptive selection act. Applying our results to a model of growth under size-mediated sexual selection, we show how selection on growth rates distributes across ages and sexes, and how its relative strength depends on genetic correlations, individual ploidy and life-history. We also show how sex-specific developmental trade-offs and constraints can generate disruptive selection on male growth and favour the evolution of alternative male life histories. More broadly, our results highlight how adaptation is mediated by the interaction of development and demography, and provide tools to investigate how conflicts across ages and classes may influence senescence, sexual dimorphism, and the diversification of ontogenetic strategies.

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Publication Details

Journal
PLoS Computational Biology
Published
2026-04-03
DOI
https://doi.org/10.1371/journal.pcbi.1013591
Primary Topic
Animal Behavior and Reproduction
Type
article
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article

Directional and disruptive selection in populations structured by class and continuous ontogeny under incomplete plasticity

Laurent Lehmann, Arthur Weyna, Charles Mullon
PLoS Computational Biology
Animal Behavior and Reproduction
article

Directional and disruptive selection in populations structured by class and continuous ontogeny under incomplete plasticity

Laurent Lehmann, Arthur Weyna, Charles Mullon
article en

Abstract

Many organisms undergo ontogeny, whereby individuals change in state (e.g., in size, morphology, or condition) as they age. Understanding the evolution of traits influencing ontogeny is challenging because their fitness effects unfold across an individual’s lifetime and may differ between classes such as sexes. Here, we analyse selection on non-plastic traits (e.g., fixed resource allocation strategies) that determine the development of dynamical states throughout life (e.g., body size), with consequences for fecundity and survival in age- and class-structured populations. Using invasion analysis, we derive expressions for directional and disruptive selection that decompose into age- and class-specific components. This allows us to identify convergence stable trait values, assess whether they are uninvadable or potentially experience evolutionary branching, and pinpoint the age and class pathways through which correlational and disruptive selection act. Applying our results to a model of growth under size-mediated sexual selection, we show how selection on growth rates distributes across ages and sexes, and how its relative strength depends on genetic correlations, individual ploidy and life-history. We also show how sex-specific developmental trade-offs and constraints can generate disruptive selection on male growth and favour the evolution of alternative male life histories. More broadly, our results highlight how adaptation is mediated by the interaction of development and demography, and provide tools to investigate how conflicts across ages and classes may influence senescence, sexual dimorphism, and the diversification of ontogenetic strategies.

PLoS Computational BiologyVol. 22(4)
University of Lausanne (CH)
Openalex Percentile: Top 21%
Animal Behavior and Reproduction
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