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Macronutrient intakes and the lifespan-fecundity trade-off: a geometric framework agent-based model


Hosking, CJ and Raubenheimer, D and Charleston, MA and Simpson, SJ and Senior, AM, Macronutrient intakes and the lifespan-fecundity trade-off: a geometric framework agent-based model, Journal of the Royal Society Interface, 16, (151) Article 20180733. ISSN 1742-5689 (2019) [Refereed Article]

Copyright Statement

2019 The Author(s) Published by the Royal Society. All rights reserved.

DOI: doi:10.1098/rsif.2018.0733


Lifespan and fecundity, the main components in evolutionary fitness, are both strongly affected by nutritional state. Geometric framework of nutrition (GFN) experiments has shown that lifespan and fecundity are separated in nutrient space leading to a functional trade-off between the two traits. Here we develop a spatially explicit agent-based model (ABM) using the GFN to explore how ecological factors may cause selection on macronutrient appetites to optimally balance these life-history traits. We show that increasing the risk of extrinsic mortality favours intake of a mixture of nutrients that is associated with maximal fecundity at the expense of reduced longevity and that this result is robust across spatial and nutritional environments. These model behaviours are consistent with what has been observed in studies that quantify changes in life history in response to environmental manipulations. Previous GFN-derived ABMs have treated fitness as a single value. This is the first such model to instead decompose fitness into its primary component traits, longevity and fecundity, allowing evolutionary fitness to be an emergent property of the two. Our model demonstrates that selection on macronutrient appetites may affect life-history trade-offs and makes predictions that can be directly tested in artificial selection experiments.

Item Details

Item Type:Refereed Article
Keywords:GPU, agent-based modelling, appetite, in silico evolution, life histories, nutritional geometry
Research Division:Mathematical Sciences
Research Group:Applied mathematics
Research Field:Biological mathematics
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in the biological sciences
UTAS Author:Charleston, MA (Professor Michael Charleston)
ID Code:152674
Year Published:2019
Web of Science® Times Cited:7
Deposited By:Physics
Deposited On:2022-08-23
Last Modified:2022-09-07

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