Ecological ModellingEvolutionary BiologyPeer-reviewed journal article
Agent-based modelling of a nematode system provides novel insights into the evolutionary constraints and modulators of phenotypic plasticity, bet-hedging, and environmental homeostasis
Abstract
In this study, I implemented an agent-based model aimed at exploring the competition between plastic and non-plastic genotypes of the dimorphic nematode Pristionchus pacificus in a digital environment with periodic fluctuations in food resources. Simulation scenarios monitoring frequency and time until fixation of two alleles of the developmental switch gene eud-1 were performed to capture emerging eco-evolutionary patterns generated by the interplay of three main variables, namely the intrinsic cost of plasticity, the timescale of environmental fluctuations, and the individual degree of plasticity. Interestingly, while intermediate-to-long periods of environmental stability and higher levels of plasticity might favour plastic strategists in a cost-free condition, the introduction and increase of inherent costs of plasticity could select for genotypes with either very low or high sensitivity to environmental cues, induce a sequential collapse in the frequency of fixation of plastic strains and time of coexistence between strains, and make invasions by non-plastic mutants/immigrants more likely until a plateau is reached. In addition, asymmetries in fitness between the two alternative phenotypes might be an almost necessary condition to enable the invasion of a non-plastic population by plastic genotypes. Collectively, while confirming previous theoretical findings, these outcomes may also uncover the sensitivity of a nematode system involving stochastic, conditional, and constitutive phenotype production to even small changes in key variables, suggesting the existence of phase transitions and critical evolutionary constraints.
- research focus: synaptic & neural circuits
- research focus: host-pathogen & immune response
- research focus: development & morphogenesis
- research focus: chromatin & epigenetic regulation
- approach: deep learning
- approach: statistical model
- Citations
- 0
- FWCI
- 0.00
- Field percentile
- top 41%
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