How does robustness affect evolvability?
Data files
Jun 03, 2025 version files 294.81 MB
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Hardy_2025_S3_simData.csv
4.18 MB
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Hardy_2025_S4_simData.csv
174.99 MB
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Hardy_2025_S5_simData.csv
22.76 MB
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Hardy_2025_S6_simData.csv
92.88 MB
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README.md
2.74 KB
Abstract
Here, by consolidating and extending simple models of how genetic robustness affects the evolvability of phenotypes with discrete states, I uncover three new insights. (i) Environmental robustness can boost evolvability by allowing populations to spread across a migrationally-neutral network of demes, and thereby increasing the diversity of plastic phenotypes that can be accessed by migration. (ii) Counter-intuitively, when adaptive landscapes are complex, an increase in environmental stability can increase the frequency of environmentally-robust but mutationally-sensitive genotypes. This is appears to be due to relaxed selection for mutational robustness in generalists. (iii) Evolvability can be affected by changes in mutational sensitivity, or by changes in the neighborhood of phenotypes accessible by non-neutral mutations. Because it allows for the evolution of increased evolvability without a concomitant increase in genetic load, selection should favor changes in the phenotypic neighborhood over changes in mutational sensitivity. Moreover, with fluctuating selection, the potential gains in evolvability conferred by increased mutational sensitivity can be diminished by selective sweeps on the phenotypic neighborhood.
https://doi.org/10.5061/dryad.brv15dvj8
Codes for models of how mutational and environmental robustness affect evolvability.
Code/Software
Codes for models that explore the effect of mutational and environmental robustness on evolvability.
File Hardy-2025-S1.R is an R implementation of the model of Meyers et al. 2005.
The rest are Eidos codes of individual based model for the SLiM 4 framework (Haller and Messer 2023).
Ancel Meyers, L., Ancel, F. D., & Lachmann, M. (2005). Evolution of genetic potential. PLoS computational biology, 1(3), e32.
Haller, B. C., & Messer, P. W. (2023). SLiM 4: multispecies eco-evolutionary modeling. The American Naturalist, 201(5), E127-E139.
Data
All data were simulated by SLiM models and are in csv format.
Hardy-2025-S3-simData.csv column names are rep (simulation replicate), gen (generation), K (phenotypic neighbrohood size), q (probability of neutral migration), u (mutation rate), fA (frequency of phenotype A), fB (frequency of phenotype B).
Hardy_2025_S4_simData.csv columns names are rep (simulation replicate), gen (generation), Regi (selective regime, that is, the environmental state), K, (phenotypic neighborhood size), mW (mean fitness), mQ (mean probability of neutral mutation), mB (mean frequency of the B phenotype), S (richness of phenotypic neighborhoods), D (Simpson's diversity of phenotypic neighborhoods), mV (mean frequency of the generalist phenotype).
Hardy_2025_S5_simData.csv column names are rep (simulation replicate), gen (generation), lambda (number of generations between environmental fluctuations), rho (ratio of genotype-phenotype-map mutations to genotype mutations), mW (mean fitness), **fV **(mean frequency of the generalist phenotype; called V in the manuscript), **fVk **(mean frequency at the which the generalist phenotype is in the phenotypic neighborhood), mS (mean phenotypic neighborhood richness), tM (the most common genotype-phenotype map), and tg (the most common genotype).
Hardy_2025_S6_simData.csv column names are rep (simulation replicate), gen (generation), Regi (environmental state), u (mutation rate), mW (mean fitness), mQ (mean probability of neutral mutation), mK (mean phenotypic neighborhood size), mB (mean frequency of the B phenotype), mS (mean richness of phenotypic neghborhoods), mD (Simpson's diversity of phenotypic neighborhoods), mV (mean frequency of the generalist genotype), **mVk **(mean frequency of phenotypic neighborhoods containing the generalist phenotype).
This is an analysis of individual based, population genetic simulation models.