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Predicting population genetic change in an autocorrelated random environment: Insights from a large automated experiment

Most natural environments exhibit a substantial component of random variation, with a degree of temporal autocorrelation that defines the color of environmental noise. Such environmental fluctuations cause random fluctuations in natural selection, affecting the predictability of evolution. But despi...

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Autores principales: Rescan, Marie, Grulois, Daphné, Aboud, Enrique Ortega, de Villemereuil, Pierre, Chevin, Luis-Miguel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8259966/
https://www.ncbi.nlm.nih.gov/pubmed/34161327
http://dx.doi.org/10.1371/journal.pgen.1009611
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author Rescan, Marie
Grulois, Daphné
Aboud, Enrique Ortega
de Villemereuil, Pierre
Chevin, Luis-Miguel
author_facet Rescan, Marie
Grulois, Daphné
Aboud, Enrique Ortega
de Villemereuil, Pierre
Chevin, Luis-Miguel
author_sort Rescan, Marie
collection PubMed
description Most natural environments exhibit a substantial component of random variation, with a degree of temporal autocorrelation that defines the color of environmental noise. Such environmental fluctuations cause random fluctuations in natural selection, affecting the predictability of evolution. But despite long-standing theoretical interest in population genetics in stochastic environments, there is a dearth of empirical estimation of underlying parameters of this theory. More importantly, it is still an open question whether evolution in fluctuating environments can be predicted indirectly using simpler measures, which combine environmental time series with population estimates in constant environments. Here we address these questions by using an automated experimental evolution approach. We used a liquid-handling robot to expose over a hundred lines of the micro-alga Dunaliella salina to randomly fluctuating salinity over a continuous range, with controlled mean, variance, and autocorrelation. We then tracked the frequencies of two competing strains through amplicon sequencing of nuclear and choloroplastic barcode sequences. We show that the magnitude of environmental fluctuations (determined by their variance), but also their predictability (determined by their autocorrelation), had large impacts on the average selection coefficient. The variance in frequency change, which quantifies randomness in population genetics, was substantially higher in a fluctuating environment. The reaction norm of selection coefficients against constant salinity yielded accurate predictions for the mean selection coefficient in a fluctuating environment. This selection reaction norm was in turn well predicted by environmental tolerance curves, with population growth rate against salinity. However, both the selection reaction norm and tolerance curves underestimated the variance in selection caused by random environmental fluctuations. Overall, our results provide exceptional insights into the prospects for understanding and predicting genetic evolution in randomly fluctuating environments.
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spelling pubmed-82599662021-07-19 Predicting population genetic change in an autocorrelated random environment: Insights from a large automated experiment Rescan, Marie Grulois, Daphné Aboud, Enrique Ortega de Villemereuil, Pierre Chevin, Luis-Miguel PLoS Genet Research Article Most natural environments exhibit a substantial component of random variation, with a degree of temporal autocorrelation that defines the color of environmental noise. Such environmental fluctuations cause random fluctuations in natural selection, affecting the predictability of evolution. But despite long-standing theoretical interest in population genetics in stochastic environments, there is a dearth of empirical estimation of underlying parameters of this theory. More importantly, it is still an open question whether evolution in fluctuating environments can be predicted indirectly using simpler measures, which combine environmental time series with population estimates in constant environments. Here we address these questions by using an automated experimental evolution approach. We used a liquid-handling robot to expose over a hundred lines of the micro-alga Dunaliella salina to randomly fluctuating salinity over a continuous range, with controlled mean, variance, and autocorrelation. We then tracked the frequencies of two competing strains through amplicon sequencing of nuclear and choloroplastic barcode sequences. We show that the magnitude of environmental fluctuations (determined by their variance), but also their predictability (determined by their autocorrelation), had large impacts on the average selection coefficient. The variance in frequency change, which quantifies randomness in population genetics, was substantially higher in a fluctuating environment. The reaction norm of selection coefficients against constant salinity yielded accurate predictions for the mean selection coefficient in a fluctuating environment. This selection reaction norm was in turn well predicted by environmental tolerance curves, with population growth rate against salinity. However, both the selection reaction norm and tolerance curves underestimated the variance in selection caused by random environmental fluctuations. Overall, our results provide exceptional insights into the prospects for understanding and predicting genetic evolution in randomly fluctuating environments. Public Library of Science 2021-06-23 /pmc/articles/PMC8259966/ /pubmed/34161327 http://dx.doi.org/10.1371/journal.pgen.1009611 Text en © 2021 Rescan et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Rescan, Marie
Grulois, Daphné
Aboud, Enrique Ortega
de Villemereuil, Pierre
Chevin, Luis-Miguel
Predicting population genetic change in an autocorrelated random environment: Insights from a large automated experiment
title Predicting population genetic change in an autocorrelated random environment: Insights from a large automated experiment
title_full Predicting population genetic change in an autocorrelated random environment: Insights from a large automated experiment
title_fullStr Predicting population genetic change in an autocorrelated random environment: Insights from a large automated experiment
title_full_unstemmed Predicting population genetic change in an autocorrelated random environment: Insights from a large automated experiment
title_short Predicting population genetic change in an autocorrelated random environment: Insights from a large automated experiment
title_sort predicting population genetic change in an autocorrelated random environment: insights from a large automated experiment
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8259966/
https://www.ncbi.nlm.nih.gov/pubmed/34161327
http://dx.doi.org/10.1371/journal.pgen.1009611
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