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...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2021-06-01
|
| Series: | PLoS Genetics |
| Online Access: | https://journals.plos.org/plosgenetics/article/file?id=10.1371/journal.pgen.1009611&type=printable |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850159953517477888 |
|---|---|
| author | Marie Rescan Daphné Grulois Enrique Ortega Aboud Pierre de Villemereuil Luis-Miguel Chevin |
| author_facet | Marie Rescan Daphné Grulois Enrique Ortega Aboud Pierre de Villemereuil Luis-Miguel Chevin |
| author_sort | Marie Rescan |
| collection | DOAJ |
| 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. |
| format | Article |
| id | doaj-art-1e5882f74daa42258cbb5a33ec7297cc |
| institution | OA Journals |
| issn | 1553-7390 1553-7404 |
| language | English |
| publishDate | 2021-06-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Genetics |
| spelling | doaj-art-1e5882f74daa42258cbb5a33ec7297cc2025-08-20T02:23:18ZengPublic Library of Science (PLoS)PLoS Genetics1553-73901553-74042021-06-01176e100961110.1371/journal.pgen.1009611Predicting population genetic change in an autocorrelated random environment: Insights from a large automated experiment.Marie RescanDaphné GruloisEnrique Ortega AboudPierre de VillemereuilLuis-Miguel ChevinMost 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.https://journals.plos.org/plosgenetics/article/file?id=10.1371/journal.pgen.1009611&type=printable |
| spellingShingle | Marie Rescan Daphné Grulois Enrique Ortega Aboud Pierre de Villemereuil Luis-Miguel Chevin Predicting population genetic change in an autocorrelated random environment: Insights from a large automated experiment. PLoS Genetics |
| 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 |
| url | https://journals.plos.org/plosgenetics/article/file?id=10.1371/journal.pgen.1009611&type=printable |
| work_keys_str_mv | AT marierescan predictingpopulationgeneticchangeinanautocorrelatedrandomenvironmentinsightsfromalargeautomatedexperiment AT daphnegrulois predictingpopulationgeneticchangeinanautocorrelatedrandomenvironmentinsightsfromalargeautomatedexperiment AT enriqueortegaaboud predictingpopulationgeneticchangeinanautocorrelatedrandomenvironmentinsightsfromalargeautomatedexperiment AT pierredevillemereuil predictingpopulationgeneticchangeinanautocorrelatedrandomenvironmentinsightsfromalargeautomatedexperiment AT luismiguelchevin predictingpopulationgeneticchangeinanautocorrelatedrandomenvironmentinsightsfromalargeautomatedexperiment |