Extent of adaptation is not limited by unpredictability of the environment in laboratory populations of <i>Escherichia coli</i>

Karve, Shraddha; Bhave, Devika; Dey, Sutirth

doi:10.1111/jeb.13338

Cited by 10 publications

(10 citation statements)

References 33 publications

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“…For instance, the stochastic variance in selection coefficients over one generation (or infinitesimal time step in continuous time) can be used to predict evolutionary outcomes over multiple generations, such as probabilities of fixation [27,29] or expected heterozygosities [28], analogously to the influence of effective population size for genetic drift. However, while the demographic consequences of the magnitude and autocorrelation of environmental variations have been experimentally explored [35][36][37][38], and evolutionary experiments have been performed under randomly changing environments [39][40][41][42][43], we are not aware of attempts to measure the stochastic variance of population genetic change under conditions where patterns of random environmental fluctuations have been experimentally manipulated.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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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Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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“…(a) The harmonic mean sizes of laboratory populations in existing bacterial experimental evolution studies on fitness costs conducted in fluctuating environments. Reference key: A (Ketola & Saarinen, 2015)*, B (Jasmin & Kassen, 2007a), C (Roemhild et al, 2015), D (Jasmin & Kassen, 2007b), E (Satterwhite & Cooper, 2015), F (Bennett & Lenski, 1999), G (Buckling et al, 2007), H (Buckling et al, 2000), I (Karve et al, 2018) ‡ . *The population size has been calculated indirectly using the stationary phase densities reported for a different species in the selection medium in question and is likely an overestimate; ‡ population size estimate was provided by the authors of Study I.…”

Section: Introductionmentioning

confidence: 99%

Interplay of population size and environmental fluctuations: A new explanation for fitness cost rarity in asexuals

2021

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Theoretical models of ecological specialisation commonly assume that adaptation to one environment leads to fitness reductions (costs) in others. However, experiments often fail to detect such costs. We addressed this conundrum using experimental evolution with Escherichia coli in several constant and fluctuating environments at multiple population sizes. We found that in fluctuating environments, smaller populations paid significant costs, but larger ones avoided them altogether. Contrastingly, in constant environments, larger populations paid more costs than the smaller ones. Overall, large population sizes and fluctuating environments led to cost avoidance only when present together. Mutational frequency distributions obtained from whole‐genome whole‐population sequencing revealed that the primary mechanism of cost avoidance was the enrichment of multiple beneficial mutations within the same lineage. Since the conditions revealed by our study for avoiding costs are widespread, it provides a novel explanation of the conundrum of why the costs expected in theory are rarely detected in experiments.

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“…Using a randomized complete block design (RCBD), we conducted the fitness measurements over six different days, assaying one replicate population of each type in both the environments on a given day (Milliken and Johnson, 2009). We estimated fitness as the maximum growth rate (R) (Kassen, 2014; Ketola and Saarinen, 2015; Vogwill et al , 2016), which was computed as the maximum slope of the growth curve over a moving window of ten readings (Leiby and Marx, 2014; Karve et al , 2015, 2016, 2018; Chavhan et al , 2019a; Chavhan et al , 2019b).…”

Section: Methodsmentioning

confidence: 99%

Larger bacterial populations evolve heavier fitness trade-offs and undergo greater ecological specialization

Chavhan

Malusare

Dey

2020

Preprint

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30Evolutionary studies over the last several decades have invoked fitness trade-offs to explain 31 why species prefer some environments to others. However, the effects of population size on 32 trade-offs and ecological specialization remain largely unknown. To complicate matters, trade-33 offs themselves have been visualized in multiple ways in the literature. Thus, it is not clear how 34 population size can affect the various aspects of trade-offs. To address these issues, we 35 conducted experimental evolution with Escherichia coli populations of two different sizes in 36 two nutritionally limited environments and studied fitness trade-offs from three different 37 perspectives. We found that larger populations evolved greater fitness trade-offs, regardless of 38 how trade-offs are conceptualized. Moreover, although larger populations adapted more to their 39 selection conditions, they also became more maladapted to other environments, ultimately 40 paying heavier costs of adaptation. To enhance the generalizability of our results, we further 41 investigated the evolution of ecological specialization across six different environmental pairs 42 and found that larger populations specialized more frequently and evolved consistently steeper 43 reaction norms of fitness. This is the first study to demonstrate a relationship between 44 population size and fitness trade-offs and the results are important in understanding the 45 population genetics of ecological specialization and vulnerability to environmental changes.

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Extent of adaptation is not limited by unpredictability of the environment in laboratory populations of Escherichia coli

Cited by 10 publications

References 33 publications

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

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

Interplay of population size and environmental fluctuations: A new explanation for fitness cost rarity in asexuals

Larger bacterial populations evolve heavier fitness trade-offs and undergo greater ecological specialization

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