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Asynchronous abundance fluctuations can drive giant genotype frequency fluctuations

Nature Ecology & Evolution volume 9, pages 166–179 (2025)Cite this article

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Abstract

Large stochastic population abundance fluctuations are ubiquitous across the tree of life, impacting the predictability and outcomes of population dynamics. It is generally thought that abundance fluctuations with a Taylor’s law exponent of two do not strongly impact evolution. However, we argue that such abundance fluctuations can lead to substantial genotype frequency fluctuations if different genotypes in a population experience these fluctuations asynchronously. By serially diluting mixtures of two closely related Escherichia coli strains, we show that such asynchrony can occur, leading to giant frequency fluctuations that far exceed expectations from genetic drift. We develop an effective model explaining that the abundance fluctuations arise from correlated offspring numbers between individuals, and the large frequency fluctuations result from (even slight) decoupling in offspring number correlations between genotypes. The model quantitatively predicts the observed abundance and frequency fluctuation scaling. Initially close trajectories diverge exponentially, suggesting that chaotic dynamics may underpin the excess frequency fluctuations. Our findings suggest that decoupling noise is also present in mixed-genotype Saccharomyces cerevisiae populations. Theoretical analyses demonstrate that decoupling noise can strongly influence evolutionary outcomes, in a manner distinct from genetic drift. Given the generic nature of these frequency fluctuations, we expect them to be widespread across biological populations.

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Fig. 1: Observation of large genotype frequency fluctuations.
Fig. 2: Model of population fluctuations.
Fig. 3: Within-cycle chaotic dynamics of genotype frequencies.
Fig. 4: Quantifying the relative strength of intrinsic noise, extrinsic noise and the effect of sharing mother cultures.
Fig. 5: Extrinsic and intrinsic decoupling noise found in barcoded S. cerevisiae populations.
Fig. 6: Theoretical evolutionary implications of decoupling noise.

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Data availability

All data presented in this manuscript are available via GitHub at https://github.com/joaoascensao/giantpopflucts and Zenodo at https://doi.org/10.5281/zenodo.13787815 (ref. 63). All strains presented in this paper are available upon request.

Code availability

All code presented in this manuscript is available via GitHub at https://github.com/joaoascensao/giantpopflucts and Zenodo at https://doi.org/10.5281/zenodo.13787815 (ref. 63).

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Acknowledgements

We thank A. Arkin, K. Buttrey, B. Good, J. Denk, K. Wetmore, Q. Q. Yu, J. Ye, D. Nayak and all members of the Hallatschek lab (past and present) for helpful comments and advice on the project. We thank R. Lenski for sending us the LTEE-derived strains and populations, along with experimental advice and feedback. We thank T. Cooper for sending us the REL606 ΔpykF mutant. Research reported in this publication was supported by a National Science Foundation CAREER Award (grant no. 1555330 to O.H.). This work was supported by the National Institute of General Medical Sciences of the NIH under award R01GM115851 (O.H.) and by a Humboldt Professorship of the Alexander von Humboldt Foundation (O.H.). J.A.A. acknowledges support from an NSF graduate research fellowship, a Berkeley fellowship (from UC Berkeley) and Lloyd and Brodie scholarships (from UC Berkeley Dept of Bioengineering). We thank M. West of the Cell and Tissue Analysis Facility (CTAF) at UC Berkeley. This work was performed in part in the QB3 CTAF, that provided the ThermoFisher Attune Flow Cytometer (2017 model).

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Author notes

  1. Kristen Lok

    Present address: Department of Biomedical Engineering, Duke University, Durham, NC, USA

Authors and Affiliations

  1. Department of Bioengineering, University of California Berkeley, Berkeley, CA, USA

    Joao A. Ascensao & Kristen Lok

  2. California Institute for Quantitative Biosciences, University of California Berkeley, Berkeley, CA, USA

    Joao A. Ascensao

  3. Department of Physics, University of California Berkeley, Berkeley, CA, USA

    Oskar Hallatschek

  4. Department of Integrative Biology, University of California Berkeley, Berkeley, CA, USA

    Oskar Hallatschek

  5. Peter Debye Institute for Soft Matter Physics, Leipzig University, Leipzig, Germany

    Oskar Hallatschek

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  1. Joao A. Ascensao
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Contributions

J.A.A. and O.H. designed the project. J.A.A. and K.L. performed the experiments and analysed the data. J.A.A. and O.H. wrote and revised the paper.

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Correspondence to Oskar Hallatschek.

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Extended data

Extended Data Fig. 1 Simulations of chaotic dynamics in a simple two-genotype system.

(A) We model the dynamics of two genotypes, A and B, as coupled logistic maps (in discrete time). The model we analyse here is not intended to represent the underlying dynamics of the S/L system, but rather serves to demonstrate generic properties of chaotic dynamics. The parameters KA and KB are the carrying capacities of strains A and B, respectively. When the coupling parameter, c, goes to zero, we recover standard, independent logistic maps. Here, we consistently use r = 3.9, which puts the populations in the chaotic regime. (B-C) Examples of the abundance and genotype frequency dynamics. Here, we use c = 0.1. Even though the abundance dynamics are mildly coupled to each other, we still see fluctuations creeping into the genotype frequency dynamics. (D-E) Mean-variance scaling behaviors of the population abundance and genotype frequency. In both cases, we see power-law scaling with exponents of two, indicating the presence of effective offspring number correlations and decoupling noise. We varied the carrying capacities over several orders of magnitude to change the abundance and frequency. We computed the mean and variance of trajectories ran for 105 iterations, and discarded the first 104 iterations (to control for transient behaviors). We see that the degree of coupling does not affect the power-law exponent, but can change the intercept (as expected).

Extended Data Fig. 2 Correlated fluctuations between barcoded clones, from Venkataram et al. data.

Using the previously analyzed barcoded S. cerevisiae data45, we computed the pairwise correlation in log-displacement between every pair of high-frequency clones over every time point, replicate, and batch, that is corr \((\Delta \log {f}_{i,t},\Delta \log {f}_{j,t})\). Points represent correlation coefficients for every pair of clones, and bars represent the average (n = 3321, 190, 1640; left to right). Error bars represent 95% CIs. We see that, on average, pairs of haploid clones have highly correlated displacements, followed by pairs of diploid clones, and then pairs consisting of one haploid and one diploid clone.

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Ascensao, J.A., Lok, K. & Hallatschek, O. Asynchronous abundance fluctuations can drive giant genotype frequency fluctuations. Nat Ecol Evol 9, 166–179 (2025). https://doi.org/10.1038/s41559-024-02578-3

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