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Evolutionary shift of a tipping point can precipitate, or forestall, collapse in a microbial community

Nature Ecology & Evolution (2024)Cite this article

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Abstract

Global ecosystems are rapidly approaching tipping points, where minute shifts can lead to drastic ecological changes. Theory predicts that evolution can shape a system’s tipping point behaviour, but direct experimental support is lacking. Here we investigate the power of evolutionary processes to alter these critical thresholds and protect an ecological community from collapse. To do this, we propagate a two-species microbial system composed of Escherichia coli and baker’s yeast, Saccharomyces cerevisiae, for over 4,000 generations, and map ecological stability before and after coevolution. Our results reveal that tipping points—and other geometric properties of ecological communities—can evolve to alter the range of conditions under which our microbial community can flourish. We develop a mathematical model to illustrate how evolutionary changes in parameters such as growth rate, carrying capacity and resistance to environmental change affect ecological resilience. Our study shows that adaptation of key species can shift an ecological community’s tipping point, potentially promoting ecological stability or accelerating collapse.

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Fig. 1: Tipping points mark ecosystem states where small changes in the environment can cause large changes in the ecosystem’s equilibrium state.
Fig. 2: Evolution shapes equilibrium frequency and basins of attraction.
Fig. 3: Adaptation to acute stress can shift tipping point behaviour.
Fig. 4: The evolution of growth characteristics, interspecies interactions and resistance have uneven impacts on tipping points.

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Data availability

Raw sequencing reads used to generate the data in this study have been deposited in GenBank under the BioProject identifier PRJNA1023613. Raw FACS data are available via GitHub at https://github.com/ChrisBMircobes/Tipping-Points. Source data are provided with this paper.

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Acknowledgements

M.J.M. was supported by an ARC Discovery grant (DP220103548). The authors thank Finn McDonald for help with illustrations.

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Authors and Affiliations

  1. School of Biological Sciences, Monash University, Clayton, Victoria, Australia

    Christopher Blake, Jake N. Barber, Tim Connallon & Michael J. McDonald

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Contributions

M.J.M. conceived the experiments; C.B., J.N.B. and M.J.M. designed the experiments; C.B. and J.N.B. carried out the experiments; C.B. carried out the topographical analysis; C.B. carried out the sequencing and data analysis; C.B. and T.C. developed the theory; C.B. carried out the simulations; and C.B. and M.J.M. carried out data visualization. All authors wrote the paper.

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Correspondence to Michael J. McDonald.

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Nature Ecology & Evolution thanks P. Catalina Chaparro Pedraza, Annelies Veraart and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Overview of evolution experiments of E. coli and yeast in coculture.

To analyze how evolutionary processes shape the ecosystem topography of a two-species model community, we used strains that had evolved in the lab for ~4000 generations27 as well as cocultures resistant to tetracycline (~70 generations). The blue boxes (a) depict the coevolution of E. coli-yeast pairs. In short, we coevolved E. coli-yeast pairs through daily cycles of 210-fold dilutions and growth in a high glucose media (HGM). Both species reach their carrying capacity within the 24 h growth period, allowing for 10 doublings (210) and thus 10 generations for both E. coli and yeast per daily transfer. We carried out the first 100 transfers (1000 generations) in 96-well plates to ensure high number of replicates, because in most replicates E. coli took over, driving yeast to extinction. For the second round of coevolution, we passaged cocultures daily in 3 mL of HGM in 15 mL falcon tubes. We stored coevolved E. coli-yeast pairs every 70 generations as frozen stock at −80 °C. For the following experiments, we re-isolated clones of E. coli and yeast from coexisting pairs after 1000 and 4000 generations. Panel b depicts the second evolution experiment; the adaptation towards an acute stress. For this we chose the bacteriostatic tetracycline because it only affects growth of E. coli but has no notable effect on yeast’s growth38. At low concentrations of tetracycline, growth of E. coli is boosted39, while at high concentrations E. coli growth is arrested. We evolved tetracycline resistance by passaging E.coli-yeast pairs in media supplemented with sub-minimum inhibitory concentrations for 7 days (Methods). We chose replicates from the highest concentration of tetracycline where, E. coli-yeast pairs remained in stable coexistence (1.5 μg/mL). We re-isolated resistant E. coli clones by passaging for an additional round in cycloheximide to remove yeast and subsequent plating on agar supplemented with tetracycline (4, 8, 16 and 32 μg/mL). We excluded isolates that evolved either very high (>8-fold MIC) or low (<2-fold MIC) resistance. We determined the MIC of remaining isolates and chose one single clone of the ancestral and 4000 generation background with MIC for the following experiments.

Source data

Extended Data Fig. 2 Overview of community stability experiments to measure equilibrium frequency and map tipping points.

We analyzed the geometric properties of ecosystem stability of E. coli-yeast pairs from ancestral, coevolved and tetracycline resistant backgrounds. Panel a shows the clones used in stability mapping experiments (ancestor, coevolved for 1000 generations, coevolved for 4000 generations, tetracycline resistant from ancestral background and tetracycline resistant from generation 4000 background). b depicts the experimental set-up to analyze community stability. The bottom right plot of Panel B shows how we can distinguish E. coli cells from yeast cells based on differences in forward and side scatter caused by the distinct shape and size of E. coli and yeast cells. c displays how we combine the 7-day frequency data from all tested environmental conditions to map out the space of coexistence and the tipping point.

Source data

Extended Data Fig. 3 Overview of the reconstruction of basins of attraction from daily frequency data.

To quantify the basin of attraction, we employ a statistical method that transforms the E. coli frequency distribution into potential (U), a mathematical representation of the systems stability landscape (Methods). Panel (a) illustrates the daily frequency data obtained from flow cytometry. To reconstruct the basin of attraction, data from the initial perturbation on days 0 and 1 are excluded. The remaining frequency data can be visualized as a histogram with E. coli frequency in bins on the x-axis and the frequency of data points within each bin on the y-axis (b). One might expect a higher frequency of data points closer to a stable point. We then fitted a generic function to the probability density curve, where its maxima correspond to the stable states (c). Using the associated Fokker–Planck equation, we approximate the potential function with the probability density function (d). The potential signifies the stored energy of our E.coli-yeast pairs at a given E. coli frequency. Higher potential indicates a larger force driving the system towards the stable attractor. Consequently, the minima of the Potential Function represent stable states—the bottom of the basin—while the maxima signify the tipping point, the boundary between two basins.

Extended Data Fig. 4 Growth rate and population carrying capacity for E. coli and yeast strains used in this study.

With tetracycline (a, b) and without tetracycline (c, d). “Anc” = ancestor, “1000 gen” = 1000 generation coevolved, “4000 gen” = 4000 generation coevolved and “tet res” refers to tetracycline resistant E. coli. Each marker shows the data from an independent growth measurement, each measurement was comprised of at least 5 biological replicates.

Extended Data Fig. 5 Plots of E. coli-yeast co-cultures used to map tipping points and range of coexistence in Fig. 3b–d.

Cultures used to map the tetracycline sensitive co-cultures for the ancestral (fuchsia), 1000 generation evolved (green) and 4000 generation evolved (blue).

Extended Data Fig. 6 Plots of E. coli-yeast co-cultures used to map tipping points and range of coexistence in Fig. 3e, f.

Cultures used to map the tetracycline resistant co-cultures for the ancestral (fuchsia), 1000 generation evolved (green) and 4000 generation evolved (blue).

Supplementary information

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Mathematical appendix and Supplementary Tables 1–6.

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Source data

Source Data Extended Data Figs. 1 and 2

Sheet 1: FACS flow cytometry data of coevolved E. coli–yeast pairs with a range of tetracycline concentrations. These data were used to plot bifurcation diagrams and basins of attraction of Fig. 3b–d. Sheet 2: Flow cytometry data of tet-resistant E. coli–yeast pairs with a range of tetracycline concentrations. These data were used to plot bifurcation diagrams and basins of attraction of Fig. 3e,f.

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Blake, C., Barber, J.N., Connallon, T. et al. Evolutionary shift of a tipping point can precipitate, or forestall, collapse in a microbial community. Nat Ecol Evol (2024). https://doi.org/10.1038/s41559-024-02543-0

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