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Phenotypic memory drives population growth and extinction risk in a noisy environment

Abstract

Random environmental fluctuations pose major threats to wild populations. As patterns of environmental noise are themselves altered by global change, there is a growing need to identify general mechanisms underlying their effects on population dynamics. This notably requires understanding and predicting population responses to the colour of environmental noise, in other words its temporal autocorrelation pattern. Here, we show experimentally that environmental autocorrelation has a large influence on population dynamics and extinction rates, which can be predicted accurately provided that a memory of past environment is accounted for. We exposed nearly 1,000 lines of the microalgae Dunaliella salina to randomly fluctuating salinity, with autocorrelation ranging from negative to highly positive. We found lower population growth, and twice as many extinctions, under lower autocorrelation. These responses closely matched predictions based on a tolerance curve with environmental memory, showing that non-genetic inheritance can be a major driver of population dynamics in randomly fluctuating environments.

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Fig. 1: Salinity, growth rates and population dynamics for two time series with different environmental autocorrelations.
Fig. 2: Population dynamics in a stochastic environment.
Fig. 3: Population growth rate in response to environmental autocorrelation.
Fig. 4: Intracellular glycerol concentration in genotype C after transfer to a new salinity.
Fig. 5: Population extinctions across environmental autocorrelations.

Data availability

The population dynamics and glycerol acquisition data that support the findings of this study are available from the Dryad digital repository50.

Code availability

R, C++ and Mathematica codes are available from the Dryad digital repository50.

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Acknowledgements

This work was funded by the European Research Council under the European Union’s Horizon 2020 research and innovation programme (grant no. STG-678140-FluctEvol). We thank the MRI-IGMM platform for access to the cell sorter, the GenSeq platform (LaBEX CEMEB, Montpellier) for sequencing and O. Cotto, C.A. Klausmeier, R. Lande and J. Tufto for helpful feedback.

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Authors

Contributions

L.-M.C. and M.R. designed the experiment. D.G. and M.R. conducted the population dynamics experiment. L.-M.C. and M.R. performed the statistical analysis. D.G. and M.R. extracted and amplified barcode genes of all lines and D.G., E.O.-A. and M.R. analysed the bioinformatics data.

Corresponding author

Correspondence to Luis-Miguel Chevin.

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The authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Population dynamics summary for each of the six ancestral genotypes.

(same colour as Fig. 2). a. Distribution of r: lines represent the estimated distribution fitted in the gamma model (solid lines) and histograms are based on the 3119 realized growth rates estimated from the population dynamics of each line, including residual deviation from the underlying model (gamma). b. Tolerance curves: lighter grey indicate higher growth rate, the thick bold line corresponds to the growth rate that allows overcoming the biweekly dilution (r = 0.542), and the thin bold line to r = 0.c. Estimated survival curves.

Extended Data Fig. 2 Population dynamics in constant salinities.

a. Time series of population sizes for all lines of ancestral genotype A in the 3 constant salinities (light blue: 0.8M, blue: 2.4M, dark blue: 3.2M – 5 replicates each) are plotted (shaded lines),together with the mean population size (solid line) ± standard deviation (dashed lines).Population sizes> 1,000 are plotted on the log scale and mean and standard deviation were estimated on the log scale, excluding extinct lines. Note that at 3.2M, the population size slowly decreases over time because its exponential growth slightly undercompensates the biweekly dilution. b. Distribution of population size in genotype A during the stationary phase (except at 3.2M where no stationary phase is reached), for all constant environmental treatments. Note the reduced variances and skewnesses as compared with the stochastic environments (Fig. 2). c. and d.: Growth rate (r) and carrying capacity (K) of all ancestral genetic backgrounds, estimated in the 3 constant salinities, with their standard errors. Salinity has a significant effect on K (p = 5.2.10-15, likelihood ratio test) but this was neglected in the population dynamic analysis in fluctuating environment because the biweekly 15% dilution implied that exponential growth was prevalent in our experiment (Extended Data Fig. 1).

Extended Data Fig. 3 Raw measurements.

a, b and c. Cytometer (Guava® EasyCyte) plots. Dunaliella shape and size do not allow distinction from debris but a gate can designed based on chlorophyll (red and yellow) autofluorescence. Dead cells were detectable after transfers from low to high salinity. They show an immediate decrease in red fluorescence, and are not taken into account when measuring population size. d and e. Regression between spectrometer measures (685nm fluorescence – 10722 points and 680nm optical density – 7332 points, see Supplementary Table 1 for details). Fluorescent values below 400 and optical density values below 0.01 (vertical lines) were discarded from the analysis and colours represent the day of each measure (from 0 (black) to 133 (light blue).

Extended Data Fig. 4 Tolerances curves of 3 clones from BC genotype under autocorrelation 0.

We measured exponential growth (starting 3 independent replicates from 5000 cells and measuring population size after 3 days) in 75 previous x current cross salinities ranging from 0.1 to 4.7M. Raw growth rate ranging from -1.5 (black) to 1 (light grey) were measured (dots) and bivariate tolerance curves (colour and curves) were fitted using equation 12 (see Method section). Clones displayed similar plasticity as found in the total population (Extended Data Fig. 1, genotype BC).

Extended Data Fig. 5 Salinity distribution in the stochastic treatments.

a. Salinity time series of 10 of the 39 independent lines for each of the four autocorrelation treatments (same colours as Fig. 2). b. Distribution of salinities in the 39 independent salinity series experienced by the six ancestral genetic background in the four autocorrelation treatments. Note that truncation of salinities that could not be reached with our dilution level did not much affect the final distribution of salinities in our treatments, which was similar to the Gaussian expectation in the absence of truncation (line).

Extended Data Fig. 6 Comparison between population sizes fitted in the state–space model including an autocorrelated normal (x) or a gamma (y) distribution of the intrinsic growth rate r.

The same colours and symbols as Fig. 2 were used to represent autocorrelation and genotypes of the 22497 data points.

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Supplementary text, references and Tables 1 and 2.

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Rescan, M., Grulois, D., Ortega-Aboud, E. et al. Phenotypic memory drives population growth and extinction risk in a noisy environment. Nat Ecol Evol 4, 193–201 (2020). https://doi.org/10.1038/s41559-019-1089-6

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