Continuously fluctuating selection reveals fine granularity of adaptation

ABSTRACT Temporally fluctuating environmental conditions are a ubiquitous feature of natural habitats. Yet, how finely natural populations adaptively track fluctuating selection pressures

via shifts in standing genetic variation is unknown1,2. Here we generated genome-wide allele frequency data every 1–2 generations from a genetically diverse population of _Drosophila

melanogaster_ in extensively replicated field mesocosms from late June to mid-December (a period of approximately 12 total generations). Adaptation throughout the fundamental ecological

phases of population expansion, peak density and collapse was underpinned by extremely rapid, parallel changes in genomic variation across replicates. Yet, the dominant direction of

selection fluctuated repeatedly, even within each of these ecological phases. Comparing patterns of change in allele frequency to an independent dataset procured from the same experimental

system demonstrated that the targets of selection are predictable across years. In concert, our results reveal a fitness relevance of standing variation that is likely to be masked by

inference approaches based on static population sampling or insufficiently resolved time-series data. We propose that such fine-scaled, temporally fluctuating selection may be an important

force contributing to the maintenance of functional genetic variation in natural populations and an important stochastic force impacting genome-wide patterns of diversity at linked neutral

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SIMILAR CONTENT BEING VIEWED BY OTHERS MULTIPLE MECHANISMS DRIVE GENOMIC ADAPTATION TO EXTREME O2 LEVELS IN _DROSOPHILA MELANOGASTER_ Article Open access 12 February 2021 QUANTIFYING THE

EFFECT OF GENETIC, ENVIRONMENTAL AND INDIVIDUAL DEMOGRAPHIC STOCHASTIC VARIABILITY FOR POPULATION DYNAMICS IN _PLANTAGO LANCEOLATA_ Article Open access 30 November 2021 LONG-TERM STUDIES

PROVIDE UNIQUE INSIGHTS INTO EVOLUTION Article 19 March 2025 DATA AVAILABILITY Founder line sequences are available at the NCBI with the accession ID PRJNA722305. Sequences generated via

pooled sequencing for this experiment are available at the NCBI with the accession ID PRJNA1031645. Allele frequency data are available at the following Dryad repository:

https://doi.org/10.5061/dryad.xd2547dpv. CODE AVAILABILITY Code associated with all analyses conducted in this paper is publicly available at:

https://github.com/MarkCBitter/DrosophilaMesocosm21_FluctuatingSelection. CHANGE HISTORY * _ 12 MAY 2025 In the version of this article initially published, minus signs were missing from the

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(2011). Article  CAS  PubMed  PubMed Central  Google Scholar  Download references ACKNOWLEDGEMENTS We are grateful to members of the Petrov and Schmidt laboratories for discussion during

experimental design, laboratory work and data analysis; M. Karageorgi and K. Solari for support with sample sequencing; J. Rhodes and S. Greenblum for feedback on statistical analysis and

computing; M. Exposito-Alonso, H. Fraser, O. Ghosh, S. Greenblum, J. Hemker, M. Karageorgi, B. Kim, J. Rhodes, K. Solari and S. Walton for insightful comments on drafts of the manuscript;

and R. Dhillon for the illustration presented in Fig. 1. Funding was provided by the National Science Foundation (NSF PRFB 2109407 to M.C.B.) and the US National Institutes of Health (NIH

R35GM118165 to D.A.P. and NIH R01GM137430 to P.S.). AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * Department of Biology, Stanford University, Stanford, CA, USA M. C. Bitter, A. Huynh, Egor

Lappo & D. A. Petrov * Department of Biology, University of Pennsylvania, Philadelphia, PA, USA S. Berardi, H. Oken & P. Schmidt * Chan Zuckerberg Biohub, San Francisco, CA, USA D.

A. Petrov Authors * M. C. Bitter View author publications You can also search for this author inPubMed Google Scholar * S. Berardi View author publications You can also search for this

author inPubMed Google Scholar * H. Oken View author publications You can also search for this author inPubMed Google Scholar * A. Huynh View author publications You can also search for this

author inPubMed Google Scholar * Egor Lappo View author publications You can also search for this author inPubMed Google Scholar * P. Schmidt View author publications You can also search

for this author inPubMed Google Scholar * D. A. Petrov View author publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS The experiment was conceived by

M.C.B., D.A.P. and P.S. The mesocosm study was conducted by M.C.B., S.B., H.O. and P.S. Population census size estimates were conducted by A.H. DNA extraction, library preparation and

sequencing were conducted by M.C.B. Bioinformatic analysis and statistical analysis of allele frequency data were carried out by M.C.B. and E.L. M.C.B., D.A.P. and P.S. wrote the manuscript,

with feedback from all authors. CORRESPONDING AUTHORS Correspondence to M. C. Bitter, P. Schmidt or D. A. Petrov. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no competing

interests. PEER REVIEW PEER REVIEW INFORMATION _Nature_ thanks the anonymous reviewers for their contribution to the peer review of this work. Peer reviewer reports are available. ADDITIONAL

INFORMATION PUBLISHER’S NOTE Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. EXTENDED DATA FIGURES AND TABLES EXTENDED

DATA FIG. 1 COMPARISON OF BASELINE POPULATION TO WILD _D. MELANOGASTER_ POPULATIONS. The degree of genome-wide differentiation between our baseline population (_n_ = 4) and the Pennsylvania

population from which it was derived (_n_ = 14) (Baseline: PA Pop) is comparable, and statistically indistinguishable, from that observed between the Pennsylvania population and other wild

populations throughout Eastern North America (_n_ = 6) (PA Pop: East NA Pops; one-tailed Student’s t-test, P value = 0.99). The baseline population is significantly less differentiated from

the Pennsylvania population (Baseline: PA Pop) than from populations in California (_n_ = 17) (Baseline: CA Pop; one-tailed Student’s t-test P value < 0.001) and European populations

(Baseline: EU Pops; one-tailed Student’s t-test, P value < 0.001), following the same trend as differentiation between the wild Pennsylvania population and other wild populations in

eastern North America (PA Pop: East NA Pops), California (PA Pop: CA Pop), and Europe (PA Pop: Eu Pops). Natural population sampling and data provided by Machado et al.30. EXTENDED DATA FIG.

2 GENOME-WIDE DIFFERENTIATION THROUGHOUT EXPERIMENT. Distribution of genome-wide Fst computed pairwise between all technical replicate samples (same DNA extraction, different library

preparation and sequencing lane; _n_ = 10), biological replicates (same time point and replicate cage, different pool of 100 flies; _n_ = 17), and samples collected at each time point (_n_ =

 12) and the baseline population samples (‘Base’, _n_ = 4), for each of the twelve time points. Center lines of each box plot display the median Fst value, vertical lines denote the 25th and

75th percentiles, and dots indicate outlier values. The distributions of genome-wide FST between time point samples and the baseline population was significantly greater than that observed

between technical replicates (*) and biological replicates (+) (two-tailed Student’s t-test; P-value < 0.01). FST between time point samples and the baseline population increased

throughout the course of the experiment (generalized linear model; P = 0.039). EXTENDED DATA FIG. 3 SNP-PAIR LINKAGE WITHIN AND BETWEEN UNLINKED LOCI. SNP-pair linkage is calculated as the

correlation (R-squared) between founder genotypes. (a-c) Smoothed LOESS regressions (grey shaded regions indicate 95% confidence intervals) of linkage for (a) pairs of GLM significant SNPs

within and across all 264 unlinked loci, (b) pairs of GLM significant SNPs within the same unlinked locus, and (c) between pairs of SNPs located within separate, unlinked loci (_n_ = 

1,000,000 total SNP-pair comparisons). (d) Empirical cumulative density functions of SNP-pair linkage across the top 50 SNPs of each of the identified 264 unlinked loci, segregated by

chromosomal arm. Linkage was calculated between top SNPs of separate loci located within 1 Mb of each other. Observed mean linkage (R2 values) between top SNPs for each chromosomal arm are

as follows: 2 L = 0.034; 2 R = 0.023; 3 L = 0.028; 3 R = 0.028; X = 0.027; Genome-wide = 0.029. EXTENDED DATA FIG. 4 CORRELATIONS OF ALLELE FREQUENCY MOVEMENT WITHIN AND ACROSS PUTATIVELY

UNLINKED LOCI. Correlations of allele frequency shifts (averaged across replicates) for pairs of GLM significant SNPs (starting frequencies and effect sizes in the same decile; _n_ = 

1,000,0000 total comparisons). Each point on the plot shows the median correlation between focal SNPs and SNPs within the same unlinked locus (positive control SNPs, green points), SNPs in a

different identified locus on the same chromosomal arm (Comparison SNPs, black points; x-axis represents distance from focal SNP; filtered to only consider pairs of SNPs that fell below our

threshold for merging putatively linked clusters (r2 < 0.03)), and SNPs in loci located on different chromosomes (negative control SNPs, red points). Note error bars representing 95%

bootstrap confidence intervals obtained by resampling combinations of SNPs are too small to be visible on plots. Observed correlations are segregated by chromosomal arm (rows) and the number

of top SNPs (based on GLM P-value) evaluated when computing correlations. EXTENDED DATA FIG. 5 MINIMUM NUMBER OF FLIPS IN THE DOMINANT DIRECTION OF SELECTION INFERRED FROM CHANGES IN MEDIAN

BEHAVIOR OF SNPS IDENTIFIED ON FIVE AND TEN TIME POINT INTERVALS. Distribution of the number of flips in the dominant direction of selection observed throughout the experiment for sets of

target SNPs identified using GLM leave-one-out cross validation across all ten time point intervals (t1->11 and t2->12) and five time point intervals (t1->6, t2->7…t6->12),

and tested within each consecutive time point interval (e.g. t1->2, t2->3…t11->12). A flip was inferred when the median allele frequency shift at target SNPs switched signs (i.e.

fluctuated from positive to negative, or vice versa) and maintained a magnitude that was significantly greater than matched controls. Thus, the number of flips computed represents the

minimum number of changes in the direction of selection required to explain the observed patterns of median allele frequency behavior throughout the experiment. SNPs identified on five time

point intervals flipped in sign significantly more than those identified on ten time point intervals (Wilcoxon rank sum test, W = 706, p = 0.02). EXTENDED DATA FIG. 6 MEASUREMENT VARIANCE OF

TARGET AND MATCHED CONTROL SNPS IDENTIFIED ACROSS ALL FIVE TIME POINT INTERVALS AND THROUGHOUT SUMMER TO FALL TRANSITION (T1->11). Distribution of variances of allele frequency estimates

between biological replicate samples (obtained at time points 3 and 11) at sets of target and matched control SNPs underlying data presented in Figs. 2f and 3. The variances are not

significantly different between sets of target or matched control SNPs (Wilcoxon rank sum test, W = 591, P = 0.88). EXTENDED DATA FIG. 7 ALLELE FREQUENCY COVARIANCES AND CONVERGENCE

CORRELATIONS THROUGHOUT THE EXPERIMENT. (a) Mean covariances (across replicates) between each pair of single time point intervals (interval 3 corresponds to t3->4, interval 4 corresponds

to t4->5, etc.). Covariances between adjacent intervals are removed due to shared technical noise. Covariance estimates with confidence intervals non-overlapping the zero line indicate

that the signal of linked selection dominates over drift, while changes in the sign of covariances across intervals implicates fluctuations in the direction of selection. (b)

Replicate-specific covariances between the first single time point interval (interval 1; t1->2) and all remaining single time point intervals, plotted separately for each replicate cage.

(c) Mean convergence correlations between all pairs of replicates within each single time point interval. Positive convergence correlations suggest parallel allele frequency movement across

replicates for a given interval. EXTENDED DATA FIG. 8 MESOCOSM DAILY TEMPERATURE. Daily minimum and maximum temperature from a weather station located within the mesocosm study system.

SUPPLEMENTARY INFORMATION SUPPLEMENTARY INFORMATION Supplementary Figs 1–25. REPORTING SUMMARY PEER REVIEW FILE SUPPLEMENTARY FILE 1 SNPs identified as significant (FDR < 0.05; allele

frequency shift > 3%) via generalized linear model regression of allele frequency data across all cages from time points 1->11. SNP coordinates provided in _Drosophila melanogaster_

reference genome v. 5.39 SUPPLEMENTARY FILE 2 Coordinates of unlinked loci underpinning patterns of selection inferred via generalized linear regression of allele frequency data across all

cages from time points 1->11. SNP coordinates provided in _Drosophila melanogaster_ reference genome v. 5.39. SUPPLEMENTARY FILE 3 SNPs identified as significant (FDR < 0.05; allele

frequency shift > 3%) via generalized linear model regression of allele frequency data across all cages, separately for each five time point interval comparison. SNP coordinates provided

in _Drosophila melanogaster_ reference genome v. 5.39. RIGHTS AND PERMISSIONS Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a

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agreement and applicable law. Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Bitter, M.C., Berardi, S., Oken, H. _et al._ Continuously fluctuating selection reveals fine

granularity of adaptation. _Nature_ 634, 389–396 (2024). https://doi.org/10.1038/s41586-024-07834-x Download citation * Received: 23 January 2024 * Accepted: 16 July 2024 * Published: 14

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