Fluctuations in the environment are an inescapable condition for all organisms. While many of these fluctuations are entirely unpredictable, some are predictable to a degree, including those that occur on diurnal and seasonal time scales. The predictability of cyclic fluctuations is reflected in the fact that many species exhibit plastic physiological and behavioral strategies that enable them to survive the unfavorable season and exploit the favorable one (Denlinger, 2002; Kostál, 2006); such plastic responses represent the classical form of seasonal adaptation (Tauber et al., 1986). However, seasonally varying selection can – in principle – maintain fitness-related genetic variation if some genotypes have high fitness in one season but not another (Gillespie, 1973; Haldane and Jayakar, 1963). Thus, in organisms that undergo multiple generations per year, a distinct form of seasonal adaptation occurs when the frequency of alternate genotypes changes in response to seasonal fluctuations in the environment.

Seasonal adaptation can be seen as a form of local adaptation – local adaptation in time to temporally varying selection pressures rather than in space. Such adaptation in time has often been considered to be uncommon and, when present, unlikely to result in long-term balancing selection (Ewing, 1979; Hedrick, 1976). Classic quantitative genetic theory suggests that an optimal, plastic genotype will eventually dominate a population that is exposed to periodically changing environments (Bürger and Gimelfarb, 1999; Korol et al., 1996; Lande, 2008; Scheiner, 1993). This is particularly so when certain environmental cues are reliable indicators of changes in selection pressure (Levins, 1974; Via and Lande, 1985). Predictions from traditional population genetic models suggest that periodically changing environments will lead to the rapid loss of seasonally favored ecotypes as slight changes in selection pressure from one year to another eventually push allele frequencies at causal alleles to fixation (Hedrick, 1976).

Recent theoretical models have called these classical predictions into question. For instance, a population genetic model by Wittmann et al., 2017 has demonstrated that seasonally varying selection can indeed maintain fitness-related genetic variation at many loci throughout the genome provided that dominance shifts from season to season in such a way that, on average, the seasonally favored allele remains slightly dominant (Curtsinger et al., 1994). This model, along with others that highlight the importance of population cycles (Bertram and Masel, 2019), as well as overlapping generations and age structure (Bertram and Masel, 2019; Ellner, 1996; Ellner and Hairston, 1994; Ellner and Sasaki, 1996), suggest that seasonal adaptation and adaptive tracking (Kain et al., 2015) could be an important feature of organisms such as Drosophila that have multiple generations per year (Behrman et al., 2015, but see Botero et al., 2015). More generally, it is possible that adaptive tracking of environmental fluctuations on rapid time scales might be more common than generally acknowledged and has been hidden from us due to the difficulty of detecting such adaptive tracking reliably (Lynch and Ho, 2020) and from the lack of finely resolved temporal data (Buffalo and Coop, 2020).

Despite the lack of theoretical consensus on whether and how seasonal adaptation operates, there is substantial empirical evidence for seasonal adaptation in many organisms including Drosophila. Seasonal adaptation was first observed in D. pseudoobscura by Dobzhansky and colleagues (e.g., Dobzhansky, 1943) by tracking frequencies of inversion polymorphisms over seasons. Later studies confirmed and extended these early findings to other species including D. melanogaster (Kapun et al., 2016; Stalker, 1980; Stalker, 1976) and D. subobscura (Rodríguez-Trelles et al., 2013).

In D. melanogaster, multiple additional lines of evidence from phenotypic and genetic analyses demonstrate the presence of seasonal adaptation. When reared in a common laboratory environment, flies collected in winter or spring (close descendants of flies that successfully overwintered) show higher stress tolerance (Behrman et al., 2015), greater propensity to enter reproductive dormancy (Schmidt and Conde, 2006), increased innate immune function (Behrman et al., 2018), and modulated cuticular hydrocarbon profiles (Rajpurohit et al., 2017) as compared to flies collected in the fall (the descendants of those flies who prospered during the summer). Rapid adaptation over seasonal time scales in these and related phenotypes has also been observed in laboratory (Schmidt and Conde, 2006) and field-based mesocosm experiments (Erickson et al., 2020; Grainger et al., 2021; Rajpurohit et al., 2018; Rajpurohit et al., 2017; Rudman et al., 2019; Rudman et al., 2021). Genome-wide analysis indicated that a large number of common polymorphisms change in frequency over seasonal time scales in one mid-latitude orchard (Bergland et al., 2014), and allele frequency change among seasons has been observed using candidate gene approaches (Cogni et al., 2014). In several cases, these adaptively oscillating polymorphisms have been linked to seasonally varying phenotypes (Behrman et al., 2018; Erickson et al., 2020; Paaby et al., 2014).

Despite ample evidence of seasonal adaptation in D. melanogaster, many aspects of this system remain unexplored. First, we do not know whether seasonal adaptation is a general feature of D. melanogaster populations across its range. Previous work (Schmidt and Conde, 2006; Bergland et al., 2014; Cogni et al., 2014; Behrman et al., 2015; Behrman et al., 2018) detected seasonal allele frequency fluctuations in a single locality over the span of 1–3 years. These results need to be confirmed at other locations. Second, as many ecological and environmental variables covary with season, it is unclear which specific factors drive the observed changes in phenotype and genotype in populations sampled over seasonal time. One straightforward and statistically powerful way to start answering these questions is to assess the extent of parallel shifts in allele frequencies over seasonal time in many populations across the D. melanogaster range. Should such parallel shifts be detected we can then infer that seasonal adaptation is widespread and at least partly driven by a common set of variants. We can also attempt to associate the magnitude and direction of allelic shifts with local environmental conditions (e.g., temperature) that may vary among populations sampled at equivalent points in seasonal time. Broad sampling will also allow us to determine the magnitude of the shared seasonal fluctuations and give us a glimpse into the genetic architecture of seasonal adaptation: for instance, whether or not the seasonally responsive variants are found genome-wide or clustered into linked blocks or even supergenes.

Here we carry out such a study by using allele frequency data from 20 paired seasonal samples from 15 localities in North America and Europe. First, we demonstrate that seasonal adaptation is a general phenomenon that occurs in multiple populations of D. melanogaster on two continents by providing evidence that at least some of the same polymorphisms cycle between seasons in populations sampled across vast geographic distances. Seasonal alleles tend to show clinal variation, with the alleles that increase in frequency through the summer generally being more frequent in lower latitude (more ‘summer-like’) locations. We also show that seasonal signal is enriched near inversion breakpoints or within inverted regions on chromosome 2L, 3L, and 3R, although these inverted regions do not account for all of the signal that we observe. Furthermore, we show that allele frequency change between seasons is most predictable when taking into account the local temperature prior to sample collections both in the spring and in the fall, which hints at the complex and nonlinear nature of adaptation with adaptive tracking occurring at sub-seasonal timescales. Taken together, our work demonstrates that seasonal adaptation is a general and predictable feature of D. melanogaster populations and has pervasive effects on patterns of allele frequencies genome-wide. More generally, we establish that metazoan populations can exhibit adaptive tracking of environmental conditions on extremely short time scales, most likely shorter than a single growing season encompassing ~10 generations.

In this study, we have focused on the identification of genomic signatures of seasonal adaptation in D. melanogaster. Our approach relies on the detection of parallel changes in allele frequency from spring to fall across 20 populations. We use coarse definitions of spring and fall, with spring as the time of year close to when D. melanogaster first becomes locally abundant and fall as the time close to the end of the growing season but before populations become too scarce to sample (Behrman et al., 2015; Ives, 1970). We sampled populations across a wide geographic range, such that the length of the growing season as well as other environmental conditions varied substantially. Despite this environmental heterogeneity and the coarseness of the definitions of spring and fall, we detect clear signals of parallel seasonal allele frequency shifts across these populations (Figure 2A,B). Specifically, we demonstrate that seasonal adaptation is a general phenomenon of temperate D. melanogaster populations, and not restricted to a single orchard (Linvilla Orchard, Media, Pennsylvania: Behrman et al., 2015; Bergland et al., 2014; Cogni et al., 2014; Schmidt and Conde, 2006), biogeographic region (East Coast of North America: Behrman et al., 2018), or even a continent.

By detecting consistent seasonal changes in allele frequencies among multiple populations in North America and Europe, we can argue that seasonal adaptation across this whole range is at least partly driven by a consistent set of loci. Although parallel responses to selection in polygenic models of adaptation may be the exception rather than the norm due to redundancy (Barghi et al., 2019), we lack power to detect alleles that cycle in a small subset of populations or are private to a single population. We therefore focus only on the polymorphisms that segregate in all populations – and which consequently have a higher population frequency – and only look at the signal of parallel cycling. Thus, our conclusions are limited to the shared genetic basis of seasonal adaptation as revealed by the cycling of ~750,000 common SNPs which represent roughly half of all detected SNPs.

The detected seasonal SNPs can either be causal or, vastly more likely, linked to nearby causal genetic variants, be they SNPs, indels, structural variants, or transposable element insertions. We detected stronger seasonal signal near or inside inversions on chromosomes 2L, 3L, and 3R, suggesting that inversions are important drivers of seasonal adaptation (Dobzhansky, 1948; Dobzhansky, 1943; Kapun et al., 2016; Kapun and Flatt, 2019; Rodríguez-Trelles et al., 2013). Furthermore, the power to detect parallel seasonal evolution should be stronger for regions with reduced recombination, plausibly increasing the power of our approach to detect parallel adaptation linked to structural variants. Note that, by focusing on the subset of seasonal sites that also show clinal patterns of geographic differentiation, we were able to detect seasonal signal outside inversions as well. The cosmopolitan inversions span a substantial portion of the genome, and there is also likely to be substantial genetic diversity within these cytologically defined elements further complicating the inference. Thus, our study suggests that the complicated architecture of seasonal adaptation will require a much broader seasonal sampling of populations to fine-map the causal variants and that such an endeavor is not futile, given that it is possible to detect a set of loci that show consistent seasonal fluctuations with all the limitation of the approach notwithstanding. It is also clearly important in the future to obtain genomic data capable of identifying structural variation, including inversions, that plausibly underlie much of seasonal adaptation in Drosophila.

One key and poorly understood aspect of Drosophila seasonal adaptation is how the populations survive annual population collapse during the winter, manage to overwinter, and then manage to expand and recolonize the available resources during the summer (Ives, 1970; Shpak et al., 2010). Also poorly understood is the extent to which these patterns of collapse-overwintering-repopulation involve migration and refugia (Coyne and Milstead, 1987; Ives, 1970). However, it is clear that seasonal adaptation in D. melanogaster cannot involve wholesale population replacements by migration because the spatial structure of the D. melanogaster populations is robust year after year as evidenced by strong spatial population structure (Figure 1C) and persistent clinal patterns (Machado et al., 2016; van Heerwaarden and Hoffmann, 2007). While one can conceive of migration from local refugia playing a role, any such scenario still requires natural selection to operate in order to reset populations back to the ‘spring’ state every year. The full metapopulation dynamics and the ways that local D. melanogaster survive seasonal crashes and expansions remains to be understood.

Our ability to detect a shared genetic basis of seasonal adaptation across these broadly distributed populations implies that there are generic features in the environment that exert consistent selective pressures despite the environmental heterogeneity across sampling times and locations. It is possible that such generic selective pressures relate to temperature, humidity, population density, resource availability, or other biotic and abiotic factors (Bogaerts-Márquez et al., 2021). The seasonal and latitudinal parallelism in allele frequency change that we observe (Figure 2E) suggests that the environmental factors that drive these patterns must both shift between seasons locally and vary spatially from south to north.

While the exact nature of the environmental variables that drive seasonal selection remain to be determined, here we provide evidence that temperature extremes in the weeks prior to sampling are a strong proximate or even ultimate selective agent (Figure 3). Specifically, we show that the seasonal behavior of any one population could be statistically associated with temperature extremes in the 2 weeks prior to sampling both in the spring and in the fall. This model accounts for why several populations showed reversal of direction of movement of seasonal SNPs relative to the bulk of the populations; these populations had unusually warm springs and unusually cool falls prior to sampling. We thus hypothesize that, at the time of sampling, some of our spring samples had already experienced sufficiently strong selection in the generically summer way and that the fall samples had experienced selective pressures in the generically winter way. This would result in seasonal alleles in the spring already showing fall-like patterns and the ones in the fall showing spring-like patterns.

These findings also raise an intriguing possibility that natural selection may not push populations in the same consistent direction across the entire growing season but rather that natural selection and adaptation may be more heterogeneous through time (Grant, 2002; Nosil et al., 2020; Siepielski et al., 2009). In the future, more dense temporal sampling might help map the action of natural selection to more specific environmental factors and to quantify how fast the selective pressures change in strength and even direction across a single growing season. Our results thus illustrate that evolution and ecology do operate on similar timescales in this system (Hendry, 2020; Schoener, 2011; Thompson, 1998; Yoshida et al., 2003) and that evolutionary adaptation cannot be generally ignored a priori in demographic or ecological investigations even on extremely short timescales (Rudman et al., 2018).

While the number of detected seasonal loci is likely to be a vast overestimate of the number of causal seasonal variants, particularly if there is selection on large inversions, the strength of selection implied by the magnitude of fluctuations at seasonal sites is likely to be an underestimate. Partially linked loci should show a smaller magnitude of fluctuation than the true causal sites. Thus the 4–8% seasonal frequency shifts at the top 1% of the seasonal sites and the corresponding effective selection coefficients of ~10–30% per season (~1–3% per generation) imply even larger magnitudes of selection at the causal sites. Furthermore, given that we can only detect patterns of seasonal cycling that are at least partially consistent across all or most sampled populations, we are likely to underestimate the impact of linked seasonal selection due to selection that is occurring in a population-specific manner. While given our dataset it is impossible to know the true extent of polygenicity, our results suggest that seasonal adaptation acts on at least several but potentially a large number of dispersed loci affecting linked variation genome-wide. This seasonal selection force could rival in strength genetic draft due to selective sweeps and should be much stronger over seasonal timescales than random genetic drift in populations as large as D. melanogaster (Karasov et al., 2010).

Our observation of signals of seasonal adaptation at common SNPs demonstrates that polymorphism is maintained across the range of D. melanogaster despite strong fluctuating selection. The most straightforward explanation of these results is that these polymorphisms are subject to balancing selection (Bertram and Masel, 2019; Charlesworth, 2015; Gulisija and Kim, 2015, Levene, 1953; Wittmann et al., 2017). Pervasive balancing selection is consistent with the recent realization that simple models of mutation-selection balance are inconsistent with the extent of quantitative genetic variation in a variety of fitness-related traits (Charlesworth, 2015). A full understanding of the genomic consequences of balancing selection caused by strong fluctuating selection will require the identification of individual causal loci. Doing so will require substantial additional sampling across seasons as well as from closely and broadly distributed locales throughout the whole geographic range of D. melanogaster in combination with the genetic mapping of seasonally fluctuating phenotypes (Behrman et al., 2018; Behrman et al., 2015; Erickson et al., 2020; Rajpurohit et al., 2018; Rajpurohit et al., 2017; Schmidt and Conde, 2006). Finally, such efforts can be strongly augmented by manipulative field experiments and functional work to investigate the molecular effects of putatively causal genetic variants. All of this work will need to be done in the context of other processes that underlie persistence of populations in the face of environmental heterogeneity such as phenotypic plasticity and bet hedging (Ayrinhac et al., 2004; Bergland et al., 2008; Kain et al., 2015; MacMillan et al., 2015; Overgaard et al., 2011; Via and Lande, 1985). Overall, it is becoming clear that all of the key ecological and evolutionary processes, including demographic changes, mutation, migration, phenotypic plasticity, and rapid evolution by natural selection, can and do occur on very short and overlapping timescales and thus must be investigated as part of one complex evolving and interacting system.

All raw sequence data have been deposited to the NCBI short read archive (SRA; BioProject Accession #PRJNA308584; accession numbers for each sample can be found in Supplementary file 1A). Code to conduct these analyses, primary results files, and code to reproduce the figures are available at https://github.com/machadoheather/dmel_seasonal_RTEC (copy archived at https://archive.softwareheritage.org/swh:1:rev:c4e0bc07a642c35470cd2c21b4f38f7ed0daa28d). VCF files with the raw allele frequencies per population and a R-data file of allele frequencies and effective sample sizes (Nc; compatible with scripts) are available on DataDryad (https://doi.org/10.5061/dryad.4r7b826).

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Author details

1. Heather E Machado

   1. Department of Biology, Stanford University, Stanford, United States
   2. Wellcome Sanger Institute, Hinxton, United Kingdom

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing

   Contributed equally with

   Alan O Bergland

   For correspondence

   heather.machado@sanger.ac.uk

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-1523-3937

2. Alan O Bergland

   1. Department of Biology, Stanford University, Stanford, United States
   2. Department of Biology, University of Virginia, Charlottesville, United States

   Contribution

   Conceptualization, Resources, Formal analysis, Funding acquisition, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing

   Contributed equally with

   Heather E Machado

   For correspondence

   aob2x@virginia.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7145-7575

3. Ryan Taylor

   Department of Biology, Stanford University, Stanford, United States

   Contribution

   Data curation, Formal analysis

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9003-6378

4. Susanne Tilk

   Department of Biology, Stanford University, Stanford, United States

   Contribution

   Investigation

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9156-9360

5. Emily Behrman

   Department of Biology, University of Pennsylvania, Philadelphia, United States

   Contribution

   Resources

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2472-9635

6. Kelly Dyer

   Department of Genetics, University of Georgia, Athens, United States

   Contribution

   Resources

   Competing interests

   No competing interests declared

7. Daniel K Fabian

   1. Institute of Population Genetics, Vetmeduni Vienna, Vienna, Austria
   2. Centre for Pathogen Evolution, Department of Zoology, University of Cambridge, Cambridge, United Kingdom

   Contribution

   Resources

   Competing interests

   No competing interests declared

8. Thomas Flatt

   1. Institute of Population Genetics, Vetmeduni Vienna, Vienna, Austria
   2. Department of Biology, University of Fribourg, Fribourg, Switzerland

   Contribution

   Resources

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5990-1503

9. Josefa González

   Institute of Evolutionary Biology, CSIC- Universitat Pompeu Fabra, Barcelona, Spain

   Contribution

   Resources

   Competing interests

   No competing interests declared

10. Talia L Karasov

    Department of Biology, University of Utah, Salt Lake City, United States

    Contribution

    Resources

    Competing interests

    No competing interests declared

11. Bernard Kim

    Department of Biology, Stanford University, Stanford, United States

    Contribution

    Formal analysis

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-5025-1292

12. Iryna Kozeretska

    1. Taras Shevchenko National University of Kyiv, Kyiv, Ukraine
    2. National Antarctic Scientific Centre of Ukraine, Taras Shevchenko Blvd., Kyiv, Ukraine

    Contribution

    Resources

    Competing interests

    No competing interests declared

13. Brian P Lazzaro

    Department of Entomology, Cornell University, Ithaca, United States

    Contribution

    Resources

    Competing interests

    No competing interests declared

14. Thomas JS Merritt

    Department of Chemistry & Biochemistry, Laurentian University, Sudbury, Canada

    Contribution

    Resources

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-4795-7534

15. John E Pool

    Laboratory of Genetics, University of Wisconsin-Madison, Madison, United States

    Contribution

    Resources

    Competing interests

    No competing interests declared

16. Katherine O'Brien

    Department of Biology, University of Pennsylvania, Philadelphia, United States

    Contribution

    Resources

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-4660-0338

17. Subhash Rajpurohit

    Department of Biology, University of Pennsylvania, Philadelphia, United States

    Contribution

    Resources

    Competing interests

    No competing interests declared

18. Paula R Roy

    Department of Ecology and Evolutionary Biology, University of Kansas, Lawrence, United States

    Contribution

    Resources

    Competing interests

    No competing interests declared

19. Stephen W Schaeffer

    Department of Biology, The Pennsylvania State University, University Park, United States

    Contribution

    Resources

    Competing interests

    No competing interests declared

20. Svitlana Serga

    1. Taras Shevchenko National University of Kyiv, Kyiv, Ukraine
    2. National Antarctic Scientific Centre of Ukraine, Taras Shevchenko Blvd., Kyiv, Ukraine

    Contribution

    Resources

    Competing interests

    No competing interests declared

21. Paul Schmidt

    Department of Biology, University of Pennsylvania, Philadelphia, United States

    Contribution

    Conceptualization, Formal analysis, Investigation, Resources, Funding acquisition, Writing - original draft, Project administration, Writing - review and editing

    Contributed equally with

    Dmitri A Petrov

    For correspondence

    schmidtp@upenn.edu

    Competing interests

    No competing interests declared

22. Dmitri A Petrov

    Department of Biology, Stanford University, Stanford, United States

    Contribution

    Conceptualization, Supervision, Formal analysis, Investigation, Funding acquisition, Writing - original draft, Project administration, Writing - review and editing

    Contributed equally with

    Paul Schmidt

    For correspondence

    dpetrov@stanford.edu

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-3664-9130

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