1. Chromosomes and Gene Expression
  2. Plant Biology
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Natural variation in autumn expression is the major adaptive determinant distinguishing Arabidopsis FLC haplotypes

  1. Jo Hepworth
  2. Rea L Antoniou-Kourounioti
  3. Kristina Berggren
  4. Catja Selga
  5. Eleri H Tudor
  6. Bryony Yates
  7. Deborah Cox
  8. Barley Rose Collier Harris
  9. Judith A Irwin
  10. Martin Howard
  11. Torbjörn Säll
  12. Svante Holm  Is a corresponding author
  13. Caroline Dean  Is a corresponding author
  1. Cell and Developmental Biology, John Innes Centre, United Kingdom
  2. Computational and Systems Biology, John Innes Centre, United Kingdom
  3. Department of Natural Sciences, Mid Sweden University, Sweden
  4. Department of Biology, Lund University, Sweden
  5. Crop Genetics, John Innes Centre, United Kingdom
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Cite this article as: eLife 2020;9:e57671 doi: 10.7554/eLife.57671

Abstract

In Arabidopsis thaliana, winter is registered during vernalization through the temperature-dependent repression and epigenetic silencing of floral repressor FLOWERING LOCUS C (FLC). Natural Arabidopsis accessions show considerable variation in vernalization. However, which aspect of the FLC repression mechanism is most important for adaptation to different environments is unclear. By analysing FLC dynamics in natural variants and mutants throughout winter in three field sites, we find that autumnal FLC expression, rather than epigenetic silencing, is the major variable conferred by the distinct Arabidopsis FLChaplotypes. This variation influences flowering responses of Arabidopsis accessions resulting in an interplay between promotion and delay of flowering in different climates to balance survival and, through a post-vernalization effect, reproductive output. These data reveal how expression variation through non-coding cis variation at FLC has enabled Arabidopsis accessions to adapt to different climatic conditions and year-on-year fluctuations.

Introduction

Developmental transitions in plants are aligned with specific seasons to maximise reproductive success. A major seasonal cue used to time the transition to flowering is temperature (Hancock et al., 2011; Andrés and Coupland, 2012; Manzano-Piedras et al., 2014). Many plants overwinter in a vegetative form and flower in spring. This alignment of the floral transition with spring is achieved through a process called vernalization, in which flowering is accelerated by previous exposure to prolonged winter cold. In Arabidopsis thaliana and its relatives, FLOWERING LOCUS C (FLC), a MADS-box transcription factor, delays the transition to flowering in warm temperatures until its expression is downregulated by exposure to cold (Michaels and Amasino, 1999; Sheldon et al., 1999). On return to warm, spring-like conditions, FLC remains epigenetically silenced, permitting expression of FT and floral meristem identity genes that trigger the floral transition (Helliwell et al., 2006; Searle et al., 2006).

The vernalization mechanism has been genetically dissected using controlled laboratory conditions (Song et al., 2013; Bloomer and Dean, 2017; Whittaker and Dean, 2017). FLC expression is upregulated by the coiled-coil protein FRIGIDA (FRI), which establishes an active transcription state at FLC through interactions with chromatin modifiers including the H3K36 methyltransferase SET DOMAIN GROUP8 (SDG8) (Choi et al., 2011; Hyun et al., 2017; Li et al., 2018). This upregulation is antagonised by the autonomous pathway, a group of interacting RNA-processing factors, (eg. FCA and FY) and chromatin modifying factors (eg. FLD and FVE). These promote a low expression state at FLC through interactions with the long non-coding antisense transcript group, COOLAIR (Ausín et al., 2004; Liu et al., 2007; Wu et al., 2016; Wu et al., 2020). A high FLC transcription state is associated with plants that overwinter before flowering. Cold transcriptionally downregulates FLC, and the locus is maintained in an epigenetically silenced state by a Polycomb Repressive Complex 2 (PRC2) mechanism (Gendall et al., 2001; Bastow et al., 2004; Sung and Amasino, 2004; Greb et al., 2007; Helliwell et al., 2011; Hepworth et al., 2018). This PRC2 mechanism requires the temperature-integrating accessory protein, VERNALIZATION INSENSITIVE3 (VIN3) (Sung and Amasino, 2004; Bond et al., 2009a; Antoniou-Kourounioti et al., 2018). Cold-induction of VIN3 expression leads to formation of a PHD-PRC2 complex that also contains the PHD protein VERNALIZATION5 (VRN5) and the specific Su(z)12 component VRN2 (Sung et al., 2006a; Greb et al., 2007; De Lucia et al., 2008). This complex is nucleated at FLC by the B3-binding protein VP1/ABI3-LIKE 1 (VAL1) (Qüesta et al., 2016; Yuan et al., 2016). The PHD-PRC2 methylates histone H3 lysine 27 residues in a ~ 3 nucleosome domain at FLC to epigenetically ‘switch’ each allele off in a stochastic manner that over the population of cells produces a quantitative memory of the length of cold (Angel et al., 2011; Yang et al., 2014; Angel et al., 2015; Yang et al., 2017). When plants return to warm conditions, the PHD-PRC2 spreads over the locus to maintain long-term epigenetic memory, and this requires the action of LIKE HETEROCHROMATIN1 (LHP1) (Mylne et al., 2006; Sung et al., 2006b; Berry et al., 2017; Yang et al., 2017).

FLC expression and memory is influenced by natural non-coding cis polymorphism at the locus (Lempe et al., 2005; Shindo et al., 2006; Coustham et al., 2012; Li et al., 2015; Qüesta et al., 2020). Different Arabidopsis accessions need different lengths of cold to epigenetically silence FLC, and this requirement is determined by non-coding SNPs at FLC (Shindo et al., 2006; Coustham et al., 2012). Indeed, within the Arabideae tribe, cis regulation of FLC orthologues contributes to perennial-vs.-annual flowering differences (Kiefer et al., 2017). Within the worldwide Arabidopsis thaliana population, non-coding variation defines a small number of FLC haplotypes, which confer quantitatively different flowering time responses to different lengths of vernalization in constant cold in the laboratory (Li et al., 2014). These haplotypes appear to have been maintained in the A. thaliana population due to their contributions to life history diversity (Sánchez-Bermejo et al., 2012; Li et al., 2014; Méndez-Vigo et al., 2016), and they show different geographic distributions, suggesting that they are adapted to different climates (Méndez-Vigo et al., 2011; Li et al., 2014).

Recent work has shown the importance of analysing flowering in natural field conditions (Figure 1A), ‘in natura’ (Kudoh, 2016). Such studies have highlighted how the influence of genes of major effect, including FLC, changes depending on the field environment (Wilczek et al., 2009; Fournier-Level et al., 2011; Duncan et al., 2015; Ågren et al., 2017; Taylor et al., 2019). In particular, the investigation of RNA levels in natura has identified genetic mechanisms in the field that were previously unknown from laboratory observations (Antoniou-Kourounioti et al., 2018; Song et al., 2018; Nagano et al., 2019). For example, field work in Sweden and the UK with the vernalization reference genotype Col FRISF2 and mutants in VIN3 (vin3-4) has shown that initial VIN3-independent FLC downregulation requires cold temperatures, whereas VIN3-mediated epigenetic silencing requires the absence of daily temperatures above 15°C (Hepworth et al., 2018). Autumn conditions greatly influence when epigenetic silencing initiates, sometimes leading to temporal separation of the two phases, which in laboratory constant-condition experiments and some natural environments occur concurrently (Hepworth et al., 2018).

Figure 1 with 2 supplements see all
Field experimental setup.

(A) Map showing locations of field sites (dots) and the origins of five of the accessions (flowers) used in this study. These accessions, with the addition of Col-0, represent the five major and one intermediate (Löv-1) FLC haplotypes identified by Li et al., 2014. The lab genotype Col FRI was also used in this study as a vernalization-requiring reference. (B–D) Temperature profiles experienced by plants at the three field sites, North Sweden – Ramsta (B), South Sweden – Ullstorp (C) and Norwich, UK (D) (Source data 1, as from Hepworth et al., 2018 and Antoniou-Kourounioti et al., 2018). Flowers above temperature profile indicate the median time of bolting of each of the natural accessions and of Col FRI (legend at bottom left corner). Black dots below temperature profile indicate the timepoints when plant material was collected for expression analysis. Black arrows below temperature profiles indicate time of transfer to greenhouse with long-day, warm conditions to assess degree of vernalization based on bolting time.

Here, we exploit field studies in three climatically distinct locations, over two years, to define the roles of FLC cis polymorphism and known genetic regulators of FLC in VIN3-dependent and -independent phases of the vernalization response under natural conditions (Figure 2A). We hypothesised that accessions bearing different FLC haplotypes would show different flowering time responses to winter environments, and that this would be due, at least in part, to variation in the downregulation and silencing of FLC itself, arising from the cis polymorphism at FLC. Moreover, we expected variation in the FLC epigenetic silencing mechanism would play a particularly important role, as this mechanism has previously been shown to vary in natural accessions (Coustham et al., 2012; Wollenberg and Amasino, 2012; Duncan et al., 2015).

Figure 2 with 3 supplements see all
Downregulation in 2014–5 in Norwich, North Sweden (two plantings) and South Sweden for all NILs and accessions.

(A) Experimental data for Col FRI in Norwich 2014–5, showing the temperature profile (top), FLC (middle) and VIN3 (bottom) expression. Different shades indicate the separation of the VIN3-dependent (grey) and -independent (black) phases of FLC silencing (Hepworth et al., 2018) and equivalent times in VIN3 and temperature profiles (as in Figure 1D). Expression data were normalised to the control sample for 2014–5 (see Materials and methods). N = 6 except where samples were lost to death or degradation (see Materials and methods and Source data 2). Error bars show standard error of the mean (s.e.m). The initial measurement in the field (Starting levels), the rate of downregulation before induction of VIN3 expression (VIN3-independent, estimated from the slope of the fitted line) and the rate of downregulation after VIN3 induction (VIN3-dependent) are the three features that were analysed and compared for each genotype and treatment in the next panels, based on the data of Figure 2—figure supplement 1. A new feature is also shown, the FLC-post-vern, that is measured based on the flowering time from plants transferred to glasshouses with inductive conditions and how that relates to the FLC levels at the time of the transfer. (B–J) FLC downregulation analysed as level at first time point (Starting levels), and rate of downregulation (Shutdown – combining early and later timepoints for FLC data – see Materials and methods) for North (B–E) and South Sweden (F–G), or rate of downregulation before (VIN3-independent, dark bars) and after (VIN3-dependent, translucent bars) VIN3 induction for Norwich (H–J). Features of genotypes that are significantly different to the reference line Col FRI are indicated by * (for Starting levels, ANOVA with Dunnett’s post-hoc test, for Shutdown rates, Satterthwaite’s t-tests on REML Linear mixed model). p-values for all comparisons are given in Supplementary file 1. Rates of downregulation are given in units of ‘a.u. per day’, where the arbitrary units (a.u.) correspond to the normalised concentration of FLC mRNA, measured by qPCR. VIN3 induction started at ~58 days in Norwich (Figure 2—figure supplements 23). Expression data were normalised to the control sample for 2014–5 (see Materials and methods). N = 6 except where samples were lost to death or degradation (see Materials and methods and Source data 2). Error bars show s.e.

Our results demonstrate how in the field the major FLC haplotypes, differing only through non-coding variation, have different starting FLC levels and rates of response to autumn cold, but show more similar epigenetic silencing rates in winter. This results in effective repression of FLC by midwinter and post-winter flowering in most years, across haplotypes and climates. Our experiments also reveal effects of the FLC haplotypes on prevention of precocious flowering in warm years, and on reproductive success after winter via effects on branching and silique number. By studying gene expression across years and climates, we have been able to dissect how non-coding cis variation at FLC modulates flowering time and fitness in response to different natural fluctuating environments.

Results

Field experiments

Li et al., 2014 identified 20 FLC haplotypes across a worldwide panel of Arabidopsis accessions defined predominantly through non-coding SNPs. These haplotypes conferred different flowering time responses to vernalization at constant 5°C, with the accessions carrying them clustered broadly into ‘Rapid Vernalizing’ (RV) and ‘Slow Vernalizing’ (SV) types. To investigate their function in field conditions, we selected accessions to represent each of the five most populous haplotypes, representing more than 60% of tested accessions, as well as a further accession, Löv-1, for which there is evidence of local adaptation to the climate in the region of our North Sweden field site (Duncan et al., 2015; Qüesta et al., 2020). To compare these haplotypes in a common genetic background, we exploited extant and developed new near-isogenic lines (NILs) in which the FLC haplotype from each accession had been repeatedly backcrossed to Col FRISF2 (‘Col FRI’; Duncan et al., 2015; Li et al., 2015). We confirmed that these selected accessions and NILs show variation in their rate of response to time in constant cold in the laboratory and also found that they varied in branch response to vernalization, an effect mediated by FLC (Figure 1—figure supplements 12, Huang et al., 2013; Lazaro et al., 2018).

These genotypes were tested across two years and three field sites, with the exception of three NIL lines, which were generated during the experiments (Figures 1A and 2B–J). The three field sites in Norwich, UK, in Ullstorp, Sweden (‘South Sweden’) and in Ramsta, Sweden (‘North Sweden’) were chosen to represent climates with different winters. Norwich has a temperate oceanic climate and a mild winter, Ullstorp has a warm-summer continental climate with a winter in which temperatures are often below freezing, and Ramsta is subarctic, usually with snow cover during winter (hence the 0°C flatline in temperatures in Figure 1B; Beck et al., 2018). The Swedish sites are also close to the source sites of several of the tested accessions; Vår2-6 and Ull2-5 in Skåne, near or at Ullstorp; Löv-1 near Ramsta (Figure 1A). In the second year of experimentation, we also included the vin3-1 mutant in the Col FRI background. The experiments ran from August/September 2014 until spring 2015 and again from August/September 2016 to the spring of 2017, with sowing times adjusted to each site (earlier in the colder climates). In the first year, two plantings were performed in North Sweden, two weeks apart. The temperatures that the plants experienced are shown in Figure 1B–D. We measured the levels of spliced and unspliced FLC, and mRNA levels of VIN3, to follow the progress of vernalization in the field. The transition to flowering (bolting) was assessed both in the field and by transfers to warm inductive conditions.

Natural variation in different phases of FLC silencing in the field

Across all the genotypes we tested and all seven field experiments, as expected, FLC expression reduced over weeks in response to autumn and winter temperatures, and VIN3 was upregulated. Previously, we had noted that in 2014–5 in Norwich, substantial VIN3 upregulation did not occur until ~58 days after sowing, although temperature conditions were suitable for VIN3-independent FLC downregulation for most of this time (Hepworth et al., 2018). In the following field season, this pattern occurred again, with VIN3 upregulation delayed until 48 days after sowing (Antoniou-Kourounioti et al., 2018). For the Col FRI reference, we had found that we could fit two separate exponential decay curves to FLC; the first for the initial, slow, VIN3-independent phase and the second for the faster, VIN3-dependent phase of the downregulation (Figure 2A; Hepworth et al., 2018). Thus three features of the FLC profile contribute to the level of FLC at any time: firstly the ‘Starting level’ of FLC before vernalization, secondly the rate of downregulation in the initial VIN3-independent phase, and thirdly the rate of VIN3-dependent downregulation (Figure 2A). This pattern was consistent across the accessions and NILs, allowing us to investigate the effect of natural variation on different aspects of FLC regulation. The time of upregulation of VIN3, and thus the time of switching from the VIN3-independent to the VIN3-dependent shutdown, also affects FLC levels, but this was very similar between genotypes at the same site.

In Norwich 2014–5, the RV FLC accession Edi-0 (haplotype group RV1) behaved very similarly to Col FRI (RV2). In comparison, the SV accessions (Löv-1, Ull2-5, Bro1-6 and Var2-6) show higher levels of FLC throughout the winter (Figure 2, Figure 2—figure supplement 1). Ull2-5 started with similar levels of FLC as Col FRI, but slower rates of downregulation in both the VIN3-independent and VIN3-dependent phases generated higher levels of FLC throughout autumn and winter. For both Var2-6 and its NIL, an apparently slower VIN3-independent rate of downregulation contributed to a higher FLC level, although the difference in rate is not significant. The VIN3-dependent phase was also slower in Var2-6 and Löv-1 (Figure 2).

In Sweden 2014–5, at both sites the VIN3-independent and -dependent phases occurred concurrently (Figure 2, Figure 2—figure supplement 1; Hepworth et al., 2018). There was little variation observed in the overall rate of FLC downregulation between genotypes, though the maximal VIN3 induction was more variable (Figure 2—figure supplement 2), with the fluctuations depending strongly on complex responses to the temperature profile, as expected from analysis of Col FRISF2 (Antoniou-Kourounioti et al., 2018). Instead, most of the natural variation in FLC levels throughout winter in Sweden was generated by differences in the early expression level. In North Sweden again both RV accessions had similar Starting levels whereas the SV Swedish accessions were higher to different degrees. However, in South Sweden Löv-1 started with similar levels to Col FRI.

In the first year of field experiments we observed that a high proportion of the FLC shutdown occurred early in autumn, and furthermore, a lot of the variation in total FLC between lines was during that early phase. To maximise this period, and to extend the time that the plants spent in warmer autumn conditions, so mimicing potential climate change effects, we sowed the second field experiment earlier. For the 2016–7 experiment, we sowed plants in North Sweden and Norwich two weeks earlier and South Sweden three weeks earlier than for the 2014–5 season (Figures 1A and 3, Figure 3—figure supplements 12). The differences in daylight length due to this earlier sowing must be considered in direct comparisons between years, but the timing of both sowing dates was within the natural germination range. In North Sweden, this early sowing was followed by an usually warm autumn: September temperatures at the nearest weather station, Lungö, averaged 11.04°C between 1990–2016, whereas they were 12.60°C in 2016, the fourth highest year during that period (Swedish Meteorological and Hydrological Institute, 2020, www.smhi.se/data/meteorologi/ladda-ner-meteorologiska-observationer). This produced a delay in VIN3 induction similar to that seen in both years in Norwich (Figure 1A; Antoniou-Kourounioti et al., 2018). However, the first stage of shutdown was still more rapid in North Sweden than in Norwich, despite higher average temperatures in North Sweden (Figure 1A), so that both VIN3-independent and VIN3-dependent phases had similar rates, resulting in the appearance of a single decline. As in the previous experiment in Sweden, these rates of downregulation were generally similar between different genotypes, with higher FLC levels in SV accessions and the Var NIL due to higher starting levels (Figure 3, Figure 3—figure supplement 1).

Figure 3 with 3 supplements see all
Downregulation in 2016 in Norwich and North Sweden for NILs and accessions show similar patterns of response to the first year.

(A–D) FLC downregulation in Col FRI, Var2-6 and the Var NIL, as measured for Norwich and North Sweden in the winters of 2014–5 and 2016–7. (E–J) FLC downregulation as Starting level and VIN3-independent and dependent rates. Features of genotypes that are significantly different to the reference line Col FRI are indicated by * (for Starting levels, ANOVA with Dunnett’s post-hoc test, for Shutdown rates, Satterthwaite’s t-tests on REML Linear mixed model). p-values for all comparisons are given in Supplementary file 1. VIN3 induction started at: Norwich 2016, ~48 days, North Sweden 2016,~46 days, see (Figure 3—figure supplement 1) Expression data were normalised to the corresponding control sample (2016–7, see Materials and methods). N = 6 except where samples were lost to death or degradation (see Materials and methods and Source data 3). Rates of downregulation are given in units of ‘a.u. per day’, where the arbitrary units (a.u.) correspond to the normalised concentration of FLC mRNA. Error bars of bar plots show s.e., of line graphs show s.e.m.

In Norwich, the patterns seemed to be largely repeated, though our statistical power was lower in the 2016–7 experiment due to fewer timepoints. Bro1-6, Löv-1, Var 2–6 and their NILs appeared to have higher starting FLC levels, though of these only Bro1-6 was significant in our analysis. Ull2-5, Var2-6, Edi-0 and Löv NIL2 showed slower VIN3-independent downregulation and Ull2-5 and Var2-6 also again showed slower VIN3-dependent downregulation (Figure 3). Other than a slower VIN3-independent rate in Norwich in Edi-0, the RV haplotypes behaved similarly to each other in 2016–7.

The slower rate of the epigenetic silencing phase in Var2-6 was consistent across years in Norwich (Figures 2 and 3). Unlike the other accessions, this change in the VIN3-dependent phase in Var2-6 was consistently mirrored in lower levels of VIN3 (Figure 2—figure supplement 2, Figure 3—figure supplement 1). The circadian clock is an important regulator of VIN3 (Antoniou-Kourounioti et al., 2018; Hepworth et al., 2018), so we tested whether trans-variation within the clock contributes to the difference in VIN3 upregulation in Var2-6. When sampled over 48 hr in the Norwich field experiment (Figure 3—figure supplement 3), VIN3 expression in Var2-6 is much lower compared to our previous results from Col FRI (Figure 3—figure supplement 3B,D; Antoniou-Kourounioti et al., 2018), as is the expression of circadian clock component CIRCADIAN CLOCK ASSOCIATED1, the protein of which binds to the VIN3 promoter (CCA1; Figure 3—figure supplement 3; Nagel et al., 2015). Therefore, variation in the circadian clock may underlie some of the difference in FLC regulation in Var2-6.

Chromatin modifiers control FLC regulation in the field

To understand the contribution of other trans factors to temperature registration at the different phases of FLC shutdown in the field, we exploited our contained site in Norwich to investigate mutants in genes known to affect FLC levels before cold or in response to cold (Figure 4).

Figure 4 with 2 supplements see all
Starting levels and rates of downregulation of FLC in mutants and transgenics in field conditions in Norwich, UK.

(A–F) FLC downregulation analysed as level at first time point (Starting levels, A, D), rate of downregulation before induction of VIN3 expression (VIN3-independent, dark bars, B, E) and rate of downregulation after VIN3 induction (VIN3-dependent, translucent bars, C, F). Features of genotypes that are significantly different to the reference line Col FRI are indicated by * (for Starting levels, ANOVA with Dunnett’s post-hoc test, for Shutdown rates, Satterthwaite’s t-tests on REML Linear mixed model). p-values for all comparisons are given in Supplementary file 1. Rates of downregulation are given in units of ‘a.u. per day’, where the arbitrary units (a.u.) correspond to the normalised concentration of FLC mRNA. VIN3 induction started at: Norwich 2014, ~58 days, see (Figure 4—figure supplement 1); Norwich 2016, ~48 days, see (Figure 4—figure supplement 2). All mutants are in the Col FRI background unless otherwise stated. Expression data were normalised to the corresponding control sample (for 2014–5 or 2016–7, see Materials and methods). N = 6 except where samples were lost to death or degradation (see Materials and methods and Source data 2 and 3). Error bars show s.e.

As previously reported (Hepworth et al., 2018), vin3 mutants (vin3-4, vin3-1) did not show the increase in downregulation rate that marks the later epigenetic phase of silencing (Figure 4C,F). This effect was also seen for other mutants defective in epigenetic memory; vrn1-4; vrn2-1 (defective in the Su(z)12 homologue component of PRC2); vrn5-8 (defective in the VIN3-related protein VRN5); and lhp1-3, in accordance with this phase being required for epigenetic silencing. Loss of the H3K36 methyltransferase sdg8 generated the same effect, correlated with its delayed upregulation of VIN3 (Kim et al., 2010; Jean Finnegan et al., 2011).

In the initial, VIN3-independent silencing phase, both Col fri (Col-0) and sdg8 FRI were hyper-responsive, consistent with a role for SDG8 with FRI in the establishment of the high FLC expression state (Hyun et al., 2017; Li et al., 2018). Mutants in the autonomous pathway, fca-9, fld-4 and fve-3, which upregulate FLC expression in the absence of FRI, had no significant effect on VIN3-independent silencing, but behaved mostly like Col FRI, although fve-3 reduced the rate of downregulation non-significantly in both years. The mutant with the most reduced VIN3-independent response was the B3-binding transcription factor VAL1, required for PRC2 action at FLC (Figure 4E; Qüesta et al., 2016; Yuan et al., 2016), followed closely by the vrn mutants, vrn1-4 and vrn5-8, and a non-significant but consistent trend for vrn2-1. Conversely, the lhp1-3 and vin3 mutants do not show clear impairment in the early phase of downregulation. Overall, it seems that chromatin modifiers are required for both phases of downregulation in the field.

Autumnal expression of FLC is the major variable in vernalization response

To identify which of the three features of FLC regulation is the major variable that defines the different haplotypes in different climates, we estimated the coefficient of variation for the rates of shutdown and starting levels for all the natural accessions, NILs, and vin3-1 where available (from Figure 2, 3, Figure 2—figure supplement 1, Figure 3—figure supplement 1, Supplementary file 2). In Sweden, the starting levels are significantly more variable than the shutdown rates (p-value=2.8⋅10-8 for the first year, Figure 5A), but in Norwich, the VIN3-independent shutdown rate is more variable than the starting levels and VIN3-dependent rate (p-value=2.1⋅10-5 for 2014, p-value=5.0⋅10-4 for 2016, Figure 5B). Combining Norwich and Sweden data, the early shutdown rate is again most variable (p-value=1.9⋅10-10 for 2014, p-value=2.0⋅10-5 for 2016, Figure 5C). On the other hand, there was no significant difference in the variability of the starting levels (Figure 5D) between the different field sites and years, and similarly for the shutdown rates (Figure 5E-G). What we describe as the starting level was measured after some days in the field, so it is not equivalent to a non-vernalized control. Some FLC shutdown, most likely VIN3-independent, will have occurred at that time. Therefore, it seems that the combination of these two determinants (starting levels and rate of VIN3-independent shutdown), which together we call ‘autumnal expression’, provides most of the variation in FLC levels between these accessions and NILs.

Mechanistic sources of natural variation in FLC levels across sites and years.

(A) The coefficient of variation for the rates of shutdown and for the starting levels in all Sweden experiments in the first year. (B) Similarly in Norwich 2014 (top) and 2016 (bottom) but separately for the VIN3-independent (V-indep) and VIN3-dependent (V-dep) shutdown rates. (C) Comparison of the variability of the starting levels and the shutdown rates, separating V-dep and V-indep where appropriate, combining data from all sites in 2014 (top) and 2016 (bottom). ‘Shutdown’ refers to the combined V-dep/V-indep shutdown rate that was fitted in Sweden 2014, and so is not present in the 2016 results. (D) The coefficients of variation of the starting levels for each site/year. (E) The coefficients of variation of the single shutdown rates for the different plantings and sites in Sweden in 2014. (F–G) Similarly, for Sweden 2016 and Norwich in both years, separating the V-indep rates (F) and V-dep (G). Data from Source data 2 and 3.

Vernalization is saturated before midwinter in the field

Having characterised how the haplotypes vary in their FLC dynamic response to field conditions, we investigated whether this variation had an effect on the floral transition in the field.

In the 2014–5 season, all accessions and NILs bolted relatively synchronously in spring in Norwich and Sweden, although there were small but significant differences in both cases. In Norwich, the flowering time difference between the NILs and Col FRI was small, and in Sweden not significant, suggesting that most of the variation seen between the accessions did not derive from the FLC haplotype (Figure 6—figure supplements 1 and 2). There was also very little difference in branch production in the NILs and accessions in Norwich (Figure 6—figure supplement 1), which was surprisingly low, despite the known role of FLC in suppressing branching (Huang et al., 2013; Lazaro et al., 2018). Nevertheless, we confirmed that this effect does occur in the accessions and NILs in controlled condition experiments, in which longer vernalization times resulted in increased numbers of branches (Figure 1—figure supplement 1).

The observed flowering times suggested that the vernalization requirement is normally saturated during the winter in the field. To test this hypothesis, in 2016–7 we transferred plants from field conditions to heated, long-day conditions at different stages, and scored the length of time until floral buds were visible at the shoot apex (bolting). There was wide variation and substantial delay to bolting within the genotypes for plants transferred to the warm greenhouses early in autumn, at all sites, indicating that vernalization requirement had not been saturated at this point (Figure 6A,C,E). Indeed, in both Swedish sites many plants did not flower within the experimental time (136 days after transfer for South, and 104 days for the North) when transferred 25 days after sowing. Some genotypes (particularly Löv-1) required more than 46 days in the field before most individuals were competent to flower in warm, long-day conditions (Figure 6—figure supplement 3). There was also clear variation between genotypes in their time to flowering in the earlier transfers. However, as winter progressed, time to bolt after transfer reduced quantitatively and became more uniform for plants within and between each genotype (Figure 6B,D,F), although vernalization saturated at different rates in different genotypes (Figure 6—figure supplements 34). All accessions and NILs bolted broadly synchronously and rapidly after removal on 21st December in Norwich, in North Sweden NILs and accessions were almost synchronous by 24th November, in line with previous findings (Duncan et al., 2015), and in South Sweden FRI-containing lines were broadly synchronous by the last transfer on 17th December (Figure 6, Figure 6—figure supplements 34). Therefore, in current climates almost all Arabidopsis plants have probably saturated their requirement for vernalization before midwinter, ensuring that flowering time is not delayed the following spring.

Figure 6 with 7 supplements see all
Vernalization requirement for FLC downregulation is saturated in natural winters.

(A–F) Bolting time for accessions and NILs after transfer to floral-induction conditions from ‘natural’ winter 2016–7, in (A) Norwich 21/10/16 (B) Norwich 21/12/16 (C) South Sweden 01/10/2016 (D) South Sweden 17/12/16 (E) North Sweden 06/09/2016 (F) North Sweden 24/11/2016. Plants that did not flower by 14/02/17 (C, D) or 23/12/16 (E, F) are shown as DNF and dead plants are indicated. Plots show the histogram of numbers of plants as the width of violin plots. A line connects the measurements to indicate the range. Flowering time of genotypes that are significantly different to the reference line Col FRI are indicated by * (ANOVA with Dunnett’s post-hoc test). p-values for all comparisons are given in Supplementary file 3. (G–H) North Sweden 2016 transfers for accessions and NILs, (G) mean time to bolting after transfer to floral-inductive conditions plotted against mean FLC expression per genotype at transfer, Norwich 2016–7, linear regression R2 = 0.68, p<0.001. (H) Mean time to bolting after transfer to floral-inductive conditions plotted against mean FLC expression per genotype at transfer, North Sweden 2016–7. Genotypes that did not bolt within 205 days not shown, linear regression R2 = 0.85, p<0.001. N = 12 plants except where plants died or (E, H) did not bolt within 205 days (Source data 5). Error bars for G and H show s.e.m.

Relationship of FLC expression and timing of flowering

We tested whether the observed effects on flowering time were linked to the varying levels of FLC in transferred plants. Across genotypes, in Norwich and North Sweden 2016–7, the time to the floral transition after transfer from the field correlated closely with FLC expression at the time of transfer, as expected (Norwich, R2 = 0.68, p<0.001, North Sweden, R2 = 0.85, p<0.001, linear regression Figure 6G,H). Accessions, NILs and mutants with high starting levels and slower downregulation rates generally bolted later and took longer to saturate their vernalization requirement, indicating that the variation in FLC dynamics in autumn has a phenotypic effect.

However, each accession differed in the relationship between FLC levels at transfer and subsequent bolting time (Figure 6—figure supplements 56). For example, for Löv-1 and the Löv NIL1, the time to bolt for a given level of FLC at transfer is longer than for Col FRI, but for Edi-0, it is shorter (Figure 6—figure supplements 56). This analysis allowed us to extract a further feature of FLC regulation (Figure 2A). We named this feature the FLC-post-vern value (m in Table 1, days-to-bolting-per-FLC-unit), representing the slope of the regression between FLC spliced RNA levels at the time of transfer (x-axis) to the subsequent floral transition time for these transferred plants (y-axis, as for Figure 6G and H). This means that, for a given level of FLC at the time of transfer, a genotype with a higher m ‘post-vern’ value flowers later than one with a lower m value.

Table 1
Linear regression relationship between bolting time and FLC mRNA expression, as shown in Figure 6—figure supplements 56, where days to bolting = m[FLC mRNA] + c, and m is the ‘FLC-post-vern’ value and c is the y-intercept fitted constant relating to non-FLC-mediated bolting delay (dependent on the transfer dates and conditions of each experiment).

NA – estimate only based on two data points, so no standard error is calculable. n.d. – no data. No value given for Col-0 as initial FLC value is too low for useful estimation.

GenotypeNorwich
m (days-to-bolting-per-FLC-unit)
Norwich
Std. error
Norwich
p-value
North Sweden MNorth Sweden
Std. error
North Sweden
p-value
Average
m ‘post-vern’
Col FRI36.55.50.00739.017.60.26937.8
vin3-1 FRI51.03.90.04967.327.60.24759.1
Bro NIL36.10.30.006n.d.n.d.n.d.36.1
Edi NIL33.52.70.05139.81.10.01736.7
Löv NIL140.75.00.079n.d.n.d.n.d.40.7
Löv NIL227.40.80.01923.80.50.01425.6
Ull NIL24.81.00.02542.19.30.13833.4
Var NIL25.70.90.02222.31.60.04624.0
Bro1-627.00.30.00737.32.10.03632.1
Edi-016.43.20.12448.39.20.11932.4
Löv-1113190.110n.d.n.d.n.d.113
Ull2-531.41.20.02416.98.50.29724.2
Var2-663.16.90.07035.0NANA49.0

We calculated this value individually for each genotype from both the Norwich and the North Sweden transfers (Figure 6—figure supplements 56), using the equation given in the legend for Table 1. There was substantial variation between the different glasshouses in the estimates for the accessions (Table 1), suggesting that there is a large genotype-by-environment effect on this parameter due to non-FLC effects. Moreover, for Col-0 and Löv-1, estimations of m were unreliable (p>0.1) due to flowering occurring rapidly even without vernalization (as FLC values are too low to cause measurable differences in flowering: Col-0) or not at all (as in North Sweden FLC values were too high to allow flowering and its timing to be measured within this experiment: Löv-1). Practically, therefore, although any genotype with functional FLC should have a theoretical post-vern value, it can only be measured for high-FLC genotypes under conditions in which they will nevertheless flower.

The m post-vern value also varied between the NILs and the common Col FRI parent within each site, suggesting that this relationship is also influenced by cis variation at FLC. Within the lines in the common Col FRI background, the estimates correlated more closely across the two glasshouse environments, with the exception of the Ull NIL. Therefore, within the Col FRI background, we can quantify the effect of FLC levels on bolting time in warm, long-photoperiod environments, and find that the FLC haplotypes have different effects after vernalization, as well as different responses to vernalization. This effect may be linked to reactivation of FLC expression in the warm, as the vin3-1 and Löv-1 NIL1, both of which have reactivation phenotypes (Sung and Amasino, 2004; Duncan et al., 2015; Qüesta et al., 2020), have high ‘post-vern’ values.

High autumnal FLC reduces precocious flowering in a warm autumn

At all sites in the 2014–5 season, and in South Sweden in 2016–7, across all accessions and NILs, flowering in the field occurred after midwinter (Figure 6—figure supplements 12, Figure 7—figure supplement 1), by which time FLC levels were below the level at which they influenced flowering time in the transfers (Figure 2—figure supplement 1, Figure 3—figure supplement 1, Figure 6). Therefore, although we observed natural variation in FLC levels in autumn (Figure 5), at most sites in most years the conditions were such that this did not result in flowering time variation. However, in the warm North Sweden autumn 2016–7 many of the plants with the Col background (NILs, Col FRI, and vin3-1 FRI) transitioned to flowering early, by the 18th November, before winter and snowfall (Figure 7A). This precocious bolting was rare in the SV accessions and was much reduced even in the RV accession Edi-0. Over all the genotypes, the percentage of plants transitioning to flowering before winter negatively correlated with genotype FLC expression on 5th October, one day after the first recorded bolting for plants in the field (p<0.001, Generalised Linear Models (GLMs) for binomial data, Figure 7B). This was before substantial upregulation of VIN3, indicating that this variation in FLC must derive from the combination of the starting levels and VIN3-independent shutdown. Within the Col FRISF2 background the Var and Löv FLC haplotypes, the NILs with the highest FLC levels during autumn (Figure 3—figure supplement 1), substantially reduced precocious bolting (p=0.005, binomial proportions test, Figure 7A). Thus, variation in FLC starting expression and shutdown (collectively called autumnal expression) results in variation in the alignment of flowering with the seasons.

Figure 7 with 2 supplements see all
High FLC reduces precocious bolting in North Sweden in warm years.

(A) Percentage of plants bolting before winter in the North Sweden 2016 experiment by genotype. Plants in the field were less likely to flower precociously before winter (18th November 2016) if they are accessions from more northerly latitudes or, to a lesser degree, if they are FLC introgression lines containing FLC haplotypes from SV accessions. (B) The percentage of plants transitioning to flowering before winter per genotype negatively correlated with FLC expression (normalised to control) on 5th October (R2 = 0.59, p=0.0058). (C) Total number of siliques produced per genotype, showing contribution from plants that bolted before winter and plants that bolted after. Within the Col FRI genetic background there was no overall penalty in average silique number for surviving plants bolting before vs. after winter (92 and 77 per plant respectively, not significant in Mann-Whitney U test). (D) Mean silique production in plants surviving to set seed positively correlated to their mean rosette branch production for Col FRI genetic background genotypes (NILs and vin3-4; R2 = 0.56, p-value=0.002). (E) Rosette branch production of Col FRI genotypes surviving to set seed is strongly negatively correlated with the FLC post-vern value for that genotype as from Table 1 (R2 = 0.86, p-value<0.002). (F) Total number of siliques produced by Col FRI background genotypes plotted against FLC post-vern, linear regression for post-vern effect alone, R2 = 0.35, p-value=0.1. N = 36 plants sown (A–C), n for surviving plants (D–F) varies per genotype, see Source data 6.

Variation in FLC expression correlates with fitness in the field

Across all genotypes, plants that bolted precociously were not observed to set seed before winter. In this year (2016) and at this site (N.Sweden), mortality was high, compared to other years and sites, and plants that bolted precociously were less likely to survive the winter (42% of bolting plants survived, whereas 67% of non-bolting plants survived to spring, p<0.001, binomial proportions test, Figure 7—figure supplement 2A). Therefore, high FLC levels in October correlated with higher survival to seed set in the field (p<0.003, GLM for binomial data, Figure 7—figure supplement 2B).

We therefore investigated whether FLC was linked to total genotype fitness, as measured by silique number, and found a complex pattern (Figure 7C). Despite many of the NILs having a high level of precocious flowering, several NILs produced more seed than the locally-adapted accessions (Figure 7C, Figure 7—figure supplement 2C).

To better understand why some NILs had higher fecundity than others, we investigated whether FLC might have an additional effect on silique production. In the Col FRI background plants, there was no overall penalty in average silique number for individual surviving plants that bolted before winter compared to those that transitioned afterwards (Figure 7C, Figure 7—figure supplement 2D), suggesting that the differences were not due to an FLC effect on flowering time. Saturating vernalization increased seed set in the 2016 Norwich transfer experiments, as plants that were transferred before saturation of vernalization requirement produced lower and more variable amounts of seed (Figure 6—figure supplement 7). However, in the field in North Sweden 2017 silique production occurred only after winter, after saturation of vernalization requirement (Figure 6F). We noted that there was large variation in branching within the Col FRI background plants, and branching is known to be affected by FLC (Figure 1—figure supplements 12; Huang et al., 2013; Lazaro et al., 2018). We asked whether the likelihood of FLC reactivation after winter might affect branching and thereby silique production. To estimate this, we used as proxy for likely FLC reactivation the relationship between FLC and bolting time in the warm, the ‘FLC-post-vern value’ as derived from Figure 6—figure supplements 56 (Table 1). This did not seem to affect survival to seed set or date of bolting of the survivors (Figure 7—figure supplement 2E,F). Instead, within the Col FRI background, the average silique set among survivors correlated with rosette branching (Figure 7D, Figure 7—figure supplement 2G) and branch number was strongly negatively correlated with FLC-post-vern, though not FLC expression (Figure 7E, Figure 7—figure supplement 2H).

As expected, survival was the principle factor explaining total silique production (Figure 7—figure supplement 2I). However, we found that although the post-vern value of FLC did not well explain total genotype fitness on its own (Figure 7F), when combined with survival to seed set both factors explained a large part of the variation in genotype fitness in Col FRI background (linear model adjusted R2 = 0.94, p-value=0.001 for model, post-vern p-value=0.006, percentage survival to silique set p-value=0.002, Figure 7F, Figure 7—figure supplement 2I).

Discussion

Investigation of the dynamics of key floral integrators in the field has recently led to important insights into their molecular response to natural environments (Antoniou-Kourounioti et al., 2018; Song et al., 2018). Here, we integrated experiments on the expression of FLC in field conditions with investigation on how natural genetic variation and induced mutation interact in distinct climates to affect phenotype and fitness. In our previous work, we found that, as expected from laboratory studies, FLC was repressed and VIN3 was induced by winter cold (Antoniou-Kourounioti et al., 2018; Hepworth et al., 2018). However, in 2014–5, winter conditions in Norwich generated a subtly different response to those in Sweden, activating the VIN3-independent transcriptional shutdown before the VIN3-dependent epigenetic pathway (delay in VIN3 onset and slow then fast shutdown of FLC in Norwich, Figure 2—figure supplement 1). In the following season, this pattern was triggered again, not only in Norwich, but also in North Sweden, implying that this is a common occurrence that plants must adjust to across climates, although with different frequencies at different locations (Figure 3—figure supplement 1).

We find that variation in FLC mRNA levels in both accessions and NILs is largely due to differences in both the starting FLC level and the VIN3-independent rate of shutdown (collectively called autumnal expression), which vary widely between accessions but not between field sites or years (Figure 5). This variation in rate of response, as well as absolute levels of FLC, partly explains why FLC levels measured at any one time may not correlate with the final flowering time phenotype in different accessions (Sasaki et al., 2018).

In Norwich, we investigated the mechanisms of the responses. Mutants of the autonomous pathway, which increase FLC expression in fri plants in laboratory conditions (Ausín et al., 2004; Liu et al., 2010; Wu et al., 2016), generally behaved in a similar manner to Col FRI in terms of vernalization response (Figure 4). However, the reduced response of the fve mutants in the VIN3-independent phase, though not significant in each year, may be worth further investigation, as FVE has been implicated in intermittent cold-sensing through histone deacetylation at FLC, independently of vernalization (Kim et al., 2004; Jung et al., 2013).

All three factors that set FLC levels in the field (starting levels, VIN3-independent and VIN3-dependent phases) require chromatin modifiers for their correct function. The VIN3-independent phase involves chromatin-modifier regulation of transcription, with the VIN3-dependent phase using a similar set of modifiers. These chromatin regulators are rarely found in genome-wide-association studies for flowering time, likely because their alteration would have pleiotropic effects. In the natural accessions we find that the VIN3-dependent (epigenetic) phase is indeed the least variable (Figure 5). This may explain why so much variation in vernalization maps to FLC itself (Lempe et al., 2005; Sánchez-Bermejo et al., 2012; Dittmar et al., 2014; Sasaki et al., 2015; Bloomer and Dean, 2017; Sasaki et al., 2018). Nevertheless, we did observe variation in VIN3 expression in Var2-6, which seems to derive from differences in circadian regulation (Figure 3—figure supplement 3), and QTLs over the VIN3 region have previously been found to affect flowering (Dittmar et al., 2014; Méndez-Vigo et al., 2016; Ågren et al., 2017). Variation in VIN3 expression in the other accessions we tested was more complex than the general downregulation in Var2-6. Given the intricate regulation of VIN3 induction (Antoniou-Kourounioti et al., 2018), identifying the source of this difference will require detailed investigation.

The importance of FLC variation in adaptation for natural populations is well established (Méndez-Vigo et al., 2011; Sánchez-Bermejo et al., 2012; Dittmar et al., 2014; Li et al., 2014; Duncan et al., 2015; Ågren et al., 2017; Bloomer and Dean, 2017). However, we found that even in a challengingly warm year, vernalization requirement saturated before midwinter across three climates (Figure 6). This response had been seen previously in North Sweden in the locally-adapted Löv-1 accession as a reaction to the extreme winters (Duncan et al., 2015). However, we find that this is a general response in all our tested accessions. The principle exceptions were the vin3 and vrn mutants, which had notably delayed flowering in both years at the Norwich site. The consequences of incomplete vernalization are severe – in our 2016–7 transfer experiments this led to delayed flowering and reduced fecundity (Figure 6, Figure 6—figure supplement 7), and several field experiments have found a general trend towards earlier flowering promoting fitness in environments with warmer winters (Ågrena et al., 2013; Fournier-Level et al., 2013; Grillo et al., 2013; Dittmar et al., 2014; Ågren et al., 2017; Exposito-Alonso et al., 2018). In 2014–5, the high synchronicity of flowering in all the accessions and NILs after winter and the fact that flowering aligned with spring across our field sites (Figure 6—figure supplement 2), suggests that in most plants vernalization has adapted to reduce the delaying effect of FLC on flowering as early as possible.

Nevertheless, we observed that the Swedish, cold-winter-adapted accessions and FLC haplotypes associated with slow vernalization expressed the highest FLC levels throughout autumn and early winter (Figures 2 and 3, Figure 2—figure supplement 1, Figure 3—figure supplement 1). In North Sweden in 2016–7, we captured behaviour for a warmer growing season that revealed an adaptive role for these high FLC expression levels. There was a relatively warm September that year with a long period with less precipitation (Swedish Meteorological and Hydrological Institute, 2020, www.smhi.se/data/meteorologi/ladda-ner-meteorologiska-observationer), which required us to water the plants to avoid drought stress. This, in combination with the earlier sowing (warmer and longer days) meant that the equivalent period of growing time for the plants (relative to their germination time) was more inductive to growth and flowering. This allowed us to capture a different behaviour. Plants are exposed to both types of the years we recorded, since the timing of both sowing dates was within the natural germination range, although in natural environments seed dormancy has a strong influence on phenology and is highly responsive to temperature and microclimate, and affected by FLC itself (Springthorpe and Penfield, 2015; Kerdaffrec et al., 2016; Marcer et al., 2018). In our experiments, in plants with a haplotype characteristic of a slow-vernalizing accession, including the locally-adapted Swedish accessions, the higher level of FLC expression protected against precocious flowering and its consequent reduced survival (Figure 7, Figure 7—figure supplement 2). These rare, but highly selective occurrences, may play a role in local adaptation, although we do not know the reason behind the high mortality in this year. However, that the effects of these haplotypes are only revealed occasionally is a logical consequence of the fact that the flowering time genes can have strong or weak effects depending on the environment (Wilczek et al., 2009; Sasaki et al., 2015; Burghardt et al., 2016; Fournier-Level et al., 2016; Sasaki et al., 2018; Taylor et al., 2019).

FLC also controls fecundity as well as survival (Ågrena et al., 2013). Li et al., 2014 found that FLC haplotypes from SV accessions produced lower seed weight compared to those from RV accessions in non-saturating vernalization conditions, though the mechanism for this effect is unknown. In the field, silique production is closely linked to branch production (Figure 7; Taylor et al., 2019). In Arabidopsis, saturation of vernalization requirement is known to increase flowering branch production, particularly rosette branch production, and this effect is linked to FLC (Figure 1—figure supplements 12; Huang et al., 2013; de Jong et al., 2019). As expected, FLC levels in autumn did not correlate with branch production in spring (Figure 7—figure supplement 2H), and in Norwich 2014–5, there was little variation in branch production. However, we found that under the conditions in the field in North Sweden, branch production was negatively related to a factor we named ‘FLC post-vern’, which encoded the relationship between FLC levels and subsequent flowering in warm controlled conditions (Figure 7). Variation in this factor probably derives from regulatory differences at FLC post-cold, such as the reactivation phenotype of the Löv-1 FLC allele (Shindo et al., 2006; Coustham et al., 2012; Duncan et al., 2015; Qüesta et al., 2020), allowing FLC repression of flowering to saturate at the shoot apical and axillary meristems at different rates, a phenomenon that occurs in the perennial relative Arabis alpina (Wang et al., 2009; Lazaro et al., 2018). Why the field conditions in Sweden, but not Norwich, revealed this effect is not yet clear.

In summary, our detailed analysis of the different phases of FLC silencing through winters in distinct climates, over multiple years, has shed light on adaptive aspects of the vernalization mechanism. Autumnal FLC expression, made up from FLC starting levels and early phases of FLC silencing, is the major determinant for variation in FLC levels during vernalization (Figure 5). FLC downregulation aligns flowering response to spring and non-coding variation affects this alignment in different climatic conditions and year-on-year fluctuations in natural temperature profiles. In a changing climate, understanding the complex genotype-by- environment interactions that govern timing mechanisms will become ever more important.

Materials and methods

Plant materials

Sources of previously described mutant lines and transgenics are presented in Supplementary file 4. Requests for materials should be addressed to Caroline Dean.

NILs

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All near-isogenic lines were produced by six rounds of backcrossing to the Col FRI parent, selecting for the introgressed FLC in each generation, before one round of selfing and selection of homozygous families.

Experimental conditions

Field experiments

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Field experiments have been described previously (Antoniou-Kourounioti et al., 2018 (2016-7 winter); Hepworth et al., 2018 (2014-5 winter)). Briefly, seeds were stratified at 4°C for three days. For gene expression measurements, for all field sites and sowing dates and timepoints within them, six replicate tray-cells were sown using a block-randomised design within 5.7 cm 28 cell trays (Pöppelman, Lohne, Germany), and where each replicate included material from at least three plants. For flowering time, plants were thinned to a single plant per cell in 3.9 cm 66 cell trays (Pöppelman). Trays were watered when necessary.

In Norwich, trays were placed on benches in an unheated, unlit glasshouse, and bedded in vermiculite. For expression, plants were randomised within six single-replicate sample-sets, which were then randomised using Research Randomiser (Urbaniak and Plous, 2015) in a three complete block design lengthwise along the greenhouse, adjusting to ensure each of the two replicates per block were on different benches. For flowering time, plants were block-randomised per tray and per block.

In Sweden, trays were germinated and grown outside under plastic covers for two weeks at Mid Sweden University, Sundsvall (North Sweden) or Lund University (South Sweden), due to the difficulty of sowing directly into the field and to ensure sufficient germination for analysis, based on previous experiments. Trays were then moved to the experiment sites and dug into the soil. The experiment site in the North was at Ramsta (62° 50.988´N, 18° 11.570´E), and in the South at Ullstorp (56° 06.6721´N, 13° 94.4655´E). Expression sample-sets were randomised in three blocks. Flowering time plants were in a completely randomised design across two (2014–5) or three (2016–7) blocks.

Plants were sown and moved to the field site on: Norwich first year, sown into position on 29th September 2014, second year, sown into position on 15th September 2016: South Sweden first year, sown 24th September 2014, moved 8th October 2014, second year sown on 6th September 2016, moved on 21st September 2016: North Sweden first year, first planting ‘A’ sown 26th August 2014, moved 11th September 2014; second planting ‘B’ sown 8th September 2014, moved 24th September 2014: North Sweden second year, sown on 12th August 2016, and moved 24th August 2016.

For the transferred plants, in Norwich 2016–7, for each transfer six trays (each holding two replicates, total N = 12) were moved from the unheated, unlit, ventilated greenhouse to a greenhouse with supplementary lighting (600W HPS lamps) and heating set to 22°C/18°C, 16 light/8 hr dark, and 70% humidity on 21st October 2016 (22 days after sowing), 3rd November 2016 (35 das), 17th November 2016 (49 das), 30th November 2016 (62 das), 21st December 2016 (83 das) and 26th January 2017 (119 das). For selected time points, plants were covered with ventilated clear plastic bags to collect seed for weighing. In South Sweden, trays were transferred from the field site to heated, lit greenhouses at Lund University, on 1st October (25 das), 22nd October (46 das), 19th November (74 das) and 17th December 2016 (102 das). In North Sweden, for each transfer three trays (each holding four replicates, N = 12) were moved from the field site to a greenhouse set to 16 hr light, 22°C, at Mid Sweden University, Sundsvall (as in Duncan et al., 2015), on 6th September (25 das), 4th October (53 das), 1st November (81 das) and 24th November 2016 (104 das).

For all expression analysis except the 48 hr sampling (Figure 3—figure supplement 3), six replicates per timepoint per genotype were chosen in order to allow sufficient number of samples for statistical analysis while allowing for losses in the field and to allow duplication within randomisation blocks. Where resulting samples are smaller, this is due to experimental or processing loss (e.g., death of plants in the field, degradation due to poor sample quality or processing, see RNA extraction and QPCR). For Figure 3—figure supplement 3 and replicates per timepoint per genotype were chosen due to space constraints. Each expression sample (single replicate) was of at least three plants pooled. For flowering time, 12 plants per genotype per transfer condition were chosen to provide replication across blocks and trays while remaining within size constraints. For field flowering in North Sweden 2016–7, 36 plants per genotype were sown to allow for losses, although in the event these were more substantial than anticipated.

Temperature was recorded at plant level at each site with TinyTag Plus two dataloggers (Gemini Data Loggers (UK) Ltd, Chichester, UK). Bolting was scored when flower buds were visible at the shoot apical meristem. For the North Sweden 2016–7 field experiment, plants were scored for survival and flowering in the field from planting to December 2016, and then from March to May 2017. Plants were harvested and scored for branching and silique production after the end of flowering, in July 2017.

Branching analysis

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; Seeds were sown on soil, stratified after sowing for three days at ~4–5°C, and transferred to a Norwich long-day glasshouse set to 18°C/15°C, 16/8 hr light/dark conditions for 7 days before being returned to vernalization conditions (a 4°C growth chamber under short day, low light conditions; 8/16 hr light/dark) for 12, 8, 4, and 0 weeks. Sowing was staggered so that after vernalization all plants were transferred to glasshouse conditions simultaneously. Plants were scored for their flowering time, total branch number, cauline branch number and rosette branch number. In all cases, plants were randomised into blocks and at least three replicate plants for each accession/cultivar per treatment were scored for their flowering and branching phenotypes. Primary rosette and cauline branch number were scored at senescence.

RNA extraction and QPCR

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RNA extraction and QPCR for field experiments were performed as described in Hepworth et al., 2018 and Antoniou-Kourounioti et al., 2018. Field data were unified across sites and timepoints within the yearly datasets and normalised to a synthetic control sample, as described in Hepworth et al., 2018, to give the ‘normalised concentration’ reported in the results. QPCR results were analysed using LinReg (Ruijter et al., 2009), and normalised to the geometric means of At5g25760 (‘PP2A’) and At1g13320 (‘UBC’) control genes (Czechowski et al., 2005; Yang et al., 2014).

QPCR samples that showed high Cp values (UBC Cp >28 for LinReg analysis) of the control genes, indicating possible degradation, were excluded if test amplicon (FLC, VIN3) results were also anomalous (criteria: absent, or varying from non-flagged samples by an order of magnitude). Any measurements where amplicon Cp values varied by more than 0.6 were also excluded if insufficient sample was available for a repeat.

Primers used are described in Supplementary file 5.

Statistics

The rates of FLC shutdown were estimated using linear regression, as shown in Figure 2A. For Norwich 2014–5 and for all sites in 2016–7, a separate rate was calculated for timepoints before the point of induction of VIN3 (Norwich 2014: ~58 days, Norwich 2016: ~48 days, South Sweden 2016: ~35 days, North Sweden: ~46 days) and for those after. For North and South Sweden 2014–5, when the VIN3 induction was not delayed, all timepoints were combined, excluding measurements after 155 days. These timepoints were during or after the snow, when temperatures started to increase and reactivation was observed in some genotypes.

For the analysis presented in Supplementary file 1 and Figures 24, the Starting level comparisons and the Shutdown rate comparisons were done separately, using the following methods. For the starting levels, ANOVA was performed in the R (R Development Core Team, 2018) statistical language using the lm function, followed by a Dunnett post-hoc test comparing all genotypes against the control Col FRI. For the shutdown rates, the lmer function from the lmerTest package in R was used to perform the same comparison on the genotype-timepoint interaction, controlling for blocks as random factors and after setting the mean of the timepoints to 0 on the x-axis, using the default Satterthwaite’s method for the post-hoc t-tests.

We used the R package cvequality (Version 0.1.3; Marwick and Krishnamoorthy, 2019) with the asymptotic test (Feltz and Miller, 1996) to assess differences between the coefficients of variation of different groups as described in the text and Supplementary file 2. The significance limit was adjusted to control the false discovery rate using the Benjamini-Hochberg procedure with a false discovery rate of 0.05 (Supplementary file 2, Supplementary file 6). This analysis was performed including all the natural accession and NILs combined (Figure 4), but also for all the accessions separately and for all the NILs separately (including Col FRI in both cases, but not Col-0 or mutants). The different analyses do not change our overall conclusions and all three are reported in Supplementary file 2.

Statistical analysis for flowering and branching data (Supplementary file 3, Figure 6, Figure 6—figure supplements 1, 2, 3 and 4 and Figure 7—figure supplement 1) were performed on the plants that flowered during the experiment using the same procedure as for the Starting levels described above.

For multiple regression on field data, R was used to obtain minimal adequate models using linear regression (lm function), except when n > 10 for count data, for which general linear models (GLM, glm function) using Poisson error distributions were used (total numbers of siliques), or for proportion data for which GLMs with binomial errors were used (survival, bolting before winter).

Appendix 1

Appendix 1—key resources table
Reagent type
(species) or resource
DesignationSource or
reference
IdentifiersAdditional
information
Gene (Arabidopsis thaliana)FLOWERING LOCUS C; FLCTAIRAt5g10140
Gene (Arabidopsis thaliana)VERNALIZATION INSENSITIVE3; VIN3TAIRAt5g57380
Gene (Arabidopsis thaliana)PROTEIN PHOSPHATASE 2A SUBUNIT A3; PP2ATAIRAt1g13320
Gene (Arabidopsis thaliana)UBC; PEX4
PEROXIN4
TAIRAt5g25760
Gene (Arabidopsis thaliana)CIRCADIAN CLOCK ASSOCIATED1; CCA1TAIRAt2g46830
Strain, strain background (Arabidopsis thaliana)Col-0Shindo et al., 2005
Doi:10.1104/pp.105.061309
Nottingham Arabidopsis Stock Centre (NASC) ID N22625
Strain, strain background (Arabidopsis thaliana)Bro1-6Long et al., 2013, Doi:10.1038/ng.2678NASC ID
N76726
Strain, strain background (Arabidopsis thaliana)Edi-0Shindo et al., 2005 Doi:10.1104/pp.105.061309NASC ID N22657
Strain, strain background (Arabidopsis thaliana)Löv-1Shindo et al., 2005 Doi:10.1104/pp.105.061309NASC ID N22574
Strain, strain background (Arabidopsis thaliana)Ull2-5Shindo et al., 2005 Doi:10.1104/pp.105.061309NASC ID N22586
Strain, strain background (Arabidopsis thaliana)Var2-6Shindo et al., 2005 Doi:10.1104/pp.105.061309NASC ID N22581
Genetic reagent (A. thaliana)Col FRISF2Lee and Amasino, 1995
Doi:10.1038/ncomms3186
Genetic reagent (A. thaliana)Bro NILThis paperFLC from Bro1-5 backcrossed to Col FRI background six times and brought to homozygosity, as described in Plant materials section of Materials and methods. Requests for materials should be addressed to Caroline Dean.
Genetic reagent (A. thaliana)Edi NILThis paperFLC from Edi-0 backcrossed to Col FRI background six times and brought to homozygosity, as described in Plant materials section of Materials and methods. Requests for materials should be addressed to Caroline Dean.
Genetic reagent (A. thaliana)Löv NIL1Duncan et al., 2015 Doi:10.7554/eLife.06620
Genetic reagent (A. thaliana)Löv NIL2Duncan et al., 2015 Doi:10.7554/eLife.06620
Genetic reagent (A. thaliana)Ull NILThis paper, derived from Strange et al., 2011 doi:10.1371/journal.pone.0019949FLC from Ull2-5 backcrossed to Col FRI background six times and brought to homozygosity, as described in Plant materials section of Materials and methods. Requests for materials should be addressed to Caroline Dean.
Genetic reagent (A. thaliana)Var NILLi et al., 2015 doi:10.1101/gad.258814.115
Genetic reagent (A. thaliana)vin3-1 FRISung and Amasino, 2004 doi:10.1038/nature02195
Genetic reagent (A. thaliana)vin3-4 FRIBond et al., 2009b doi:10.1111/j.1365-313X.2009.03891.x
Genetic reagent (A. thaliana)vrn1-4 FRISung and Amasino, 2004 doi:10.1038/nature02195
Genetic reagent (A. thaliana)vrn2-1 FRIYang et al., 2017 doi:10.1126/science.aan1121
Genetic reagent (A. thaliana)vrn5-8 FRIGreb et al., 2007 doi:10.1016/j.cub.2006.11.052
Genetic reagent (A. thaliana)ndx1-1 FRISun et al., 2013 doi:10.1126/science.1234848
Genetic reagent (A. thaliana)fca-9Liu et al., 2007 doi:10.1016/j.molcel.2007.10.018
Genetic reagent (A. thaliana)fld-4Liu et al., 2007 doi:10.1016/j.molcel.2007.10.018
Genetic reagent (A. thaliana)fve-3Ausín et al., 2004 doi:10.1038/ng1295
Genetic reagent (A. thaliana)fca-9 FRIThis paperCross Col FRI and lines reported above. Requests for materials should be addressed to Caroline Dean.
Genetic reagent (A. thaliana)fld-4 FRIThis paperCross Col FRI and lines reported above. Requests for materials should be addressed to Caroline Dean.
Genetic reagent (A. thaliana)fve-3 FRIThis paperCross Col FRI and lines reported above. Requests for materials should be addressed to Caroline Dean.
Genetic reagent (A. thaliana)val1-2 FRIQüesta et al., 2016 doi:10.1126/science.aaf7354
Genetic reagent (A. thaliana)sdg8 FRIYang et al., 2014 doi:10.1016/j.cub.2014.06.047
Genetic reagent (A. thaliana)lhp1-3 FRIMylne et al., 2006 doi:10.1073/pnas.0507427103
Sequence-based reagentUBC_qPCR_FHepworth et al., 2018 doi:10.1038/s41467-018-03065-7CTGCGACTCAGGGAATCTTCTAA
Sequence-based reagentUBC_qPCR_RHepworth et al., 2018 doi:10.1038/s41467-018-03065-7TTGTGCCATTGAATTGAACCC
Sequence-based reagentPP2A QPCR F2Hepworth et al., 2018 doi:10.1038/s41467-018-03065-7ACTGCATCTAAAGACAGAGTTCC
Sequence-based reagentPP2A QPCR R2Hepworth et al., 2018 doi:10.1038/s41467-018-03065-7CCAAGCATGGCCGTATCATGT
Sequence-based reagentFLC_4265_F (spliced sense)Hepworth et al., 2018 doi:10.1038/s41467-018-03065-7AGCCAAGAAGACCGAACTCA
Sequence-based reagentFLC_5683_R (spliced sense)Hepworth et al., 2018 doi:10.1038/s41467-018-03065-7TTTGTCCAGCAGGTGACATC
Sequence-based reagentFLC_3966_F (unspliced sense)Hepworth et al., 2018 doi:10.1038/s41467-018-03065-7CGCAATTTTCATAGCCCTTG
Sequence-based reagentFLC_4135_R (unspliced sense)Hepworth et al., 2018 doi:10.1038/s41467-018-03065-7CTTTGTAATCAAAGGTGGAGAGC
Sequence-based reagentFLC unspliced RT (4029)Hepworth et al., 2018 doi:10.1038/s41467-018-03065-7TGACATTTGATCCCACAAGC
Sequence-based reagentVIN3 qPCR 1 FHepworth et al., 2018 doi:10.1038/s41467-018-03065-7TGCTTGTGGATCGTCTTGTCA
Sequence-based reagentVIN3 qPCR 1 RHepworth et al., 2018 doi:10.1038/s41467-018-03065-7TTCTCCAGCATCCGAGCAAG
Sequence-based reagentJF118-CCA1-FMacGregor et al., 2013 doi:10.1105/tpc.113.114959CTGTGTCTGACGAGGGTCGAA
Sequence-based reagentJF119-CCA1-RMacGregor et al., 2013 doi:10.1105/tpc.113.114959ATATGTAAAACTTTGCGGCAATACCT
Commercial assay or kitTurbo DNA Free KitInvitrogen
Commercial assay or kitSuperScript Reverse Transcriptase IIIInvitrogen
Commercial assay or kitRocheLightCycler 480 SYBR Green I Master
Software, algorithmRR Development Core Team, 2018
Software, algorithmLinReg PCRhttps://www.medischebiologie.nl/files/ Ruijter et al., 2009, doi:10.1093/nar/gkp045

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Decision letter

  1. Hao Yu
    Reviewing Editor; National University of Singapore & Temasek Life Sciences Laboratory, Singapore
  2. Christian S Hardtke
    Senior Editor; University of Lausanne, Switzerland
  3. Sureshkumar Balasubramanian
    Reviewer; Monash University, Australia
  4. Ilha Lee
    Reviewer; Seoul National University, Republic of Korea
  5. Xavier Picó
    Reviewer; Spanish National Research Council (CSIC), Spain

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Acceptance summary:

FLOWERING LOCUS C (FLC), a MADS-box transcription factor, plays a major role in determining flowering time in Arabidopsis in response to vernalization. In this manuscript, the authors analysed FLC silencing in natural variants throughout winter in three field sites over multiple years, and revealed that FLC starting levels and early phases of its silencing are the major determinants underlying the vernalization response. These findings suggest that the initial expression of FLC in natural variants is important for the adaption of vernalization response to natural fluctuating temperature profiles.

Decision letter after peer review:

Thank you for submitting your article "Natural variation in autumn FLC levels, rather than epigenetic silencing, aligns vernalization to different climates" for consideration by eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by Hao Yu as the Reviewing Editor and Christian Hardtke as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Sureshkumar Balasubramanian (Reviewer #1); Ilha Lee (Reviewer #2); Xavier Picó (Reviewer #3).

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

Summary:

In this manuscript, the authors suggest that FLC starting levels and early phases of its silencing are the major variables underlying the vernalization response via analysing FLC silencing in natural variants throughout winter in three field sites (Norwich, Northern Sweden, Southern Sweden).

Essential revisions:

While there was a common interest about the topic and the relevant findings, some major concerns on the description of methodologies and the interpretation of results as listed below need to be addressed to support the conclusion.

1) The Introduction could be further improved with more attractive description of the background and a working hypothesis to be uniquely tested by expression data from field settings in this study.

2) The definition of "aligning vernalization response to different climatic conditions" is unclear. Clarification of the relevant phenotypes, the measures, and the parameters used for this analysis is necessary for understanding the conclusion drawn from this analysis.

3) The meaning of FLC-post-vern value (m) in Table 1 is confusing and should be clarified as understanding how this value is calculated and used in this study will considerably influence interpretation of the relevant results. In addition, how the constant related to non-FLC-mediated bolting delay is defined also should be explained.

4) The authors should explain the reasons of different sowing and moving (to field sites) schedules for each experiment, and discuss the potential problems pertaining to the results and conclusion that may result from such variability.

5) The authors should clarify with concrete evidence or correct some specific terms, such as "unusual years" and "climate", used in the manuscript.

6) The conclusion and evolutionary implications of this study are not fully supported by the results based on the analysis of existing fitness components and their relationship with the major variable (FLC starting levels). The author should re-evaluate whether the traits (survival and fecundity) that were used as the major fitness components are appropriate for this study, and then correct some relevant descriptions and infer the conclusion and the relevant evolutionary implications accordingly.

7) As suggested by all reviewers, the quality of figures and tables as well as their citations in the text should be improved for style and clarity.

Please take into consideration the specific comments from the reviewers below to revise the manuscript.

Reviewer #1:

The manuscript by Hepworth et al., assesses FLC expression and its rate of decay and flowering time in a set of genotypes at multiple field sites (Norwich, Northern Sweden, Southern Sweden) in multiple seasons and tries to understand which part of the variable could explain vernalisation response better in natural conditions. The authors use Col FRI in Norwich condition to show that there are 3 aspects of the regulation of FLC expression. Initial levels, initial decay in FLC expression prior to the induction of VIN3 and later stages of decay in FLC expression that is VIN3 dependent. The authors argue that primarily the initial levels of FLC "aligns vernalization to different climatic conditions". It is nice that the authors use a simple descriptive approach to tease apart some complex phenotypic responses. I have some concerns which I list below.

First, the authors should define what is meant by "aligning vernalization (or sometimes they use term vernalization response) to different climatic conditions". What exactly is the quantitative measure of the same? In the absence of a quantitative measure, how can one conclude which of the 3 distinct quantitative measures (initial levels, slopes) best explain the variation in "aligning vernalization"? Authors show in Figure 5 that initial levels of FLC and initial shut down show higher levels of variability and draw the conclusion higher levels of variability means higher association with vernalization… Is this assumption valid? Also, what is the coefficient of variation at final FLC levels? I am presuming these levels differ to between genotypes. Is this distinctly different from CoV of initial FLC levels?

From the data it appears the second aspect (VIN3-independent shut down) is primarily seen in Norwich climate and not much in other sites. How much of the initial VIN3 induction explained by their temperature profiles?

I find the use of the "m-value" confusing. As far as I understood, the m-value appears to be highly variable independent of the allelic status of FLC. For example, Table 1 shows the m-value for Col to be 486 and Col FRI to be 36.5 (Norwich). Both genotypes have the same FLC allele. It is unclear to me how meaningful the conclusions of the m-value of FLC alleles are if the same allele has such different values? In this context, in the eighth paragraph of the Discussion, where the authors suggest that the variation in m-value derives from regulatory differences.

Reviewer #2:

The manuscript submitted by Hepworth et al. provides enormous data showing natural variation of vernalization response to the natural environments from Norwich to South and North Sweden, and suggests that starting level of FLC is critical for aligning vernalization to each climate. It is very difficult and time-consuming research to use natural environments with several accessions and mutants of Arabidopsis. In addition, to get some realistic idea about the evolutionary adaptive mechanism, we have to analyze the phenotypic response to natural environments. Thus, I think the work presented here is very useful information to not only plant scientists but also to the general readers. So, basically I recommend accepting this paper.

But I would like to suggest major revision as below. In that way, I think the manuscript will be easier to read. Otherwise, it will be too difficult to understand.

The major problem with this manuscript is the definition of the words.

For example, I could not understand the exact meaning of 'aligning vernalization response'. Could it be evolutionary fitness of vernalization? If it is, I think the authors should explain what kinds of phenotype are involved in the fitness (survival rate or seed number). Thus, I could not understand which data support the author's suggestion, 'starting FLC levels is critical for aligning vernalization to different climates'. I hope the manuscript revised to make it clear that how fitness and phenotypes of each accession are connected to the aligning vern.

Also, the meaning of FLC-post-vern value (m) should be clearly defined. I think this value is quite important to understand the whole manuscript. The value m does not look like FLC level right before transfer to warm temperature. As shown in Table 1, Col has much higher value than Col FRI (13 times higher). Thus, I feel there must be some equation to get the value and it must be provided.

Reviewer #3:

I have reviewed the manuscript entitled "Natural variation in autumn FLC levels, rather than epigenetic silencing, aligns vernalization to different climates" by Hepworth et al. This is an interesting manuscript dealing with an important topic in plant biology: the genetic mechanisms underlying flowering time, the most important fitness-related life-cycle trait in plants. Given the solid knowledge in the model plant Arabidopsis thaliana on the topic, the authors designed and carried out an array of experiments with various materials to address the role and interplay of FLC and VIN3 genes in flowering time. The most remarkable and strongest aspect of this study has to do with the field experiments conducted over several years, which in my opinion is the right scenario to address all study questions in plant evolutionary genetics. Although I have enjoyed the manuscript, some conclusions seem not to be supported by the results or read too speculative. In addition, I consider that some methodological issues also require some clarification because they are affecting the interpretation of the results. Below I go through all my major concerns:

1) The Introduction could be developed further and make it more attractive to specialized and non-specialized readers. As it is, it reads too general and I do not understand why. It seems that the major motivation of this study is that "the importance of cis FLC polymorphism in delivering the different phases of FLC silencing in different climates (is) unknown". Given the extensive knowledge on the study system by the co-authors and the whole research community, this Introduction deserves a more extended development of the theoretical background, and most importantly, a working hypothesis to be put to the test. In other words, be ambitious (conceptually speaking) because this study deserves it. In addition, expression data from natural field settings is still rare in the literature, which should also be highlighted as a promising avenue to increase and improve our knowledge on the genetic mechanisms underlying flowering time.

2) It is said that one of the study years was unusually warm (Introduction and elsewhere). However, the reader does not know how unusual that year was in comparison with the historical weather records at the experimental facilities. Given that 10 of the hottest years ever recorded across the planet have been documented in the last 12-15 years, it is clear that we are experiencing an unstoppable warming (on top of many other evidences). The odds of having "unusual years" are high from now on. In any case, the authors could show deviances from historical weather records in a very simple manner to reinforce their point. I understand that weather data obtained from loggers are not from official weather data, but data from local meteorological stations for the study years could be compared to historical weather records (means and deviances). Actually, differences between data loggers and meteorological stations inform about micro-environmental variation at the experimental facilities, which could also be the case here.

3) The authors confound weather (or environment) by climate (Introduction and elsewhere). The authors are not comparing FLC expression between temperate and tropical climates, but across different weather or environmental conditions represented by the three experimental facilities (all of them within the same climate type). Please check it out throughout the text.

4) I am confused about the different sowing and moving (to field sites) dates for each experiment (subsection “Field experiments”). Ideally, the comparison among sites or years should be based on experiments established at the same time to avoid undesirable sources of variation, such as differences in environmental conditions (even in a period of weeks) and day length. I cannot assess how this problem affected the results because plants from different sites and years have been exposed to different environmental conditions during their development due to variation in sowing and moving dates (particularly for all the winter experiments). However, given one of the main results of this study (the uniform vernalisation response across haplotypes in different years and environments) the problems posed by this flaw were probably mitigated (I am speculating here). In any case and for the sake of clarity, the authors should explain the reasons of such sowing and moving schedule and the problems that such variability may cause on this sort of experiments.

5) My other major concern about this work is the treatment of fitness as a concept. The authors clearly state that survival and fecundity are the two major fitness components (subsection “Variation at FLC affects fitness in the field through branching and silique number”), the two traits are treated and analyzed separately. In my opinion, the authors have all the data to estimate fitness as the product between such two components, that is, survival (survival to seed set) by fecundity (number of fruits per plant). Expressions like "… silique production, and hence fitness, in surviving plants.…" should be avoided because they are not correct. In line with this, I consider branching as a secondary parameter that increases fruit fecundity. Somehow, the authors pay more attention to branching pattern than to survival throughout the manuscript (I do see the effects of FLC on it though). In any case, this work could gain clarity and consistency if the authors focus on how the interesting parameters found (FLC starting levels to name a relevant one) affect fitness. Then they will have the means to infer the evolutionary implications (in terms of local adaptation) of their results.

6) In relation to the previous point, I found that conclusions on the adaptive variation value of the results are speculative throughout the text, mainly because fitness has not been tackled appropriately. For example, the following sentence is quite obscure and hard to follow: "What we describe as the starting level was measured after some days in the field, so it is not equivalent to a non-vernalized control. Some FLC shutdown, most likely VIN3-independent, will have occurred at that time. Therefore, it is likely that the combination of these two determinants of FLC levels early in the field (starting levels, VIN3-independent shutdown) provides most of the potentially adaptive variation". I simply cannot see how the authors reach this conclusion, which deserves some work disentangling the effects of FLV and VIN-3 variation on fitness. There are further examples about this and overall the authors should tone-down the evolutionary implications of their results (see also "… our detailed analysis of the different phases of FLC silencing through winters in distinct climates, over multiple years, has given a clear picture of the mechanistic basis of adaptation in vernalization response”).

7) Finally, there are many style issues, particularly in the graphical material. For example, panels in Figure 1 should be lined up and have the same size and scale. Overall, I would increase symbol size to identify all haplotypes within panels and across figures or change the way to indicate that. Coefficients of variation are normally given as percentages. Letter size and type varies among figures. Chose the format (number of digits) to present data and be consistent (see the huge heterogeneity in Table 1). Crosses in Figure 6 are expected to be means (SE).

https://doi.org/10.7554/eLife.57671.sa1

Author response

Essential revisions:

While there was a common interest about the topic and the relevant findings, some major concerns on the description of methodologies and the interpretation of results as listed below need to be addressed to support the conclusion.

1) The Introduction could be further improved with more attractive description of the background and a working hypothesis to be uniquely tested by expression data from field settings in this study.

We thank the editors and reviewers for this comment. We believe we have now significantly improved the Introduction to include a more full and attractive description as well as a working hypothesis.

2) The definition of "aligning vernalization response to different climatic conditions" is unclear. Clarification of the relevant phenotypes, the measures, and the parameters used for this analysis is necessary for understanding the conclusion drawn from this analysis.

We hope the extensive revisions we have made address this important issue.

3) The meaning of FLC-post-vern value (m) in Table 1 is confusing and should be clarified as understanding how this value is calculated and used in this study will considerably influence interpretation of the relevant results. In addition, how the constant related to non-FLC-mediated bolting delay is defined also should be explained.

We have re-written this paragraph to include the calculation in the main text as well as the table and supplementary figure legends and clarified that the constant is highly dependent on the specific experiment. A more detailed reply has been included in the response to individual reviewers.

4) The authors should explain the reasons of different sowing and moving (to field sites) schedules for each experiment, and discuss the potential problems pertaining to the results and conclusion that may result from such variability.

We now explain our motivation for sowing and moving for the Swedish sites (practicality and to ensure germination) in the Materials and methods, the reasons for the different sowing dates (local differences in climate) in the description of the field experiments, and the reasons for differences between years in the section ‘Natural variation in different phases of FLC silencing in the field’.

5) The authors should clarify with concrete evidence or correct some specific terms, such as "unusual years" and "climate", used in the manuscript.

Thank you, we have improved our definition of climate according to the Köppen-Geiger classification system, and shown comparison of temperature averages to support the observation of the unusually warm autumn in September 2016 in North Sweden (more detailed responses provided to the reviewers).

6) The conclusion and evolutionary implications of this study are not fully supported by the results based on the analysis of existing fitness components and their relationship with the major variable (FLC starting levels). The author should re-evaluate whether the traits (survival and fecundity) that were used as the major fitness components are appropriate for this study, and then correct some relevant descriptions and infer the conclusion and the relevant evolutionary implications accordingly.

We have changed the title of the last section to soften the claim. We think our explanation of our measures was principally at fault, and therefore we have changed the description of these results to clarify when we are discussing FLC levels, when we are discussing genotype fitness, and when we are discussing individual plant fitness. We have reduced our discussion of the branching factor, and changed the summing-up paragraph to say we have “shed light on” rather than “given a clear picture” to the mechanisms of adaptation.

7) As suggested by all reviewers, the quality of figures and tables as well as their citations in the text should be improved for style and clarity.

Thank you, we have made revisions in response to the individual suggestions from the reviewers, and would like to thank them for their help in substantially improving the manuscript.

Please take into consideration the specific comments from the reviewers below to revise the manuscript.

Reviewer #1:

[…] I have some concerns which I list below.

First, the authors should define what is meant by "aligning vernalization (or sometimes they use term vernalization response) to different climatic conditions". What exactly is the quantitative measure of the same?

Prof. Balasubramanian raises a very good point – our definition is not clear. We have endeavoured to correct this, by stating that we took as ‘alignment’ the incidence of ‘appropriate’ or ‘springtime’ flowering as our measure, as in ‘aligning development with the seasons’. We have changed the title to remove the word ‘aligning’, and specify our hypothesis and measures more clearly, we hope, throughout the text.

In the absence of a quantitative measure, how can one conclude which of the 3 distinct quantitative measures (initial levels, slopes) best explain the variation in "aligning vernalization"? Authors show in Figure 5 that initial levels of FLC and initial shut down show higher levels of variability and draw the conclusion higher levels of variability means higher association with vernalization… Is this assumption valid?

We believe that the higher variability of the initial level and starting slopes is biologically most relevant because we see the greatest variation in resulting flowering time during the time at which the initial phase only is still ongoing (from transfer experiments in Figure 6A, C and E vs. B, D and F respectively). We have modified the manuscript to make this assumption clearer.

Also, what is the coefficient of variation at final FLC levels? I am presuming these levels differ to between genotypes. Is this distinctly different from CoV of initial FLC levels?

Prof. Balasubramanian raises an interesting and complex point. There are two obvious candidates for final FLC level – the level in spring, and the threshold that allows flowering in any given context. In the 2014-15 experiment, FLC levels in spring did not correlate with flowering time, likely because they were far below the point at which FLC had a detectable effect on flowering. We have now made this point in section “High autumnal FLC reduces precocious flowering in a warm autumn”.

Threshold is also difficult as figures Figure 6G and H shows that for almost all plants (except Lov1 and Var accessions) FLC levels are low enough to only delay, rather than totally prevent, flowering. Nevertheless, the FLC effects are quantitative over most of the range of expression during autumn. We therefore concentrated on autumn FLC dynamics, as at this time FLC was detectably delaying flowering in the transfer experiments, Figure 6, and in the field Figure 7. We have modified the text to make this clearer.

(N.B. in N Sweden 2016, we note that in the week preceding flowering, regardless of whether this was before or after winter, maximum daily temperatures were at least 15.5 and averaged 19.8°C, even though overall average daily temperatures were very cold (<10°C), strongly suggesting limitation by the ambient temperature pathway in parallel with that of the FLC effects, but that only part of the day need reach these temperatures to remove FLM/SVP repression).

From the data it appears the second aspect (VIN3-independent shut down) is primarily seen in Norwich climate and not much in other sites. How much of the initial VIN3 induction explained by their temperature profiles?

The VIN3 induction is very sensitive to temperature. In previous work (Antoniou-Kourounioti et al., 2018) we showed that for Col FRI the temperature profile of these field studies could be used to predict VIN3 expression using a mathematical model. Since the VIN3 measurements follow similar patterns of response between the different genotypes at the same site (although with different maxima and minima), we would conclude the same complexity for the natural accessions and mutants presented in the current work. Comments were added to highlight the temperature sensitivity of VIN3 in the manuscript (subsection “Natural variation in different phases of FLC silencing in the field”, third paragraph, Discussion, third paragraph).

I find the use of the "m-value" confusing. As far as I understood, the m-value appears to be highly variable independent of the allelic status of FLC. For example, Table 1 shows the m-value for Col to be 486 and Col FRI to be 36.5 (Norwich). Both genotypes have the same FLC allele. It is unclear to me how meaningful the conclusions of the m-value of FLC alleles are if the same allele has such different values? In this context, in the eighth paragraph of the Discussion, where the authors suggest that the variation in m-value derives from regulatory differences.

We have removed the ‘post-vern’ estimations for Col-0 as these are indeed confusing – the strange values are due to FLC being so low initially that the estimations are based on points that are too close together for accurate inference – reflected in the high p value (0.19) for the estimation. (In Col-0 flowering is mainly delayed by other pathways – other genotypes don’t reach similar levels of FLC until they have vernalised for a long time and pathways such as the miR156 pathway are no longer active). We have commented on this in the text.

We have rewritten the paragraph discussing the derivation of the FLC post-vern value to describe the linear regression in more detail and clarify that while in the accessions the effect is too much affected by other GxE effects to be reliable, within the NILs it is more consistent across environments.

Reviewer #2:

[…] I would like to suggest major revision as below. In that way, I think the manuscript will be easier to read. Otherwise, it will be too difficult to understand.

The major problem with this manuscript is the definition of the words.

For example, I could not understand the exact meaning of 'aligning vernalization response'. Could it be evolutionary fitness of vernalization? If it is, I think the authors should explain what kinds of phenotype are involved in the fitness (survival rate or seed number). Thus, I could not understand which data support the author's suggestion, 'starting FLC levels is critical for aligning vernalization to different climates'. I hope the manuscript revised to make it clear that how fitness and phenotypes of each accession are connected to the aligning vern.

We thank the reviewer for the positive comments.

We have endeavoured to be clearer about our concept of ‘alignment’ throughout the text, by stating that this is the alignment of flowering with spring conditions, and that in North Sweden, where this alignment breaks down, both survival and reproduction are affected differently by the FLChaplotypes. We have therefore made several changes throughout the text.

Also, the meaning of FLC-post-vern value (m) should be clearly defined. I think this value is quite important to understand the whole manuscript. The value m does not look like FLC level right before transfer to warm temperature. As shown in Table 1, Col has much higher value than Col FRI (13 times higher). Thus, I feel there must be some equation to get the value and it must be provided.

We have tried to clarify this parameter by referring to the equation in the main text and reorganising this section. We have removed the Col-0 value (as mentioned in response to Reviewer 1) as, in the absence of FRI, FLC levels are too low to estimate the post-vern value accurately, and commented on this in the text.

Reviewer #3:

I have reviewed the manuscript entitled "Natural variation in autumn FLC levels, rather than epigenetic silencing, aligns vernalization to different climates" by Hepworth et al. This is an interesting manuscript dealing with an important topic in plant biology: the genetic mechanisms underlying flowering time, the most important fitness-related life-cycle trait in plants. Given the solid knowledge in the model plant Arabidopsis thaliana on the topic, the authors designed and carried out an array of experiments with various materials to address the role and interplay of FLC and VIN3 genes in flowering time. The most remarkable and strongest aspect of this study has to do with the field experiments conducted over several years, which in my opinion is the right scenario to address all study questions in plant evolutionary genetics. Although I have enjoyed the manuscript, some conclusions seem not to be supported by the results or read too speculative. In addition, I consider that some methodological issues also require some clarification because they are affecting the interpretation of the results. Below I go through all my major concerns:

1) The Introduction could be developed further and make it more attractive to specialized and non-specialized readers. As it is, it reads too general and I do not understand why. It seems that the major motivation of this study is that "the importance of cis FLC polymorphism in delivering the different phases of FLC silencing in different climates (is) unknown". Given the extensive knowledge on the study system by the co-authors and the whole research community, this Introduction deserves a more extended development of the theoretical background, and most importantly, a working hypothesis to be put to the test. In other words, be ambitious (conceptually speaking) because this study deserves it. In addition, expression data from natural field settings is still rare in the literature, which should also be highlighted as a promising avenue to increase and improve our knowledge on the genetic mechanisms underlying flowering time.

We thank the reviewer for the positive comments. We have developed the Introduction further and included specific hypotheses. We couldn’t possibly do much justice to the extensive work of the flowering time community within the word limit, but have tried to improve what we have. Regarding the major motivation – we only observed that the different phases were temperature-separable in these same experiments. We have rephrased the Introduction to make this clearer.

2) It is said that one of the study years was unusually warm (Introduction and elsewhere). However, the reader does not know how unusual that year was in comparison with the historical weather records at the experimental facilities. Given that 10 of the hottest years ever recorded across the planet have been documented in the last 12-15 years, it is clear that we are experiencing an unstoppable warming (on top of many other evidences). The odds of having "unusual years" are high from now on. In any case, the authors could show deviances from historical weather records in a very simple manner to reinforce their point. I understand that weather data obtained from loggers are not from official weather data, but data from local meteorological stations for the study years could be compared to historical weather records (means and deviances). Actually, differences between data loggers and meteorological stations inform about micro-environmental variation at the experimental facilities, which could also be the case here.

Many thanks for the suggestion, we have compared the September conditions for 2016 to weather-station data and found this year to be warm, although not the warmest in recent years – which is in itself interesting, as the reviewer notes. We have included the specific data in the “Natural variation in different phases of FLC silencing in the field” section.

3) The authors confound weather (or environment) by climate (Introduction and elsewhere). The authors are not comparing FLC expression between temperate and tropical climates, but across different weather or environmental conditions represented by the three experimental facilities (all of them within the same climate type). Please check it out throughout the text.

We thank Prof. Picó for pointing out that we have confused these concepts in some cases. We have corrected instances in which we were referring to the year-on-year differences in North Sweden, and further researched the classifications of climate to describe our field sites according to the Köppen-Geiger system of climate designation in the ‘Field experiments’ section of the Results.

4) I am confused about the different sowing and moving (to field sites) dates for each experiment (subsection “Field experiments”). Ideally, the comparison among sites or years should be based on experiments established at the same time to avoid undesirable sources of variation, such as differences in environmental conditions (even in a period of weeks) and day length. I cannot assess how this problem affected the results because plants from different sites and years have been exposed to different environmental conditions during their development due to variation in sowing and moving dates (particularly for all the winter experiments). However, given one of the main results of this study (the uniform vernalisation response across haplotypes in different years and environments) the problems posed by this flaw were probably mitigated (I am speculating here). In any case and for the sake of clarity, the authors should explain the reasons of such sowing and moving schedule and the problems that such variability may cause on this sort of experiments.

Indeed, this is a limitation on our work. The sowing and moving schedule in Sweden was used because of the difficulty of sowing directly into the field, and to ensure sufficient germination. A note has been made in the Materials and methods to explain this. For the different germination dates, reasons have been given in the main text, and a paragraph discussing this point in the Discussion.

5) My other major concern about this work is the treatment of fitness as a concept. The authors clearly state that survival and fecundity are the two major fitness components (subsection “Variation at FLC affects fitness in the field through branching and silique number”), the two traits are treated and analyzed separately. In my opinion, the authors have all the data to estimate fitness as the product between such two components, that is, survival (survival to seed set) by fecundity (number of fruits per plant). Expressions like "… silique production, and hence fitness, in surviving plants.…" should be avoided because they are not correct. In line with this, I consider branching as a secondary parameter that increases fruit fecundity. Somehow, the authors pay more attention to branching pattern than to survival throughout the manuscript (I do see the effects of FLC on it though). In any case, this work could gain clarity and consistency if the authors focus on how the interesting parameters found (FLC starting levels to name a relevant one) affect fitness. Then they will have the means to infer the evolutionary implications (in terms of local adaptation) of their results.

A previous version of this manuscript had investigated the effect of delayed flowering on mortality in our North Sweden transfers in more explicit detail, but the Editor and other reviewers suggested removing it, particularly as we do not know the cause of death of these plants (a point we have now added to the Discussion). Hence, we do not concentrate too much on FLC effects on survival. We have reduced the section on branching in the Discussion, and removed it from the Abstract. Within the final section, we have changed the language to make it clear when we are discussing individual plant fitness versus genotype fitness, and attempted to convey better how we have brought these elements together. We have changed the problematic sentence to:

“In the field, silique production is closely linked to branch production”

6) In relation to the previous point, I found that conclusions on the adaptive variation value of the results are speculative throughout the text, mainly because fitness has not been tackled appropriately. For example, the following sentence is quite obscure and hard to follow: "What we describe as the starting level was measured after some days in the field, so it is not equivalent to a non-vernalized control. Some FLC shutdown, most likely VIN3-independent, will have occurred at that time. Therefore, it is likely that the combination of these two determinants of FLC levels early in the field (starting levels, VIN3-independent shutdown) provides most of the potentially adaptive variation". I simply cannot see how the authors reach this conclusion, which deserves some work disentangling the effects of FLV and VIN-3 variation on fitness.

This was poorly phrased, we were not intending to imply an effect on fitness in this section, just highlighting that these metrics result in the different levels of FLC between the different accessions, and that if different levels of FLC have a fitness effect (which we investigate later) then these are the mechanisms that produce this variation. We have edited the text to clarify that the combination of these metrics “provides most of the variation in FLC levels”.

There are further examples about this and overall the authors should tone-down the evolutionary implications of their results (see also "… our detailed analysis of the different phases of FLC silencing through winters in distinct climates, over multiple years, has given a clear picture of the mechanistic basis of adaptation in vernalization response”.

We have modified our Abstract, conclusory statement within the Introduction, and Discussion accordingly.

7) Finally, there are many style issues, particularly in the graphical material. For example, panels in Figure 1 should be lined up and have the same size and scale.

We have corrected this.

Overall, I would increase symbol size to identify all haplotypes within panels and across figures or change the way to indicate that.

We have standardised the figure size and font size to ensure labels are clear. We have also changed the plots for flowering time to show the full distribution, which also makes the identification of each genotype easier.

Coefficients of variation are normally given as percentages.

We have corrected this.

Letter size and type varies among figures.

We have corrected this.

Chose the format (number of digits) to present data and be consistent (see the huge heterogeneity in Table 1).

We have done this for Table 1, thank you.

Crosses in Figure 6 are expected to be means (SE).

They are. We have noted that the error bars are s.e.m. In some cases errors are smaller than the crosses.

https://doi.org/10.7554/eLife.57671.sa2

Article and author information

Author details

  1. Jo Hepworth

    Cell and Developmental Biology, John Innes Centre, Norwich, United Kingdom
    Contribution
    Data curation, Formal analysis, Supervision, Investigation, Visualization, Methodology, Writing - original draft
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4621-8414
  2. Rea L Antoniou-Kourounioti

    Computational and Systems Biology, John Innes Centre, Norwich, United Kingdom
    Contribution
    Data curation, Formal analysis, Supervision, Investigation, Visualization, Writing - original draft
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5226-521X
  3. Kristina Berggren

    Department of Natural Sciences, Mid Sweden University, Sundsvall, Sweden
    Contribution
    Investigation
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7859-9928
  4. Catja Selga

    Department of Biology, Lund University, Lund, Sweden
    Contribution
    Investigation
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8683-1291
  5. Eleri H Tudor

    Crop Genetics, John Innes Centre, Norwich, United Kingdom
    Contribution
    Formal analysis, Investigation, Methodology
    Competing interests
    No competing interests declared
  6. Bryony Yates

    Cell and Developmental Biology, John Innes Centre, Norwich, United Kingdom
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  7. Deborah Cox

    Cell and Developmental Biology, John Innes Centre, Norwich, United Kingdom
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  8. Barley Rose Collier Harris

    Cell and Developmental Biology, John Innes Centre, Norwich, United Kingdom
    Contribution
    Investigation
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5745-1812
  9. Judith A Irwin

    Crop Genetics, John Innes Centre, Norwich, United Kingdom
    Contribution
    Conceptualization, Supervision, Funding acquisition, Methodology
    Competing interests
    No competing interests declared
  10. Martin Howard

    Computational and Systems Biology, John Innes Centre, Norwich, United Kingdom
    Contribution
    Conceptualization, Supervision, Funding acquisition, Methodology, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7670-0781
  11. Torbjörn Säll

    Department of Biology, Lund University, Lund, Sweden
    Contribution
    Conceptualization, Supervision, Funding acquisition, Investigation, Methodology, Writing - review and editing
    Competing interests
    No competing interests declared
  12. Svante Holm

    Department of Natural Sciences, Mid Sweden University, Sundsvall, Sweden
    Contribution
    Conceptualization, Supervision, Funding acquisition, Investigation, Methodology, Writing - review and editing
    For correspondence
    Svante.Holm@miun.se
    Competing interests
    No competing interests declared
  13. Caroline Dean

    Cell and Developmental Biology, John Innes Centre, Norwich, United Kingdom
    Contribution
    Conceptualization, Resources, Supervision, Funding acquisition, Investigation, Methodology, Project administration, Writing - review and editing
    For correspondence
    caroline.dean@jic.ac.uk
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6555-3525

Funding

Horizon 2020 Framework Programme (MEXTIM)

  • Jo Hepworth
  • Rea L Antoniou-Kourounioti
  • Kristina Berggren
  • Catja Selga
  • Eleri H Tudor
  • Deborah Cox
  • Barley Rose Collier Harris
  • Judith A Irwin
  • Martin Howard
  • Torbjörn Säll
  • Svante Holm
  • Caroline Dean

Biotechnology and Biological Sciences Research Council (BB/J004588/1)

  • Jo Hepworth
  • Rea L Antoniou-Kourounioti
  • Eleri H Tudor
  • Deborah Cox
  • Barley Rose Collier Harris
  • Judith A Irwin
  • Martin Howard
  • Caroline Dean

Biotechnology and Biological Sciences Research Council (BB/P013511/1)

  • Jo Hepworth
  • Rea L Antoniou-Kourounioti
  • Eleri H Tudor
  • Bryony Yates
  • Deborah Cox
  • Barley Rose Collier Harris
  • Judith A Irwin
  • Martin Howard
  • Caroline Dean

Biotechnology and Biological Sciences Research Council (BB/P003095/1)

  • Jo Hepworth
  • Eleri H Tudor
  • Judith A Irwin

Biotechnology and Biological Sciences Research Council (BB/L016079/1)

  • Eleri H Tudor

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

For genetic materials, we are indebted to Prof. Johanna Schmitt (University of California Davis) for the kind gift of vin3-1 FRI, and Huamei Wang for creating the Ull and Bro NIL lines and preparing seed stocks. For the Swedish field sites, many thanks to family Öhman and Nils Jönsson. Thanks to Prof. James Brown (JIC) for advice on the statistics, and to Ian Baldwin (Senior Editor at eLIFE) for helpful suggestions on the initial submission of the manuscript. Finally the authors would like to reiterate their appreciation of all those from the Dean, Howard, Holm, Säll and Irwin groups who helped in cold, heat, wind, rain and laboratory with the field studies, plus Ingalill Thorsell at Drakamöllan Gårdshotell for making the lab retreats during the field studies so enjoyable.

Senior Editor

  1. Christian S Hardtke, University of Lausanne, Switzerland

Reviewing Editor

  1. Hao Yu, National University of Singapore & Temasek Life Sciences Laboratory, Singapore

Reviewers

  1. Sureshkumar Balasubramanian, Monash University, Australia
  2. Ilha Lee, Seoul National University, Republic of Korea
  3. Xavier Picó, Spanish National Research Council (CSIC), Spain

Publication history

  1. Received: April 8, 2020
  2. Accepted: September 8, 2020
  3. Accepted Manuscript published: September 9, 2020 (version 1)
  4. Version of Record published: September 25, 2020 (version 2)

Copyright

© 2020, Hepworth et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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