
==== Front
bioRxiv
BIORXIV
bioRxiv
2692-8205
Cold Spring Harbor Laboratory

39282395
10.1101/2024.09.02.610887
preprint
1
Article
Genetic variation in phenology of wild Arabidopsis thaliana plants
DeLeo Victoria L. 1
Marais David L. Des 2
Juenger Thomas E. 3
Lasky Jesse R. 14
1 Department of Biology, Pennsylvania State University
2 Department of Civil and Environmental Engineering, Massachusetts Institute of Technology
3 Department of Integrative Biology, University of Texas at Austin
Author Contribution

JRL conceived of the project with TEJ, DLDM assisted in experiments, VLD conducted experiments, and analyzed data, VLD and JRL led writing, all authors contributed to the interpretation and writing of the manuscript.

4 Author for correspondence. lasky@psu.edu
03 9 2024
2024.09.02.610887https://creativecommons.org/licenses/by-nc-nd/4.0/ This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator.
nihpp-2024.09.02.610887.pdf
Phenology and the timing of development are often under selection, but at the same time influence selection on other traits by controlling how traits are expressed across seasons. Plants often exhibit high natural genetic variation in phenology when grown in controlled environments, and many genetic and molecular mechanisms underlying phenology have been dissected. There remains considerable diversity of germination and flowering time within populations in the wild and the contribution of genetics to phenological variation of wild plants is largely unknown. We obtained collection dates of naturally inbred Arabidopsis thaliana accessions from nature and compared them to experimental data on the descendant inbred lines that we synthesized from two new and 155 published controlled experiments. We tested whether the genetic variation in flowering and germination timing from experiments predicted the phenology of the same inbred lines in nature. We found that genetic variation in phenology from controlled experiments significantly, but weakly, predicts day of collection from the wild, even when measuring collection date with accumulated photothermal units. We found that experimental flowering time breeding values were correlated to wild flowering time at location of origin estimated from herbarium collections. However, local variation in collection dates within a region was not explained by genetic variation in experiments, suggesting high plasticity across small-scale environmental gradients. This apparent low heritability in natural populations may suggest strong selection or many generations are required for phenological adaptation and the emergence of genetic clines in phenology.
==== Body
pmcIntroduction

Phenology, or the timing of the developmental transitions between an organism’s life stages, directly and indirectly influences plant fitness and selection by determining the traits that are expressed at any point throughout the year (Donohue 2005). In seasonally variable environments, traits such as flowering time, growth rate, and dormancy can ameliorate harsh abiotic conditions by timing dormant periods to coincide with unfavorable seasons (e.g. drought escape (Ludlow 1989; Lawrence-Paul and Lasky 2024). Phenology may be under distinct selective pressures to maximize growth and resource acquisition during favorable conditions and reduce the risk of experiencing unfavorable conditions during sensitive growth stages, potentially leading to fitness tradeoffs between fast growing, resource acquisitive organisms and slow growing, resource conservative organisms (Stearns 1989; Franco and Silvertown 1996; Reich 2014; Salguero-Gómez et al. 2016). Phenology is determined by both endogenous and external cues, i.e. genetic and plastic variation (Amasino 2004; Andrés and Coupland 2012; Auge et al. 2018) but their relative importance in nature is unclear even in model plants.

A large portion of knowledge about the genetic basis for plant phenology comes from study of the model Arabidopsis thaliana (hereafter Arabidopsis). Common garden trials in both lab and field settings of inbred lines have uncovered genetic loci and genotype by environment interactions contributing to much of the observed variation in dormancy and flowering time (Juenger et al., 2005; Brachi et al., 2010; Fournier-Level et al., 2013; Ågren et al., 2017). Flowering time and dormancy are determined by complex, overlapping gene networks (Simpson and Dean 2002; Wilczek et al. 2010). These traits are also plastic (Juenger et al. 2005; Zhou et al. 2005; Wilczek et al. 2009): germination responds to temperature, photoperiod, moisture, and nutrient availability (Huang et al. 2010, 2018; Penfield and Springthorpe 2012; Footitt et al. 2013; Kenney et al. 2014), while flowering responds to multiple cues such as temperature and daylength (Thomas and Vince-Prue 1997; Lempe et al. 2005; Balasubramanian et al. 2006). The weight of environmental cues in phenological timing contributes to cascading effects on later phenological stages (Donohue 2005), where, for example, the timing of flowering within the year determines seed maturation environment and thus dormancy (Chiang et al. 2013; Springthorpe and Penfield 2015). In turn, germination timing can influence the expression of flowering time by determining the seasonal environment during growth (Zhou et al. 2005; Li et al. 2010; Chiang et al. 2013).

The broad phenological variation described in experiments is observed in wild populations as well (Ratcliffe 1976; Simpson and Dean 2002; DeLeo et al. 2020). Arabidopsis can exhibit the life history of a winter annual, germinating in the fall, spending the winter as a rosette, and flowering in the spring. However, Arabidopsis can also germinate and flower in a single season. These shorter-lived plants can flower in spring, summer, or fall, and in some regions this fast life cycle enables multiple generations to complete within a year. This variation in life histories occurs in many annual plants (Baskin and Baskin 1988) and is made possible in part by a range of flowering times and germination traits that can vary independently from each other (Marcer et al. 2018; Martínez-Berdeja et al. 2020). Broad geographic clines in breeding values for components of phenology and allele frequencies of phenology QTL as well as evidence of selection on phenology QTL (Caicedo et al. 2004; Stinchcombe et al. 2004; Samis et al. 2012; Debieu et al. 2013; Fournier-Level et al. 2013; Ågren et al. 2017; Exposito-Alonso et al. 2018; Gamba et al. 2023; Lasky et al. 2024) support the conclusion that phenology is adapted to local environmental conditions in the wild. Yet, even within geographic proximity one can find genotypes with substantial genetic variation in flowering time (Alonso-Blanco et al. 2016; Méndez-Vigo et al. 2022).

It is not known to what degree genetic variation explains the phenology of wild Arabidopsis individuals, and there are several reasons to expect the phenology of a given genotype in experiments versus nature to be different (Wilczek et al. 2009). While genotype likely influences the phenology of an individual plant in the wild, interactions between stages as well as plasticity may limit the translation of genetic values of single stage phenology to natural phenology. First, there are interactions between germination and flowering time, both because of a shared genetic basis and because the timing of early life transitions influences environmental exposures in later life stages (Chiang et al. 2009; Huang et al. 2010; Springthorpe and Penfield 2015; Huo et al. 2016). Secondly, there is stochasticity in individual germination and flowering time (Jimenez-Gomez et al. 2011; Abley et al. 2020). Thirdly, spatial environmental variation is extensive in nature and genotype-environment interactions have major impacts on phenology. Maternal effects are an important source of plasticity especially for seed dormancy (Boyd et al. 2007; Chiang et al. 2013; Burghardt et al. 2016; Huang et al. 2018) and there is often variation in phenology even under tightly controlled growing conditions. For these reasons, flowering time loci identified in common garden lab or field experiments might fail to predict flowering time variation among natural individuals (Chiang et al. 2013), possibly due to additional variation in dormancy (Huang et al. 2010; Chiang et al. 2013) and genotype-environment interactions across environmental gradients (Wilczek et al. 2009; Brachi et al. 2010a; Burghardt et al. 2015; Springthorpe and Penfield 2015). Genotypes differing in phenology in one set of conditions can have to the same expressed phenology in another set of conditions (Burghardt et al. 2016), and there is great diversity in germination and flowering date within populations and within genetic backgrounds (Brachi et al. 2013; Méndez-Vigo et al. 2013; Johnston and Bassel 2018; Abley et al. 2020).

The correspondence between genetic breeding values for phenological traits in controlled experiments and the phenology in nature of the same genotypes has been little studied owing to a lack of data. Here, we exploit measurements of wild phenology of individual plants from natural history collections (herbaria and seed stock centers) (Miller-Rushing et al. 2006; MacGillivray et al. 2010; Davis et al. 2015). Because Arabidopsis are naturally inbred, we can compare collection dates of wild individuals with phenology of nearly genetically identical descendants in controlled experiments. This comparison may provide a window into how genetic variation shapes phenology in natural populations against the forces of plasticity and GxE.

We developed several hypotheses for how the phenology of individual plants in nature would be related to genetic variation in phenological traits, elaborated in Figure 1. To summarize, we expected that genetic clines in flowering time (early flowering in warmer climates Figure 1D) and germination rates (Figure 1F), combined with plastic acceleration of flowering in warmer conditions (Figure 1E) would lead to range-wide positive relationships between breeding values for flowering time, germination rates, and collection dates (Figure 1G–J). Locally, within populations, where much of the plasticity seen across the species range is reduced, we expected genetic effects on flowering time would be positively related to collection dates and that this relationship would be the most accurate signal of genetic effects on phenology (Figure S1).

Materials and Methods

Natural genotypes

To complement published data (see below), we tested a set of 101 naturally inbred genotypes (“ecotypes”) with known collection date (either from the herbarium specimen label or recorded in the Arabidopsis Biological Resource Center https://abrc.osu.edu/ database). 47 of these ecotypes had not been studied in previous flowering time experiments and 75 had not been included in previous germination experiments. Seed was ordered from ABRC (94 accessions) or was germinated from herbarium sheets (7 accessions).

To improve germination, seeds from herbarium specimens were cold-stratified in tap water at pH 7 and placed at 4°C for 7 d. Seeds were then directly sown into damp Fafard germination mix and grown in Conviron growth chambers under 10/14hr, 18/22°C days/nights. Seven herbarium accessions germinated using this protocol. In the second round of germination, we surface sterilized seeds using standard protocols and then cold-stratified, as above. We plated seeds onto MS+Gamborg’s vitamins + agar plates containing 1% sucrose and 10uM GA4 and then placed them in growth chambers, as above. Two more herbarium accessions flowered using this protocol; these were transplanted to Fafard Germination mix. To induce flowering, plants were exposed to 30d of 4°C with 10/18hr day/night cycles.

Flowering and germination experiments

Ecotypes were grown in common conditions prior to the flowering time experiment, and flowering time replicates were descended from a single mother plant. For each ecotype, three replicates were grown in separate pots. Seeds were stratified at 4°C for 5 days before sowing in pots. Each pot was thinned to a single individual after the emergence of the second set of true leaves. Plants were grown at 22°C under 16h days of fluorescent light in a walk-in Conviron growth chamber (model MTPS). Day of bolting and day petals appeared were both recorded as measures of flowering time.

Seeds from each replicate maternal plant in the flowering time experiment were collected and stored separately in dry conditions until the germination trial. For each treatment, forty seeds from each parent plant (or as many seeds as were available for replicates with low fecundity) evenly divided across 2 plates were sown on filter paper in petri dishes and germinated at 23/18°C during day/night with constant 16h daylength in a Conviron growth chamber. In total, 1,752 plates and >50k seeds were assayed.

Seeds were subjected to cold stratification at 4°C in the dark for 3 treatment lengths: 2 weeks, 3 days, and 0 days. Cold stratification can break primary dormancy, however 2 weeks of chilling can prompt secondary dormancy in the seedbank (Penfield and Springthorpe 2012). The difference in germination rate between 3 days and 0 days of stratification can therefore indicate primary dormancy while the difference in germination rate between 3 days and 2 weeks of stratification may indicate secondary dormancy. As a caveat, lower germination after 2 weeks of stratification could be due to other, unmeasured negative effects on germination, such as bacterial infection. We staggered the planting so that all the plates came out of stratification on the same day. The number of seeds that had germinated in each plate was recorded on days 1, 3, 5, 10, 14, 21, and 28. Seeds were considered germinated if the radicle was visible. Because maternal plants flowered at different times, germination rates for this experiment may be influenced by the length of the time between flowering and planting and any after-ripening that may have occurred. We also tested for these maternal effects among individuals of the same inbred line (see below).

Phenology from published experiments

We searched the literature for experiments on Arabidopsis that measured flowering time or germination traits across different natural inbred lines (commonly referred to as Arabidopsis “ecotypes”). The minimum number of ecotypes in any single experiment was 17. Our final set used data from 38 previous studies (31 included some measure of flowering time, 15 included germination) that, combined with our new experiments described above, included over 3,000 ecotypes for 86 flowering time experimental conditions and 66 germination experimental conditions, although all ecotypes were used in only a subset of the trials and only 291 ecotypes had a reliable date of original collection from the wild.

We used this dataset of phenology measurements from the literature to create an estimate of genetic variation in flowering time and dormancy among ecotypes. We sought to gain statistical power by combining data from different experiments. To make phenotypes comparable, we standardized across treatments and experiments, transforming each flowering time experiment such that the earliest flowering accession had a value of 0 and the latest flowering accession had a value of 1.

We averaged standardized experimental flowering times across experiments, keeping vernalized, non-vernalized, and field experiments separate. Here, we use ‘vernalization’ to describe extended cold treatments applied to rosettes. Non-vernalized growing conditions uncover genetic variation in flowering time due to vernalization requirements that is masked under vernalized or fall-sown field conditions and may be important for determining phenology and life history in the wild (Wilczek et al. 2009). However, Arabidopsis plants growing in many natural settings are expected to experience changes in temperature and photoperiod that would be more similar to vernalized and field experiments (Li et al. 2010). By keeping the three treatments separate, we could test whether non-vernalized flowering time or vernalized flowering time was more predictive of wild phenology. Field flowering time was aggregated across all seasons, which may lead to less coherent estimates of field genetic flowering time if seasonal differences lead to meaningful differences in flowering time, and also broken down further into spring (4 treatments over 2 experiments), summer (4 treatments, 1 experiment), and fall plantings (23 treatments, 9 experiments). To control for regional bias, this aggregation was performed both for all experiments and without experiments that tested only ecotypes collected from a single country.

Data collection approaches for estimating germination rate and dormancy were more heterogeneous than for flowering time. Depending on the experiment, dormancy was reported as the number of days after planting until a set percentage of germination was reached, the percentage of seed germinated a given number of days after planting, the number of days of storage until a set percentage of germination, or the germination rate after a given number of days of storage. Because of the difference in metrics across experiments, dormancy values were standardized by rank within each experiment with zero indicating low to no dormancy and one indicating high dormancy. Measures that reported a percentage germinated were ranked in the opposite direction from measures that reported number of days until germination or the number of days of storage before 50% germination. It is reasonable to expect some relationship between different measures of dormancy (Ranal and De Santana 2006) and, indeed, we found that standardized rank-based metrics that increase with dormancy (such as days to 50% germination) were correlated with rank based on metrics that decrease with dormancy (such as percentage of seeds germinated after a set number of days, Pearson’s r = 0.485).

These experiments captured variation in primary dormancy, or recalcitrance to germinate immediately after harvest. Secondary dormancy, or dormancy induced when a seed experiences conditions unfavorable to germination, has been studied in experiments that measured an increase in dormancy during storage. Primary and secondary dormancy could lead to different phenological and ecological outcomes (Martinez-Berdeja et al. 2020), so we averaged across experiments that measured primary or secondary dormancy separately to estimate each of these two traits. Secondary dormancy was not included in the Generalized Additive Models described below.

Wild phenology

The date of collection (for ecotypes maintained by ABRC) or collection date (for ecotypes grown from a known herbarium record) was used as an estimate of reproductive phenology of individuals in the wild (Primack et al. 2004; Davis et al. 2015). We excluded records from regions where Arabidopsis has been recently introduced, like North America and Japan. Accessions from outside the native range and or collected after the 320th day of the year (which we deemed likely errors based on location, sometimes due to intentional plantings, and were greater than 2 standard deviations from the mean) were removed. Our geographic limits also excluded island accessions to the south of the Mediterranean, e.g. Cape Verde Island.

We hypothesized that genetic differences in wild phenology may be more apparent if we account for spatiotemporal environmental fluctuations causing plasticity. Therefore, we calculated photothermal units (PTUs) for each accession from the day of collection using monthly climate time series data from CRU (Harris et al. 2014), beginning with January 1 of each year, following the methods of (DeLeo et al. 2020) and (Burghardt et al. 2015). PTUs integrate the temperature and light experienced by a plant at a given location through the growing season and therefore may capture environmental cues relevant to phenology and better describe genetic variation in development (Wilczek et al. 2009; Brachi et al. 2010). The models described below use the square root of PTU because the resulting distribution was closer to normal than the log transformation.

Statistical comparison of collection date in the wild versus phenology in experiments

We tested if genetic variation in normalized phenology measured on naturally inbred lines explained variation in the wild phenology of the parent of the line (Figure 1 “Predictions”), using linear regression between the normalized phenology breeding values (flowering time and dormancy rank) and collection day. Vernalized, non-vernalized, and field flowering times were modelled separately, because the genetic variation in phenology uncovered by each treatment could relate to wild phenology in different ways. Under the hypothesis that vernalization and field conditions better recreate environments a plant would expect at their geographic origin, these genetic flowering time measures should be more positively related to wild flowering time. Likewise, non-vernalized flowering times may better recreate the original temporal niches of summer annuals and thus be positively related to flowering time in these ecotypes. However, it is also possible that long non-vernalized flowering times indicate obligate winter annuals. In these plants, non-vernalized flowering times would be negatively related to wild phenology since later flowering times in non-vernalized experiments would indicate plants that overwinter and are collected early the next year (Figure 1). To account for plasticity in phenology due to differences among locations in the timing and progression of growing seasons, we also tested the relationship between phenology breeding values and the estimated PTUs at the time of collection from the wild, using linear regression.

We also implemented a set of models that allowed for geographic variation in the slope of the relationship between wild and genetic variation in phenology. The hypotheses we described above (Figure 1) may be true to different degrees in different populations, due to geographic variation in GxE that, combined with microsite environmental variation, could obscure genetic effects on phenology. Generalized Additive Models (GAMs) allow for regression parameters to vary smoothly across space and thus can capture spatially varying relationships (Yee and Mackenzie 2002; Yee and Mitchell 2006). We fit GAMs using the ‘gam’ function in the ‘mgcv’ package in R (Wood 2006) using restricted maximum likelihood (REML), although Generalized Cross Validation returned similar estimates. Model fitting allowed for penalization of smooth terms to 0 so that uninformative covariates could be removed from the equation. Residuals were plotted using the ‘gam.check’ function in ‘mgcv’ (Wood 2006), and one ecotype (Nok-10) was removed from our analyses that was an extreme outlier based on its residuals.

We also investigated how range-wide differences in wild phenology related to regional differences in breeding values using a broader collection of ecotypes than just those with documented collection dates. This comparison included stock center ecotypes with experimental phenology data and known location-of-origin, but no recorded date of wild collection, and herbarium specimens for which we had not germinated seed and grown plants to measure traits in experiments. To do so, we first estimated wild flowering time for each stock center ecotype in experiments using herbarium records near the ecotype collection location. These wild flowering times were estimated from a previously published GAM with spatially-varying intercepts of herbaria collection dates from 2,655 Eurasian Arabidopsis records used in (DeLeo et al. 2020), which included year of collection as a nuisance variable, as collection date has changed over time across the range of Arabidopsis (DeLeo et al. 2020). Values of the smooth intercept surface were extracted at the coordinates of stock center ecotypes having an experimentally measured flowering time. These estimated wild flowering times were regressed against breeding values for flowering times under vernalized, non-vernalized, and field experiments and rank primary dormancy. In addition, for these same data we performed Spearman rank correlation between estimated wild flowering times and phenology breeding values.

Next, we tested for the effects of genetic variation in flowering time and dormancy on phenological variation within populations in the wild. By first accounting for geographic variation in mean flowering time, we aimed to isolate within-population variation (Figure S1). This analysis was in essence asking whether genetic variation explains natural phenological variation within populations. We built a GAM for the dependent variable of collection date which included covariates of experimental flowering time, dormancy, and a spatially varying intercept: Equation 1. Yij=μj+β1FloweringTimeij+β2PrimaryDormancyij+β3Elevationj+εij

The spatially varying intercept term μj smoothed across latitude and longitude estimates spatial variation in mean collection date (due to e.g. environmental gradients). The upper limit on degrees of freedom for the spatially varying intercept was increased to 45 following the recommendations of Wood (2006). In models that allowed for higher degrees of freedom, the effective degrees of freedom did not meaningfully increase. Elevation was included because of its known importance to flowering times and spring onset (Vidigal et al. 2016; Gamba et al. 2023) and the high resolution of elevation data compared to smooth variation in GAM parameter surfaces. Spatial variation in coefficients for flowering time and dormancy covariates (Eq 1) were not significant, so a simpler model using a constant coefficient was used in our analyses. Field, vernalized, and non-vernalized flowering times were tested in separate versions of the model and tested different hypotheses with regards to wild phenology. To compare among the three measures of flowering time, a version of Equation 1 was fit on a subset of ecotypes that had all three flowering time measures and Akaike’s Information Criterion (Akaike 1974) was compared.

In the wild, both flowering time and dormancy contribute to phenology. Thus, we also tested whether including interactions between flowering time and primary dormancy improved the model: Equation 2. Yij=μj+β1FloweringTimeij*β2PrimaryDormancyij+β3FloweringTimeij*PrimaryDormancyij+β4Elevationj+εij

Finally, given the importance of plasticity in response to temperature in the timing of germination and flowering time, PTU might better capture genetic variation in phenology in the wild (Figure 1 “Predictions”). Therefore, we also tested the models above using PTU in place of collection day to correct for climate.

Plasticity due to maternal effects

Maternal conditions may be an important source of phenological plasticity; thus we used our dormancy experiment to examine how variation in flowering time among maternal plants influences germination. For each ecotype and for each maternal replicate, we used R packages ‘drc’ (Ritz et al. 2015) and ‘drcSeedGerm’ (Onofri et al. 2018) to fit a logistic function to germination counts to estimate three parameters: maximum germination proportion, time to 50% germination, and slope of the germination curve. A Generalized Linear Mixed Model was fit using the lmer package in R (Bates et al. 2015) to estimate the influence of relative flowering time of maternal replicates of each ecotype on germination traits. Because plasticity or responsiveness to environmental cues may confer greater fitness in some environments and not others (Alpert and Simms 2002; Baythavong 2011), germination traits and variation were regressed against maternal flowering time and location of origin. We performed hierarchical clustering of a distance matrix of phenotypic variation among ecotypes to group ecotypes that reacted similarly to different stratification treatments.

Results

Genetic correlation among phenology traits in controlled experiments

Across all compiled experiments (both previously published and our new experiments) mean flowering times of ecotypes measured in vernalized and non-vernalized experiments were strongly positively correlated (Pearson’s r = 0.90, p < 0.001). Although fall-sown field trials might be expected to expose plants to cold seasonal temperatures that give vernalization cues, standardized flowering time breeding values in both non-vernalized and vernalized experiments were similarly correlated to published field experiment values (rnon-vernalized = 0.77, rvernalized = 0.72, p < 0.001 for both). Primary and secondary dormancy were negatively correlated, but not significantly so (r = −0.15, p = 0.11). Despite known interactions between flowering time and dormancy due to seed maturation environment and pleiotropy of causal loci, only non-vernalized flowering time was modestly correlated with primary dormancy (r = −0.17, p = 0.02, Figure 2). For a heatplot of all phenotypes, see Figure S2.

Comparing wild phenology with breeding values in experiments

Experimental breeding values for phenology traits were modest predictors of collection day in simple linear models, with the only significant predictors being non-vernalized flowering time (estimated slope = 27.7 days/standardized flowering time, r2 = 0.09, p < 0.001) and primary dormancy (−17.1 days/standardized rank dormancy, r2 = 0.02, p = 0.02). Flowering time breeding values under vernalized or field conditions did not significantly predict day of collection with a linear model (p > 0.05) (Figure 3). Flowering time and germination measured within individual experiments did not predict wild collection day better than our averaged breeding values, suggesting some power was gained by combining individual experiments into standardized values (Figure S3).

Because environmental differences among locations can influence phenology (Fournier-Level et al. 2013), we also calculated PTU at collection and compared to traits from experiments. However, PTU at collection was not significantly predicted by any phenology traits (Figure 3). Thus, while ecotypes with later flowering time breeding values tend to be collected later in the year, these later flowering ecotypes are not collected at higher PTU, i.e. later in local growing seasons. The fact that phenology breeding values are correlated with date, but not PTU at collection, is consistent with a hypothesis that geographic climate variation maintains clines in breeding values due to local adaptation and clines in collection date due to plasticity (co-gradient variation), generating a spurious breeding value-collection date correlation. That PTUs, which account for variation in seasonality among site, show no relationship with breeding values suggests a weak role for genetic variation in phenology in nature.

Flowering time and dormancy breeding values predict regional variation in collection date

Our model of collection date above was limited to the 227 ecotypes with an available collection date and experimental phenology data. To expand our predictions across more of Arabidopsis’ range, we tested whether genetic variation in flowering time in experiments predicts local mean collection dates of herbarium specimens across the landscape. First, we found that local mean collection dates across a landscape from >2500 herbarium collections were positively correlated with actual collection days of individual stock center ecotypes (which were not used in fitting the model, ρ = 0.51, p < 0.001 Figure S4), validating these local predictions of phenology in the wild based on herbarium collections.

As with the collection dates of the original maternal sources of stock center lines, breeding values for non-vernalized flowering times weakly but significantly predicted local mean herbarium collection dates (r2 = 0.10, p < 0.001). For comparison, latitude was a better predictor of mean herbarium collection date (r2 = 0.37, p < 0.001). These patterns were similar for vernalized, non-vernalized, and field measurements, although not significant for field trials when fall plantings were grouped with spring and summer plantings (r2 = 0.07, p = 0.002, r2 = 0.10, p < 0.001, and r2 = 0.02, p = 0.13, respectively). These results show that range-wide genetic variation in phenology measured in controlled environments corresponds to actual phenology of plants in nature. However, because plants in nature from different regions experience different environments these patterns cannot determine the degree to which genetic variation causes the variation in natural phenology as opposed to genetic variation being confounded with environmental variation that causes phenological plasticity in nature (Figure 1).

Variation in phenology breeding values does not predict local, within-region variation in wild collection dates

To reduce the influence of genotype-environment confounding among regions in our analyses, we next examined whether genetic values can explain variation in natural phenology within regions (Figure S1). We used GAMs that included spatially varying intercepts and an elevation covariate to account for the among population variation in environmental effects on collection dates, leaving local variation to be potentially explained by genetic variation. However, we found that normalized flowering time (slope of non-vernalized flowering time = −8.2, p = 0.098) and dormancy (slope of dormancy = 2.2, p = 0.51) were not significantly related to day of collection in this GAM (Equation 1), although elevation effects were significant (estimated slope of elevation = 0.012, p = 0.049, Table S1). In comparing models using vernalized, non-vernalized, and field experiments, we found that standardized non-vernalized flowering times led to lower AIC compared to other flowering time measures but higher AIC than a model without flowering time at all, although the difference was slight (difference < 2 for an AIC of 530). We used non-vernalized flowering times in the final models, in part because there were more ecotypes with non-vernalized flowering times (184 for non-vernalized vs 113 for vernalized or 78 for field).

Although we found that flowering time, dormancy, and an interaction between the two were not significantly related to collection day, including phenology breeding values did lower the AIC and slightly increase the deviance explained by the model. This suggests that dormancy and flowering time could be related to variation in phenology within regions. However, most of the variation was described by the spatially varying intercept –a model with no more than a spatially varying intercept had a deviance explained of 80.1% – which could account for a large plastic response to geographic climate gradients (Figure S4).

As with the linear models, we also tested whether genetic variation in phenology in experiments could explain local variation PTU of collection date in nature. Again, we found that flowering time and dormancy were not significantly related to wild PTU at collection in GAMs with spatially varying intercepts. Elevation was negatively related to PTU (slope: −0.018 √PTU/m, p < 0.01), although it was positively related to day of collection (slope: 0.012 days/m, p < 0.05).

The role of maternal effects

Finally, when we looked at phenological variation within genetic backgrounds due to different maternal replicate plants, we found that some ecotypes had more consistent phenology across maternal lines. For example, DAM1 maternal replicates clustered together in germination across cold treatments, whereas Yeg-7 replicates did not (Figure 6A). Cold stratification altered expression of variance among maternal sources. In Yeg-7, for example, maternal lines P vs A had similar total germination following 3 days or 2 weeks of stratification but different germination for 0 days stratification. Thus, stratification (or lack of stratification) could contribute to diverse phenology for similar genetic backgrounds under natural conditions. The first and second principal components in an analysis of flowering and germination traits explained 24.55% and 17.85% of the variation respectively, but again maternal replicates of ecotypes did not always cluster in PC space (Figure 6B). The variance in phenology for an ecotype was not related to collection date or geographic origin (latitude and longitude, Figure 6C), suggesting that clinal variation in maternal plasticity effects do not confound our earlier large-scale analyses comparing breeding values with phenology in nature. Nevertheless, these effects may further obscure genetic contributions to phenology in nature.

Discussion

Plant phenology comprises multiple traits that contribute to fitness. In Arabidopsis, flowering time and germination can vary independently to create a landscape of possible life histories across environments (Debieu et al. 2013; Marcer et al. 2018; Martínez-Berdeja et al. 2020). This variation in phenology is likely partly maintained by selection, given that the traits vary geographically in association with climate (Fournier-Level et al. 2011; Vidigal et al. 2016; Exposito-Alonso 2020) and biotic pressures (Lyons et al. 2015; Davila Olivas et al. 2017), QTL show evidence of local adaptation (Gamba et al. 2023; Lasky et al. 2024), and phenology has been correlated with fitness (Korves et al. 2007; Stock et al. 2015). Given the evidence that genetic variation in life history may be adaptive, we investigated to what degree experimentally measured genetic variation in Arabidopsis phenology predicts phenology of plants in the wild, based on collection dates of natural history records.

The influence of genotype and environment on flowering time and dormancy have been well studied experimentally in Arabidopsis. Yet, common garden and controlled environment experiments must be designed thoughtfully to highlight genetic differences between ecotypes that are relevant to selection in natural environments (Karrenberg and Widmer 2008). While experimental design has increasingly recognized the importance of field conditions to acquire a measure of phenology that is more representative of natural environments (Wilczek et al. 2009; Brachi et al. 2013; Poorter et al. 2016), there has been little comparison of controlled experiments, field or otherwise, to phenology in wild, naturally cycling Arabidopsis individuals. We found that flowering time and dormancy breeding values are weakly, but statistically significantly, related to date of collection and capture variation in phenology but primarily among populations in the wild, explaining little within populations. However, much of this large-scale relationship is likely due to spurious genetic correlations with environment, which also drives plasticity in the wild.

Genetic variation in flowering time and dormancy weakly predict variation in wild phenology among populations

Across the species range, both primary dormancy and non-vernalized flowering time significantly, but weakly, predicted collection dates. The predictive power of phenology breeding values was not improved by substituting collection day for PTU at collection, suggesting that temperature and photoperiod variation among locations (captured by PTU) explains a large portion of the geographic variation in collection date, which would explain why genetically later flowering ecotypes were not necessarily collected at higher PTU. Spring onset differs among locations, and so one calendar day at a higher latitude or elevation may be earlier in the season than at a lower latitude or elevation. By recording temperature and daylight above a threshold, PTUs represent how much of a growing season has passed by a given date. Thus, plants collected at lower PTUs may be early flowering (developmentally) despite a later collection date. Higher PTU may indicate plants growing as summer or fall annuals that germinated later in the year. In that case, ecotypes with higher PTUs would likely be fast cycling, earlier flowering plants. While we did find a negative correlation between flowering time breeding values in field and vernalized experiments versus PTU, we see the opposite in non-vernalized experiments. This last observation is somewhat surprising, since we expected plants that flower later in the absence of vernalization to grow as winter annuals in the wild and flower early in the spring (Figure 1). Higher PTUs in these presumptive winter annuals may suggest that plants in these locations regulate their life histories to flower later in the year than expected by temperature and daylight alone. There is some evidence that later flowering ecotypes could in fact be more flexible in their flowering time relative to germination than early flowering ecotypes (Miryeganeh et al. 2018), which could hide the signal of early spring flowering we expected in late flowering Arabidopsis.

Both dormancy and flowering time were weakly, but significantly, related to date of collection across the species range, but phenological transitions are not independent of each other. As seen in individual experiments (Martínez-Berdeja et al. 2020), we found that the average breeding values for primary dormancy and non-vernalized flowering time were negatively correlated (Figure 2). Furthermore, dormancy is strongly affected by environmental conditions during seed maturation (Penfield and Springthorpe 2012; Huang et al. 2015; Burghardt et al. 2016). Thus, interaction between flowering time and dormancy in the wild could stem from both genetic pleiotropy and environmentally induced interactions. Despite our expectation for interactions between germination and flowering traits, models of collection day that included an interaction between flowering time and dormancy had a higher AIC and did not explain more of the deviance than a model with both traits separately. Including interactions between flowering time and dormancy in our models did not help to explain collection date in the wild.

When we expanded our original set of observations of wild phenology by predicting wild flowering times from a smoothed surface of collection dates fit to herbarium records, we found that predicted wild collection date was positively related to flowering time breeding values. In our linear models, average flowering time breeding values was more closely related to this estimated day of collection than was the actual date of collection of ecotypes in experiments. However, a large portion of phenological variation within populations remained unexplained even with phenological breeding values. We attempted to avoid records that were unusually young or old by only using specimens that had both flowers and fruits, and both herbarium and seed collections spanned nearly the entire year (days 5–350 for herbarium records, 43–346 for seed collections). Still, herbarium records are known to skew slightly towards earlier in seasons (Daru et al. 2018), while seed collections must be collected long enough after the initiation of flowering to allow for the development of some mature seeds. A single observation during reproduction, common for natural history collection vouchers, cannot resolve the uncertainty around when plants begin their vegetative growth in the wild, making it difficult to describe phenology of the full life cycle. Nevertheless, maintained collections could perhaps provide a useful counterpoint observation to fine tune models of landscape phenology.

Within populations, breeding values do not predict phenology in the wild

Because Arabidopsis exhibits substantial local within-population variation in phenology (Brachi et al. 2013; Alonso-Blanco et al. 2016; DeLeo et al. 2020), we asked how genetic variation in phenology was related to phenological variation within populations. This within-population phenological variation has fitness consequences. Variation contributes to population persistence in variable environments, as when different germination behavior provides bet hedging in the seedbank (Cohen 1967; Gremer and Venable 2014). Individuals at the tails of the distribution could also play an important role in overall population fitness by maintaining potentially adaptive variation (Jump et al. 2009).

We found that neither dormancy nor flowering time breeding values from experiments were significantly related to collection date when we accounted for geographic variation in local mean collections dates in a GAM. However, flowering time and dormancy did improve GAMs for collection date over a model that included only geographic location and elevation, suggesting that genetic variation in flowering time does explain a minor fraction of the phenological variation within regions.

In our models, there was very little difference between non-vernalized, vernalized, and field-measured flowering times in predicting collection dates, despite the expectation that field experiments recreate conditions similar to nature. Data from field experimental trials were available for a smaller set of ecotypes than non-vernalized and vernalized indoor trials, and our model lacked Iberian ecotypes with field experimental data and date of collection, limiting the usefulness of this measure across the range. Our model provided weak evidence that the breeding values for flowering time and dormancy explained within-population variation in wild collection day. However, our set of ecotypes with known collection day was 258, potentially still too few to detect strong statistical significance given the noise arising from plasticity in responses to local environmental gradients.

Plasticity and maternal effects

Finally, we examined the role of plasticity and within-ecotype diversity in the expression of phenological traits to account for the range of phenological variation we observe in the wild. By comparing the stratification response of seeds from maternal replicates within ecotypes, we aimed to describe the variance in expression of germination traits within a genetic background. Hierarchical clustering of germination traits across 100 ecotypes under three stratification treatments showed that for some genetic backgrounds, seeds from different maternal sources had little variance in germination traits (speed, total germination, ds50) and clustered together. Other ecotypes had more diversity in phenology expression among maternal lines.

Variation in phenology may not be adaptive in regions where there is harsh seasonality or environmental conditions are less variable year to year (Cohen 1967). Similarly, observations of phenology in the wild could be temporally variable because of year-to-year environmental differences (Walker et al. 1995; Hu et al. 2017; Postma and Ågren 2018) or genetically diverse individuals germinating from the seedbank (Ratcliffe 1976). Among replicate individuals in the same controlled experiment, variation in phenology is likely due to responses to very subtle environmental differences and some developmental stochasticity. We expected to see more such variation among ecotypes from regions with favorable growing conditions year-round. However, we did not find a geographic association between variance in germination traits and flowering time among replicate individuals of an ecotype. Instead, there may be geographically consistent selection on the canalization of phenology.

Conclusion

We demonstrated that experimental measures of genetic phenology in Arabidopsis explain little phenological variation in wild populations, suggesting low heritability or extensive rank changing genotype-environment interactions across microsites. Differences in Arabidopsis phenology across the species range are often cited as evidence of the adaptive importance of phenology (Stinchcombe et al. 2004; Fournier-Level et al. 2011; Samis et al. 2012; Brachi et al. 2013; Exposito-Alonso 2020). Incorporating information on between-population and within-population phenology diversity helps to clarify how selection may be acting on phenology across the landscape and how influential plasticity is in the phenology of this annual herb. We found that flowering time and dormancy weakly predict wild flowering time, and the two together do an incomplete job of predicting wild flowering time even when photoperiod and temperature are accounted for. Yet, genetic flowering times among populations across the species range are associated with phenological variation in natural history collections, apparently due to confounding between genotype and environment (Jones et al. 2024). While phenological plasticity is often of large magnitude, it may not be enough to maintain fitness under environmental change (Zettlemoyer et al. 2024). Ultimately, controlled experiments on many plants have suggested phenology is under selection in nature, but understanding more subtle environmental differences and stochasticity may help to clarify the evolution of phenology and translate genetic values into reliable predictions with and between populations.

Supplementary Material

Supplement 1

Acknowledgements

We thank the herbaria that allowed sampling of seed from specimens for generation of natural inbred lines used in experiments: Real Jardín Botánico de Madrid, Oslo Natural History Museum, and Komarov Botanical Institute. We are grateful for assistance with experiments from P. Patel, C. Yim, J. Kizer, T. Xia, V. Meagher, C. Lorts, K. Turner, and E. Bellis and for helpful discussions with L. Burghardt. Funding was provided by NIH award R35 GM138300 to JRL.

Data Accessibility Statement

Original data will be included as supplemental tables. Prior studies from which phenology data was sourced will be listed in a supplemental table.

Figure 1: Genotype and environment likely influence phenology of individual Arabidopsis plants in the wild. Genetics and environmental cues may determine how long a plant remains in vegetative growth (green) or how long a plant remains dormant as a seed (brown circles), with three representative life cycles shown from Scandinavia to the Mediterranean (A-C), with shaded areas showing accumulation of photothermal units (PTUs) in the growing season (e.g. temperature >4°C). Existing knowledge of clines in flowering time and germination combined with plastic acceleration of flowering in warmer temperatures (D-F) lead to our predictions of how flowering time and base germination rates in controlled experiments will correspond to phenology in natural wild plants (G-J).

Figure 2: Standardized phenology traits combined across multiple published controlled experiments combined with our new experiments. Standardized flowering times were correlated across treatments (vernalized, non-vernalized, field), but they were mostly unrelated to dormancy.

Figure 3: Date of collection (left panels) and PTUs (photothermal units) at collection (right panels) of wild plants compared to standardized breeding values for phenological traits from experiments. Collection day of wild plants was positively related to non-vernalized flowering time in experiments. Collection day was negatively related to primary dormancy. The values shown for field flowering time experiments were calculated from fall-sown experiments only.

Figure 4: Genetic variation in flowering time experiments predicts collection dates of nearby herbarium specimens. A) There is geographic variation in collection dates as shown by estimated mean date from GAMs with spatially varying means, with earlier collections (light green) around the Mediterranean (DeLeo et al. 2020). B) Local mean collection dates were positively correlated with genetic variation in vernalized flowering time experiments (ρ = 0.29, p < 0.01), and all ecotypes from locations with earlier mean collection dates in the wild (largely Mediterranean) had rapid flowering breeding values. Primary dormancy breeding values were negatively correlated with local mean collection dates (ρ = −0.30, p < 0.001).

Figure 5: Top: Spatial variation in day of collection for 227 ecotypes with known collection dates. A) Color represents the intercept value for collection day at a location after fitting a linear (i.e. non-spatially varying) relationship with elevation, experimental primary dormancy and non-vernalized flowering time. Much of the variation in collection day was explained by the spatially varying intercept rather than being explained by experimental flowering time. B) Residuals of GAM predicting collection day were not related to non-vernalized flowering time or dormancy, suggesting that genetic phenology values in experiments do not explain the local variation in phenology in nature.

Figure 6: Variation in experimental phenology among maternal lines. A) Early flowering lines tended to have lower germination under 0 days stratification relative to 3 days or 2 weeks stratification, and later-flowering lines tended to have longer times to 50% germination. However, there was variation across traits. Replicate maternal lines of the same ecotype did not always cluster. B) Maternal lines of the same ecotype did not always cluster together in PCA of phenology traits. Lines belonging to the same ecotype are represented with the same color, although colors are not unique across ecotypes. PC1 was positively associated with time to 50% germination at 0 days and 3 days stratification and flowering time and negatively associated with time to 50% germination at 2 weeks stratification and max germination under all treatments. C. Flowering time, collection day, or geographic origin did not explain variance among maternal lines.
==== Refs
References

Abley K. , Formosa-Jordan P. , Tavares H. , Chan E. , Leyser O. , and Locke J. C. W. . 2020. An ABA-GA bistable switch can account for natural variation in the variability of Arabidopsis seed germination time. bioRxiv, doi: 10.1101/2020.06.05.135681.
Ågren J. , Oakley C. G. , Lundemo S. , and Schemske D. W. . 2017. Adaptive divergence in flowering time among natural populations of Arabidopsis thaliana: Estimates of selection and QTL mapping. Evolution (N Y) 71 :550–564.
Akaike H. 1974. A New Look at the Statistical Model Identification. IEEE Trans Automat Contr 19 :716–723.
Alonso-Blanco C. , Andrade J. , Becker C. , Bemm F. , Bergelson J. , Borgwardt K. M. M. , Cao J. , Chae E. , Dezwaan T. M. M. , Ding W. , Ecker J. R. R. , Exposito-Alonso M. , Farlow A. , Fitz J. , Gan X. , Grimm D. G. G. , Hancock A. M. M. , Henz S. R. R. , Holm S. , Horton M. , Jarsulic M. , Kerstetter R. A. A. , Korte A. , Korte P. , Lanz C. , Lee C. R. , Meng D. , Michael T. P. P. , Mott R. , Muliyati N. W. W. , Nägele T. , Nagler M. , Nizhynska V. , Nordborg M. , Novikova P. Y. Y. , Picó F. X. , Platzer A. , Rabanal F. A. A. , Rodriguez A. , Rowan B. A. A. , Salomé P. A. A. , Schmid K. J. J. , Schmitz R. J. J. , Seren Ü. , Sperone F. G. G. , Sudkamp M. , Svardal H. , Tanzer M. M. M. , Todd D. , Volchenboum S. L. L. , Wang C. , Wang G. , Wang X. , Weckwerth W. , Weigel D. , and Zhou X. . 2016. 1,135 genomes reveal the global pattern of polymorphism in Arabidopsis thaliana. Cell 166 :481–491.27293186
Alpert P. , and Simms E. L. . 2002. The relative advantages of plasticity and fixity in different environments: When is it good for a plant to adjust? Evol Ecol 16 :285–297.
Amasino R. 2004. Vernalization, competence, and the epigenetic memory of winter. Plant Cell, doi: 10.1105/tpc.104.161070.
Andrés F. , and Coupland G. . 2012. The genetic basis of flowering responses to seasonal cues.
Auge G. A. , Blair L. K. , Karediya A. , and Donohue K. . 2018. The autonomous flowering-time pathway pleiotropically regulates seed germination in Arabidopsis thaliana. Ann Bot 121 :183–191.29280995
Balasubramanian S. , Sureshkumar S. , Lempe J. , and Weigel D. . 2006. Potent induction of Arabidopsis thaliana flowering by elevated growth temperature. PLoS Genet 2 :0980–0989.
Baskin C. C. , and Baskin J. M. . 1988. Germination ecophysiology of herbaceous plant species in a temperate region. Am J Bot 75 :286–305.
Bates D. , Maechler M. , Bolker B. , and Walker S. . 2015. Fitting Linear Mixed-Effects Models Using lme4. J Stat Softw 67 :1–48.
Baythavong B. S. 2011. Linking the spatial scale of environmental variation and the evolution of phenotypic plasticity: Selection favors adaptive plasticity in fine-grained environments. American Naturalist 178 :75–87.
Boyd E. W. , Dorn L. A. , Weinig C. , and Schmitt J. . 2007. Maternal effects and germination timing mediate the expression of winter and spring annual life histories in Arabidopsis thaliana. Int J Plant Sci 168 :205–214.
Brachi B. , Faure N. , Horton M. , Flahauw E. , Vazquez A. , Nordborg M. , Bergelson J. , Cuguen J. , and Roux F. . 2010. Linkage and association mapping of Arabidopsis thaliana flowering time in nature. PLoS Genet, doi: 10.1371/journal.pgen.1000940.
Brachi B. , Villoutreix R. , Faure N. , Hautekèete N. , Piquot Y. , Pauwels M. , Roby D. , Cuguen J. , Bergelson J. , and Roux F. . 2013. Investigation of the geographical scale of adaptive phenological variation and its underlying genetics in Arabidopsis thaliana. Mol Ecol 22 :4222–4240.23875782
Burghardt L. T. , Edwards B. R. , and Donohue K. . 2016. Multiple paths to similar germination behavior in Arabidopsis thaliana. New Phytologist 209 :1301–1312.26452074
Burghardt L. T. , Metcalf C. J. E. , Wilczek A. M. , Schmitt J. , and Donohue K. . 2015. Modeling the influence of genetic and environmental variation on the expression of plant life cycles across landscapes. Am Nat 185 :212–227. University of Chicago Press Chicago, IL.25616140
Caicedo A. L. , Stinchcombe J. R. , Olsen K. M. , Schmitt J. , and Purugganan M. D. . 2004. Epistatic interaction between Arabidopsis FRI and FLC flowering time genes generates a latitudinal cline in a life history trait. Proc Natl Acad Sci U S A 101 :15670–15675.15505218
Chiang G. C. K. , Barua D. , Dittmar E. , Kramer E. M. , de Casas R. R. , and Donohue K. . 2013. Pleiotropy in the wild: The dormancy gene dog1 exerts cascading control on life cycles. Evolution (NY) 67 :883–893.
Chiang G. C. K. , Barua D. , Kramera E. M. , Amasino R. M. , and Donohue K. . 2009. Major flowering time gene, FLOWERING LOCUS C, regulates seed germination in Arabidopsis thaliana. Proc Natl Acad Sci USA 106 :11661–11666.19564609
Cohen D. 1967. Optimizing reproduction in a randomly varying environment when a correlation may exist between the conditions at the time a choice has to be made and the subsequent outcome. J Theor Biol 16 :1–14.6035758
Daru B. H. , Park D. S. , Primack R. B. , Willis C. G. , Barrington D. S. , Whitfeld T. J. S. , Seidler T. G. , Sweeney P. W. , Foster D. R. , Ellison A. M. , and Davis C. C. . 2018. Widespread sampling biases in herbaria revealed from large-scale digitization. New Phytologist 217 :939–955.29083043
Davila Olivas N. H. , Frago E. , Thoen M. P. M. , Kloth K. J. , Becker F. F. M. , van Loon J. J. A. , Gort G. , Keurentjes J. J. B. , van Heerwaarden J. , and Dicke M. . 2017. Natural variation in life history strategy of Arabidopsis thaliana determines stress responses to drought and insects of different feeding guilds. Mol Ecol 26 :2959–2977.28295823
Davis C. C. , Willis C. G. , Connolly B. , Kelly C. , and Ellison A. M. . 2015. Herbarium records are reliable sources of phenological change driven by climate and provide novel insights into species’ phenological cueing mechanisms. Am J Bot 102 :1599–1609.26451038
Debieu M. , Tang C. , Stich B. , Sikosek T. , Effgen S. , Josephs E. , Schmitt J. , Nordborg M. , Koornneef M. , and de Meaux J. . 2013. Co-Variation between Seed Dormancy, Growth Rate and Flowering Time Changes with Latitude in Arabidopsis thaliana. PLoS One, doi: 10.1371/journal.pone.0061075.
DeLeo V. L. , Menge D. N. L. , Hanks E. M. , Juenger T. E. , and Lasky J. R. . 2020. Effects of two centuries of global environmental variation on phenology and physiology of Arabidopsis thaliana. Glob Chang Biol 26 :523–538.31665819
Donohue K. 2005. Niche construction through phenological plasticity: Life history dynamics and ecological consequences.
Exposito-Alonso M. 2020. Seasonal timing adaptation across the geographic range of Arabidopsis thaliana. Proceedings of the National Academy of Sciences 117 :9665–9667.
Exposito-Alonso M. , Vasseur F. , Ding W. , Wang G. , Burbano H. A. , and Weigel D. . 2018. Genomic basis and evolutionary potential for extreme drought adaptation in Arabidopsis thaliana. Nat Ecol Evol, doi: 10.1038/s41559-017-0423-0.
Footitt S. , Huang Z. , Clay H. A. , Mead A. , and Finch-Savage W. E. . 2013. Temperature, light and nitrate sensing coordinate Arabidopsis seed dormancy cycling, resulting in winter and summer annual phenotypes. Plant Journal 74 :1003–1015.
Fournier-Level A. , Korte A. , Cooper M. D. , Nordborg M. , Schmitt J. , and Wilczek A. M. . 2011. A map of local adaptation in Arabidopsis thaliana. Science (1979), doi: 10.1126/science.1209271.
Fournier-Level A. , Wilczek A. M. , Cooper M. D. , Roe J. L. , Anderson J. , Eaton D. , Moyers B. T. , Petipas R. H. , Schaeffer R. N. , Pieper B. , Reymond M. , Koornneef M. , Welch S. M. , Remington D. L. , and Schmitt J. . 2013. Paths to selection on life history loci in different natural environments across the native range of Arabidopsis thaliana. Mol Ecol 22 :3552–3566.23506537
Franco M. , and Silvertown J. . 1996. Life history variation in plants: An exploration of the fast-slow continuum hypothesis. Philosophical Transactions of the Royal Society B: Biological Sciences 351 :1341–1348.
Gamba D. , Lorts C. , Haile A. , Sahay S. , Lopez L. , Xia T. , Kulesza E. , Elango D. , Kerby J. , Yifru M. , Bulafu C. E. , Wondimu T. , Glowacka K. , and Lasky J. R. . 2023. The genomics and physiology of abiotic stressors associated with global elevation gradients in Arabidopsis thaliana. bioRxiv preprint.
Gremer J. R. , and Venable D. L. . 2014. Bet hedging in desert winter annual plants: Optimal germination strategies in a variable environment. Ecol Lett 17 :380–387.24393387
Harris I. , Jones P. D. , Osborn T. J. , and Lister D. H. . 2014. Updated high-resolution grids of monthly climatic observations–the CRU TS3. 10 Dataset. International journal of climatology 34 :623–642. Wiley Online Library.
Hu J. , Lei L. , and De Meaux J. . 2017. Temporal fitness fluctuations in experimental Arabidopsis thaliana populations. PLoS One 12 .
Huang X. , Schmitt J. , Dorn L. , Griffith C. , Effgen S. , Takao S. , Koornneef M. , and Donohue K. . 2010. The earliest stages of adaptation in an experimental plant population: Strong selection on QTLS for seed dormancy. Mol Ecol 19 :1335–1351.20149097
Huang Z. , Footitt S. , Tang A. , and Finch-Savage W. E. . 2018. Predicted global warming scenarios impact on the mother plant to alter seed dormancy and germination behaviour in Arabidopsis. Plant Cell Environ 41 :187–197.29044545
Huang Z. , Ölçer-Footitt H. , Footitt S. , and Finch-Savage W. E. . 2015. Seed dormancy is a dynamic state: Variable responses to pre- and post-shedding environmental signals in seeds of contrasting Arabidopsis ecotypes. Seed Sci Res 25 :159–169.
Huo H. , Wei S. , and Bradford K. J. . 2016. DELAY of GERMINATION1 (DOG1) regulates both Seed dormancy and flowering time through microRNA pathways. Proc Natl Acad Sci U S A 113 :E2199–E2206.27035986
Jimenez-Gomez J. M. , Corwin J. A. , Joseph B. , Maloof J. N. , and Kliebenstein D. J. . 2011. Genomic analysis of QTLs and genes altering natural variation in stochastic noise. PLoS Genet 7 .
Johnston I. G. , and Bassel G. W. . 2018. Identification of a bet-hedging network motif generating noise in hormone concentrations and germination propensity in Arabidopsis. J R Soc Interface 15 .
Jones C. V , Regan C. E. , Firth J. A. , Cole E. F. , and Sheldon B. C. . 2024. Environmental similarity between relatives reduces heritability of reproductive timing in wild great tits. bioRxiv 2024.03.11.584392.
Juenger T. E. , Sen S. , Stowe K. A. , and Simms E. L. . 2005. Epistasis and genotype-environment interaction for quantitative trait loci affecting flowering time in Arabidopsis thaliana. Pp. 87–105 in Genetica.15881683
Jump A. S. , Marchant R. , and Peñuelas J. . 2009. Environmental change and the option value of genetic diversity.
Karrenberg S. , and Widmer A. . 2008. Ecologically relevant genetic variation from a non-Arabidopsis perspective.
Kenney A. M. , McKay J. K. , Richards J. H. , and Juenger T. E. . 2014. Direct and indirect selection on flowering time, water-use efficiency (WUE, δ13C), and WUE plasticity to drought in Arabidopsis thaliana. Ecol Evol 4 :4505–4521.25512847
Korves T. M. , Schmid K. J. , Caicedo A. L. , Mays C. , Stinchcombe J. R. , Purugganan M. D. , and Schmitt J. . 2007. Fitness Effects Associated with the Major Flowering Time Gene FRIGIDA in Arabidopsis thaliana in the Field. Am Nat, doi: 10.1086/513111.
Lasky J. R. , Takou M. , Gamba D. , and Keitt T. H. . 2024. Estimating scale-specific and localized spatial patterns in allele frequency. Genetics 227 :3, doi: 10.1093/genetics/iyae082.
Lawrence-Paul E. H. , and Lasky J. R. . 2024. Ontogenetic changes in ecophysiology are an understudied yet important component of plant adaptation. Am J Bot e16294 .
Lempe J. , Balasubramanian S. , Sureshkumar S. , Singh A. , Schmid M. , and Weigel D. . 2005. Diversity of flowering responses in wild Arabidopsis thaliana strains. PLoS Genet, doi: 10.1371/journal.pgen.0010006.
Li Y. , Huang Y. , Bergelson J. , Nordborg M. , and Borevitz J. O. . 2010. Association mapping of local climate-sensitive quantitative trait loci in Arabidopsis thaliana. Proc Natl Acad Sci USA 107 :21199–21204.21078970
Ludlow M. M. 1989. Strategies of response to water stress. Pp. 269–281 in Kreeb K. H. , Richter H. , and Hinckley T. M. , eds. Structural and functional responses to environmental stresses: water shortage. SPB Academic Publishers, Berlin.
Lyons R. , Rusu A. , Stiller J. , Powell J. , Manners J. M. , and Kazan K. . 2015. Investigating the association between flowering time and defense in the Arabidopsis thaliana-Fusarium oxysporum interaction. PLoS One, doi: 10.1371/journal.pone.0127699.
MacGillivray F. , Hudson I. L. , and Lowe A. J. . 2010. Herbarium collections and photographic images: Alternative data sources for phenological research. Pp. 425–461 in Phenological Research: Methods for Environmental and Climate Change Analysis. Springer Netherlands, Dordrecht.
Marcer A. , Vidigal D. S. , James P. M. A. , Fortin M. J. , Méndez-Vigo B. , Hilhorst H. W. M. , Bentsink L. , Alonso-Blanco C. , and Picó F. X. . 2018. Temperature fine-tunes Mediterranean Arabidopsis thaliana life-cycle phenology geographically. Plant Biol 20 :148–156.28241389
Martínez-Berdeja A. , Stitzer M. C. , Taylor M. A. , Okada M. , Ezcurra E. , Runcie D. E. , and Schmitt J. . 2020. Functional variants of DOG1 control seed chilling responses and variation in seasonal life-history strategies in Arabidopsis thaliana. Proc Natl Acad Sci USA 117 :2526–2534.31964817
Méndez-Vigo B. , Castilla A. R. , Gómez R. , Marcer A. , Alonso-Blanco C. , and Picó F. X. . 2022. Spatiotemporal dynamics of genetic variation at the quantitative and molecular levels within a natural Arabidopsis thaliana population. Journal of Ecology 110 .
Méndez-Vigo B. , Gomaa N. H. , Alonso-Blanco C. , and Xavier Picó F. . 2013. Among- and within-population variation in flowering time of Iberian Arabidopsis thaliana estimated in field and glasshouse conditions. New Phytologist 197 :1332–1343.23252608
Miller-Rushing A. J. , Primack R. B. , Primack D. , and Mukunda S. . 2006. Photographs and herbarium specimens as tools to document phenological changes in response to global warming. Am J Bot 93 :1667–1674.21642112
Miryeganeh M. , Yamaguchi M. , and Kudoh H. . 2018. Synchronisation of Arabidopsis flowering time and whole-plant senescence in seasonal environments. Sci Rep 8 .
Onofri A. , Benincasa P. ,  Mesgaran M. B. , and Ritz C. . 2018. Hydrothermal-time-to-event models for seed germination. European Journal of Agronomy 101 :129–139.
Penfield S. , and Springthorpe V. . 2012. Understanding chilling responses in Arabidopsis seeds and their contribution to life history. Philosophical Transactions of the Royal Society B: Biological Sciences 367 :291–297.
Poorter H. , Fiorani F. , Pieruschka R. , Wojciechowski T. , van der Putten W. H. , Kleyer M. , Schurr U. , and Postma J. . 2016. Pampered inside, pestered outside? Differences and similarities between plants growing in controlled conditions and in the field.
Postma F. M. , and Ågren J. . 2018. Among-year variation in selection during early life stages and the genetic basis of fitness in Arabidopsis thaliana. Mol Ecol 27 :2498–2511. Blackwell Publishing Ltd.29676059
Primack D. , Imbres C. , Primack R. B. , Miller-Rushing A. J. , and Del Tredici P. . 2004. Herbarium specimens demonstrate earlier flowering times in response to warming in Boston. Am J Bot 91 :1260–1264.21653483
Ranal M. A. , and De Santana D. G. . 2006. How and why to measure the germination process?
Ratcliffe D. 1976. Germination characteristics and their inter- and intra-population variability in Arabidopsis. Arabidopsis Information Service 34–45.
Reich P. B. 2014. The world-wide “fast-slow” plant economics spectrum: a traits manifesto. Journal of Ecology 102 :275–301.
Ritz C. , Baty F. , and Streibig J. C. . 2015. Dose-Response Analysis Using R. PLoS One 10 :e0146021.26717316
Salguero-Gómez R. , Jones O. R. , Jongejans E. , Blomberg S. P. , Hodgson D. J. , Mbeau-Ache C. , Zuidema P. A. , De Kroon H. , and Buckley Y. M. . 2016. Fast-slow continuum and reproductive strategies structure plant life-history variation worldwide. Proc Natl Acad Sci U S A 113 :230–235.26699477
Samis K. E. , Murren C. J. , Bossdorf O. , Donohue K. , Fenster C. B. , Malmberg R. L. , Purugganan M. D. , and Stinchcombe J. R. . 2012. Longitudinal trends in climate drive flowering time clines in North American Arabidopsis thaliana. Ecol Evol 2 :1162–1180.22833792
Simpson G. G. , and Dean C. . 2002. Arabidopsis, the Rosetta stone of flowering time?
Springthorpe V. , and Penfield S. . 2015. Flowering time and seed dormancy control use external coincidence to generate life history strategy. Elife 2015 .
Stearns S. C. 1989. Trade-Offs in Life-History Evolution. Funct Ecol 3 :259. JSTOR.
Stinchcombe J. R. , Weinig C. , Ungerer M. , Olsen K. M. , Mays C. , Halldorsdottir S. S. , Purugganan M. D. , and Schmitt J. . 2004. A latitudinal cline in flowering time in Arabidopsis thaliana modulated by the flowering time gene FRIGIDA. Proceedings of the National Academy of Sciences, doi: 10.1073/pnas.0306401101.
Stock A. J. , McGoey B. V. , and Stinchcombe J. R. . 2015. Water availability as an agent of selection in introduced populations of Arabidopsis thaliana: Impacts on flowering time evolution. PeerJ 2015 :1–16.
Thomas B. , and Vince-Prue D. . 1997. Photoperiodism in plants. 2nd ed. Academic Press, San Diego, CA, USA.
Vidigal D. S. , Marques A. C. S. S. , Willems L. A. J. , Buijs G. , Méndez-Vigo B. , Hilhorst H. W. M. , Bentsink L. , Picó F. X. , and Alonso-Blanco C. . 2016. Altitudinal and climatic associations of seed dormancy and flowering traits evidence adaptation of annual life cycle timing in Arabidopsis thaliana. Plant Cell Environ 39 :1737–1748.26991665
Walker M. D. , Ingersoll R. C. , and Webber P. J. . 1995. Effects of interannual climate variation on phenology and growth of two alpine forbs. Ecology 76 :1067–1083.
Wilczek A. M. , Burghardt L. T. , Cobb A. R. , Cooper M. D. , Welch S. M. , and Schmitt J. . 2010. Genetic and physiological bases for phenological responses to current and predicted climates. Philosophical Transactions of the Royal Society B: Biological Sciences 365 :3129–3147.
Wilczek A. M. , Roe J. L. , Knapp M. C. , Cooper M. D. , Lopez-Gallego C. , Martin L. J. , Muir C. D. , Sim S. , Walker A. , Anderson J. , Egan J. F. , Moyers B. T. , Petipas R. , Giakountis A. , Charbit E. , Coupland G. , Welch S. M. , and Schmitt J. . 2009. Effects of genetic perturbation on seasonal life history plasticity. Science (1979) 323 :930–934.
Wood S. N. 2006. Generalized additive models: an introduction with R. 1st ed. Chapman and Hall/CRC.
Yee T. W. , and Mackenzie M. . 2002. Vector generalized additive models in plant ecology. Ecol Modell, doi: 10.1016/S0304-3800(02)00192-8.
Yee T. W. , and Mitchell N. D. . 2006. Generalized additive models in plant ecology. Journal of Vegetation Science, doi: 10.2307/3236170.
Zettlemoyer M. , Conner R. , Seaver M. , Waddle E. , and DeMarche M. . 2024. A Long-Lived Alpine Perennial Advances Flowering under Warmer Conditions but Not Enough to Maintain Reproductive Success. Am Nat 203 .
Zhou D. , Wang T. , and Valentine I. . 2005. Phenotypic plasticity of life-history characters in response to different germination timing in two annual weeds. Canadian Journal of Botany 83 :28–36.
