Plants adjust when they grow, develop flowers and produce, or ‘set’, seeds in response to changes in temperature and day length. It is therefore unsurprising that climate change alters the timing of these important events in plants' lives; for example, many plants are adapting to rising temperatures by flowering earlier and growing for longer.

The environmental signals that control when a plant flowers, and the genes that underlie this process, have been well studied in the model plant Arabidopsis thaliana. This plant's ability to quickly colonize and thrive in disturbed habitats—including agricultural land, construction sites, and waste ground—is partly because some of its seeds lie dormant in the soil, for up to several years, before they start to grow. Whether or not a seed undergoes a period of dormancy is controlled by the temperature that the seeds experienced when they were developing; this in turn is influenced by earlier events, such as when the flowers first developed, and when the plant first started to grow from its seed (a process called germination).

To try to understand these complex interactions, Springthorpe and Penfield developed a computational model of the major events in the life of an Arabidopsis plant. Data collected from Arabidopsis plants that normally germinate in winter and spring were then used to check whether the model could accurately represent what happens in nature.

The analysis confirmed that the timing of seed setting depends mostly on the environmental temperature. Springthorpe and Penfield then showed that plants both flowered and set seed earlier in response to increases in temperature, so that the seeds were shed precisely when the temperature was between 14°C and 15°C.

Springthorpe and Penfield discovered that rise in the average temperature when a plant set seed from 14°C to 15°C had a dramatic effect on the seeds. Almost all of the seeds that developed below 14°C became dormant, while very few of the seeds that developed above 15°C became dormant.

From their findings, Springthorpe and Penfield predict that the temperature control of flowering time has evolved to constrain when seeds are set and ensure that plants produce a mixture of seeds: some that will become dormant, and some that will not. Their findings also show that modelling the whole life history of an organism has the potential to reveal strategies that are not obvious when studying single events in isolation. If the model was extended to include genetic variation across populations of plants, this approach could give new insights into how individual genes help plants adapt to weather and climate.

Study of climate effects on phenology have quantified shifts in the timing of developmental transitions such as flowering, bud burst, and migration. These show that warmer temperatures are advancing plant phenology, with bud burst and flowering occurring earlier and the growing season becoming longer (Menzel and Fabian, 1999; Fitter and Fitter, 2002; Cotton, 2003; Parmesan and Yohe, 2003; Cleland et al., 2007). This is an adaptive plant response to shifting temperatures, because for many species the flowering date is unaffected by climate change (Fitter and Fitter, 2002), and plants that couple their phenology to temperature are more likely to have populations resilient to climate change (Willis et al., 2008). Why many plants have evolved to behave in this manner is currently unclear.

The model plant Arabidopsis thaliana is widely distributed in the Northern hemisphere and can be used to analyze phenology, including control by individual genes (Wilczek et al., 2009; Chiang et al., 2013). In northern Europe, Arabidopsis exhibits a primarily winter annual life history, but in central and southern Europe, summer rapid cycling and summer annuals appear alongside winter annuals (Thompson, 1994). Importantly, A. thaliana is a ruderal species, primarily colonizing frequently disturbed habitats: the long-term persistence of such populations (and therefore fitness) is strongly dependent on seed behaviour and seed bank formation (Grime et al., 1981).

During the vegetative phase, plants have signalling pathways that couple progression to flowering to temperature and photoperiodic cues, and these pathways have been elucidated genetically under laboratory conditions (Andrés and Coupland, 2012). These same pathways are subject to positive selection in natural populations (Toomajian et al., 2006). However, when late flowering Arabidopsis mutants are analysed under field conditions they delay reproduction by only a few days, leaving the significance or their role unclear (Wilczek and et al., 2009; Chiang et al., 2013).

Temperature during seed set also strongly influences life history by modulating seed dormancy after shedding (Fenner, 1991; Schmuths et al., 2006; Chiang et al., 2009; Kendall et al., 2011), but in contrast to the control of flowering time comparatively little attention has been paid to the role of this process in life history generation and adaptation. Given that the environment during seed set is determined by flowering time, we hypothesized that understanding the role of either flowering time or dormancy control in population life history required a detailed integrated study of both traits. To address this problem, we therefore constructed and parameterized a field-validated model of Arabidopsis life history for the Columbia-0 (Col-0) accession.

Previously, photothermal models have been used to predict Arabidopsis flowering time under field conditions (Wilczek et al., 2009). In order to predict seed set conditions in the wild, we sought to extend this treatment to include the reproductive phase in order to determine the timing of seed set on plants germinated and grown at different times of the year. To parameterize this model, we first grew plants from bolting to first set seed under a range of temperatures under laboratory conditions (Figure 1A). We found a simple linear relationship between temperature and the number of days from bolting to first seed set, showing that seed set timing depended on ambient temperature. Our data suggested that seed set was much less dependent on photoperiod, with only photoperiods of 8 hr significantly delaying seed set (Figure 1—figure supplement 1). With these data, we constructed and optimized a thermal time model from first flowering to first seed set, and compared the predicted timing of seed set to that of plants setting seed under field conditions in York, UK in 2012 and 2013 (Figure 1B). The model was a good predictor of seed set timing in the field (R² = 0.43) for seed set in York in 2012 and 2013, and outperformed models that also included photoperiod components. By combining this with a previously published bolting time model (Wilczek et al., 2009), we could predict Col-0 first seed set date for any germination date.

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The precise location for the collection of Col-0 is uncertain but it is known to originate from central Europe (Robbelen, 1965; C Dean Personal communication). Simulated at the most likely collection site, Gorsow, Poland, a key feature of the model is that germination times from September to April (winter and spring annuals) lead to a very similar seed set time in late May (Figure 1C), a behaviour caused in part by growth rate and flowering time control (Wilczek and et al., 2009) but further enhanced by the seed set responses to temperature. Seed germinating from May to July set seed later in the same summer (rapid cycle), whereas August germinators have a wide range of possible flowering times (Wilczek and et al., 2009). To understand how this would vary in response to shifting climate norms, we simulated the flowering and seed set models using climate data across a transect covering 25° of latitude from Arkangel'sk, Russia, to Valencia, Spain (Figure 1D,E). Warmer climates advanced the seed set date, with 1°C rises in annual temperature giving about a 7-day advance in seed set time for winter annual cohorts from late June in Arkhangel'sk to early April in Valencia (Figure 1E). This behaviour has been widely observed in phenological studies, but our analysis also revealed a striking effect on temperature at first seed set: although mean annual temperature at the sites varied between 0.5°C and 17.5°C, winter annual seed set temperature was maintained at 14–15°C independent of latitude (Figure 1E). Therefore, a key emergent feature of the model is that the acceleration of flowering and seed set timing by warmer temperatures serves to stabilize the temperature range during which seed set begins.

Lower temperatures during seed production enhance seed dormancy in most angiosperm species (Fenner, 1991), but rarely has this response been characterized in detail. Laboratory experiments revealed that Col-0 dormancy appeared related to the mean daily temperature and was not clearly influenced by daily temperature cycles (Figure 2—figure supplement 1). To understand precisely how Col-0 seed dormancy responds to temperature during seed set, we matured seeds between 10°C and 20°C in the laboratory and germinated them following incubation treatments at temperatures between 4°C and 20°C (Figure 2). Our results revealed a clear bi-phasic behaviour: At or below 14°C, seeds produced are very dormant and do not germinate at high levels even after prolonged incubations under any temperatures. This is not due to low viability because seeds set at 10°C are viable at harvest (Kendall et al., 2011). In contrast, above 14°C, high levels of germination were possible especially where seeds were incubated at similar or lower temperatures than experienced during seed set. At these temperatures, seeds incubated in the dark experienced a transient period of primary dormancy loss during which time germination could occur if the seeds were given light, followed by secondary dormancy induction. This high sensitivity of dormancy to a 1°C increase in seed maturation temperature supports our previous observation of a general increase in the temperature-sensitivity of gene expression in developing seeds relative to vegetative tissues (Kendall et al., 2011).

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To allow simulation of the whole life history, we developed a predictive framework for Col-0 germination probability and constructed a germination model based on the following assumptions used previously for similar studies (Totterdell and Roberts, 1979; Bradford, 2005; Batlla et al., 2009): (1) germination is possible in the absence of dormancy; (2) primary dormancy depth (dormancy generated during seed maturation) is fixed at harvest and is lost during imbibition; (3) the onset of secondary dormancy (dormancy induced de novo after imbibition) begins independently during seed imbibition. Primary dormancy loss and secondary dormancy induction were represented as cumulative logistic functions (see ‘Materials and methods’). We noted that secondary dormancy appeared to be induced faster at warmer imbibition temperatures (Figure 2), and fixing this relationship in the model revealed that close matches to experimental data could be produced if initial primary dormancy was modelled as a function of the temperature during seed maturation (see ‘Materials and methods’ and Figure 2—figure supplements 2–4). In fact, a model with four parameters in which primary dormancy loss was dependent linearly on maturation temperature and secondary dormancy induction was dependent exponentially on imbibition temperature could be parameterised to provide a good fit to experimental data generated between 12 and 20°C (R² = 0.84; Figure 2—figure supplement 2) and could match germination data in an experimental series set at 16°C and not used to train the model (Figure 2—figure supplement 3). In order to determine whether our model could predict the germination of seed set under field conditions, we collected five seed batches during 2012 in a field site in York, UK and germinated them under multiple temperature regimes in the laboratory (Figure 2—figure supplement 4). Behaviour of these lots was qualitatively similar to that observed in the laboratory. We then fitted the parameterised model to each field germination data set, generating a prediction for the seed maturation temperature for each seed lot. This was then compared to the temperature history of each batch in the field (Figure 2—figure supplement 4). In each case the model-predicted temperature was close to the daily mean temperature in the last days before harvest for each seed batch, as has been shown previously in barley (Gualano and Benech-Arnold, 2009). Therefore, our model captures field-relevant relationships between maturation temperature and germination behaviour.

To understand the germination potential of seeds set at different times of the year, we ran the model for the locations in our transect (Figure 3). The germination model predicts behaviour with two principle states obvious from the data: Firstly, mean temperatures below 14°C during seed set strongly inhibit germination. Secondly, above 15°C the interaction between maturation temperature and imbibition temperature generally permits high germination rates. This is because faster gain of secondary dormancy after imbibition at warmer temperatures is offset by the lower level of primary dormancy induced during seed maturation. Warmer climates have a longer summer window of high germination until at very warm locations the model predicts germination inhibition at the height of summer, due to fast secondary dormancy induction. Our models show that the predicted transition from dormancy to germination takes place as the seed set temperature rises above 14°C, coinciding with the time of first seed set for winter annual cohorts, regardless of location (Figure 3). Therefore, simulated Col-0 phenology generates a coincidence of seed set timing with germination physiology which is independent of climate over 25° of latitude, demonstrating that this aspect of Col-0 phenology is strongly buffered against variation in temperature. Our model predicts that the low dormant progeny will enter a rapid cycle if the climate permits, flowering and setting seed later the same summer (Figure 3). This switch therefore has the potential to control the proportion of seeds entering the seed bank and those that emerge immediately for a rapid cycle generation for production of further seeds.

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Given the high sensitivity of Col-0 seed behaviour to temperature (Figure 2), we hypothesized that moderately delayed flowering could affect progeny life histories. To test this hypothesis, we ran the model using previously published parameter sets for simulating the gigantea (gi) and vernalisation insensitive 3 (vin3) (Sung and Amasino, 2004; Toomajian et al., 2006; Wilczek and et al., 2009) for Gorsow, Poland (Figure 4), although in principle our model behaviour is relevant to large areas of central Europe (Figure 1; Figure 3). GI confers a weak late flowering in field conditions of 7–10 days during summer while vin3-1 mutants generate a longer delay of 25–50 days (Sung and Amasino, 2004; Wilczek and et al., 2009). Thus, these are representative of any natural variants with mild and more severe delays in flowering affecting temperature or photoperiod sensitivity. This is more informative than considering strong FRI alleles because these do not substantially delay flowering of winter annuals (Wilczek and et al., 2009; Chiang et al., 2013). Both gi and vin3 mutants were predicted to set seed later in the year than wild type (Wilczek and et al., 2009), producing a greater proportion of seeds above the 14.5°C threshold and theoretically giving rise to more seeds capable of immediate germination. Given the time of year, these germinating seeds would be committed to a rapid cycling life history (Figure 1D). Assuming that seed set lasts for 1 month and follows a normal distribution, simulated across our transect the model predicts that gi mutations could double the number of low-dormant seeds in northern and central Europe but would have little effect at lower latitudes (Figure 4B). Mean temperature at shedding was raised from 14°C to 15°C for gi mutants at sensitive sites (Figure 4A,B). For vin3 the model predicts a rise in the mean temperature at maturation of almost 5°C, enough to commit most seeds to rapid cycling and severely curtail entry into the soil seed bank (Figure 4C). Our analysis therefore suggests that small variations in flowering time may have dramatic effects on progeny life history in Arabidopsis by affecting seed dormancy, providing evidence for a previously unrecognized mechanism through which flowering time can affect plant fitness. This conclusion is supported by a recent study that shows that changing flowering time affects seed dormancy under field conditions more than flowering time itself (Chiang et al., 2013).

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We have revealed that Arabidopsis Col-0 seed dormancy is remarkably sensitive to a 1°C rise in mean temperature during seed set from 14°C to 15°C (Figure 2). However, until now the significance of the response of primary dormancy to the temperature during seed maturation has not been understood. A central feature of Arabidopsis phenology is the ability to generate genetically identical cohorts of seeds with contrasting life histories, such as winter annuals and rapid cyclers (Montesinos-Navarro et al., 2012). Our data and models show that in a striking coincidence winter annual seeds are set when the mean temperature is approximately 14–15°C, the temperature at which a major switch from the production of dormant to non-dormant seeds occurs. This inevitably divides progeny into at least two distinct cohorts: obligate germinators which become summer rapid cyclers, and seeds which enter the soil seed bank even if initially exposed to light (Figure 5). Notably, a similar transition has been observed in Capsella bursa-pastoris seeds: in this case there is a transition in seed coat colour during seed set that alters dormancy and correlates with flowering time (Toorop et al., 2012). In Arabidopsis seed coat, pigmentation is affected by the temperature during seed set (MacGregor et al., 2015), again demonstrating control by the mother plant. Similar bet-hedging germination strategies have been described frequently across flowering plants suggesting wide general relevance (Childs et al., 2010), but further studies are required to understand whether this is a common strategy for Arabidopsis in locations where temperatures are warm enough to permit life history variation. In addition, our study only considers annual temperature and photoperiod variation and other factors, notably water availability, are also known to strongly affect germination and growth rates.

Figure 5

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Because seed bank loading is a key component of fitness in ruderal plant populations (Grime, 1988), control of progeny behaviour likely generates a major selection pressure on flowering time control. Genetic analyses of Arabidopsis fitness have frequently considered yield to be paramount, but seed behaviour and seed bank dynamics are central to long-term population persistence: in Norway the mean lifespan of Arabidopsis in the seed bank was estimated at between 1 and 8 years (Lundemo et al., 2009), but work to understand genetic contributions to seed bank dynamics is in its infancy.

Col-0 germination in autumn or spring gives a similar flowering date in May. This suggests that Col-0 can operate equally well as a summer or winter annual. This prediction is supported by field emergence data, which shows that seed set in the laboratory at 15°C or seed set in the field conditions germinate in the soil in both autumn and spring emergence windows (Figure 6). This bet-hedging strategy can therefore be relevant to both summer and winter annual accessions. Previously it has been shown that seeds of the Cape Verde Island accession germinate only in an autumn emergence window in central England (Footitt et al., 2011), whereas accession Bur-0 shows only a spring emergence window (Footitt et al., 2013). However, our work shows that winter and spring annual behaviour does not necessarily require different genotypes and are not mutually exclusive strategies. Both spring and autumn germination windows have also been described in coastal but not montane Spanish populations (Montesinos et al., 2009), suggesting that the strategy employed by Col-0 is of wide relevance in some ecological contexts.

Figure 6

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In the case of Col-0, winter annual behaviour appears to require (1) a strong slowing of growth rates under low temperatures, showing that growth rate can be as important as leaf number in determining timing of the floral transition in natural populations, and (2) strong tendency towards fast secondary dormancy induction combined with an autumn germination window. In contrast, strong FRI and FLC alleles and other late flowering alleles also confer flowering delays primarily on summer germinants (Wilczek and et al., 2009). This achieves winter annual behaviour with a second mechanism that permits early seed germination. The increased prevalence of strong FLC alleles at northern latitudes (Shindo et al., 2006; Li et al., 2014) is good evidence that an advantage of this latter strategy is maximisation of energy capture in short growing seasons through early seedling establishment, rather than generation of winter annual behaviour itself. At mid-latutides stronger FRI/FLC alleles have the potential to push back seed set of summer germinants into autumn (Wilczek and et al., 2009), and during autumn the temperature again passes back through the critical 14–15°C range. This may then stratify the behaviour of progeny seeds of these cohorts.

In addition to flowering time, latitudinal clines in seed dormancy have also been described, whereby accessions form more northerly latitudes exhibit lower primary dormancy levels (Atwell et al., 2010). Given that decreasing temperature during seed maturation strongly increases primary dormancy, our analysis suggests that these opposing genetic and environmental trends could offset each other in field conditions allowing maintenance of progeny behavioural strategies. Variation at the DELAY OF GERMINATION1 (DOG1) locus is a major determinant of natural variation in primary seed dormancy (Bentsink et al., 2006; Chiang et al., 2011) and temperature-regulation of DOG1 transcript levels during seed maturation (Chiang et al., 2011; Kendall et al., 2011; Chiang et al., 2013) may therefore play an important role in adaptation of seed dormancy to changing temperatures.

Recent articles also show that it is possible to model Arabidopsis populations over several generations using thermal time models (Donohue et al., 2014; Burghardt et al., 2015). The process reveals theoretical interactions between primary dormancy and flowering time that can recreate aspects of Arabidopsis phenology observed previously under field conditions at different latitudes, including the ability of Col-0 to adopt winter annual, summer annual, and rapid cycling life histories described here. Our analysis also shows that germination behaviour not included in these models such as temperature regulation of primary dormancy depth and secondary dormancy kinetics are necessary to understand life history control. Combining these two complimentary approaches has the potential to enable a new level of understanding of the diversity of life history strategy in annual plants and their genetic basis.

A further feature of Col-0 phenology is that the bet-hedging strategy is stable over latitude throughout Europe, except in the far north where temperature is too cool to generate germinating cohorts. It is also stable in respect to simulated warming (Figure 3—figure supplement 1), except in respect to time of year of seed set. This surprising lack of local adaptation could facilitate population stability in changing environments or enable rapid colonization of new territories, if timing of seed set was under no further constraints. Understanding the generality of this strategy is therefore of clear importance. Our work suggests that integration of life history modelling with behaviour of genetic variants has the potential to reveal fitness tradeoffs across the whole life history and identify non-obvious selection pressures as emergent features.

1. 1. Andrés F
   2. Coupland G

   (2012) The genetic basis of flowering responses to seasonal cues

   Nature Reviews Genetics 13:627–639.

   https://doi.org/10.1038/nrg3291

   • Google Scholar
2. 1. Atwell S
   2. Huang YS
   3. Vilhjálmsson BJ
   4. Willems G
   5. Horton M
   6. Li Y
   7. Meng D
   8. Platt A
   9. Tarone AM
   10. Hu TT
   11. Jiang R
   12. Muliyati NW
   13. Zhang X
   14. Amer MA
   15. Baxter I
   16. Brachi B
   17. Chory J
   18. Dean C
   19. Debieu M
   20. de Meaux J
   21. Ecker JR
   22. Faure N
   23. Kniskern JM
   24. Jones JD
   25. Michael T
   26. Nemri A
   27. Roux F
   28. Salt DE
   29. Tang C
   30. Todesco M
   31. Traw MB
   32. Weigel D
   33. Marjoram P
   34. Borevitz JO
   35. Bergelson J
   36. Nordborg M

   (2010) Genome-wide association study of 107 phenotypes in Arabidopsis thaliana inbred lines

   Nature 465:627–631.

   https://doi.org/10.1038/nature08800

   • Google Scholar
3. 1. Batlla D
   2. Grundy A
   3. Dent KC
   4. Clay HA
   5. Finch-Savage WE

   (2009) A quantitative analysis of temperature dependent dormancy changes in Polygonum aviculare seeds

   Weed Research 49:428–438.

   https://doi.org/10.1111/j.1365-3180.2009.00706.x

   • Google Scholar
4. 1. Bentsink L
   2. Jowett J
   3. Hanhart CJ
   4. Koornneef M

   (2006) Cloning of DOG1, a quantitative trait locus controlling seed dormancy in Arabidopsis

   Proceedings of the National Academy of Sciences of USA 103:17042–17047.

   https://doi.org/10.1073/pnas.0607877103

   • Google Scholar
5. 1. Borthwick HA
   2. Parker MW
   3. Heinze PH

   (1943) Effect of photoperiod and temperature on development of barley

   Botanical Gazette 103:326–341.

   https://doi.org/10.1086/335045

   • Google Scholar
6. 1. Bradford KJ

   (2005) Threshold models applied to seed germination ecology

   The New Phytologist 165:338–341.

   https://doi.org/10.1111/j.1469-8137.2004.01302.x

   • Google Scholar
7. 1. Burghardt LT
   2. Metcalf CJE
   3. Wilczek AM
   4. Schmitt J
   5. Donohue K

   (2015) Modeling the influence of genetic and environmental variation on the expression of plant life history cycles across landscapes

   The American Naturalist 185:212–227.

   https://doi.org/10.1086/679439

   • Google Scholar
8. 1. Chiang GC
   2. Bartsch M
   3. Barua D
   4. Nakabayashi K
   5. Debieu M
   6. Kronholm I
   7. Koornneef M
   8. Soppe WJ
   9. Donohue K
   10. De Meaux J

   (2011) DOG1 expression is predicted by the seed-maturation environment and contributes to geographical variation in germination in Arabidopsis thaliana

   Molecular Ecology 20:3336–3349.

   https://doi.org/10.1111/j.1365-294X.2011.05181.x

   • Google Scholar
9. 1. Chiang GC
   2. Barua D
   3. Dittmar E
   4. Kramer EM
   5. de Casas RR
   6. Donohue K

   (2013) Pleiotropy in the wild: the dormancy gene DOG1 exerts cascading control on life cycles

   Evolution 67:883–893.

   https://doi.org/10.1111/j.1558-5646.2012.01828.x

   • Google Scholar
10. 1. Chiang GC
    2. Barua D
    3. Kramer EM
    4. Amasino RM
    5. Donohue K

    (2009) Major flowering time gene, flowering locus C, regulates seed germination in Arabidopsis thaliana

    Proceedings of the National Academy of Sciences of USA 106:11661–11666.

    https://doi.org/10.1073/pnas.0901367106

    • Google Scholar
11. 1. Childs DZ
    2. Metcalf CJE
    3. Rees M

    (2010) Evolutionary bet-hedging in the real world: empirical evidence and challenges revealed by plants

    Proceedings Biological sciences/The Royal Society 277:1149–1151.

    https://doi.org/10.1098/rspb.2010.0707

    • Google Scholar
12. 1. Cleland EE
    2. Chuine I
    3. Menzel A
    4. Mooney HA
    5. Schwartz MD

    (2007) Shifting plant phenology in response to global change

    Trends in Ecology & Evolution 22:357–365.

    https://doi.org/10.1016/j.tree.2007.04.003

    • Google Scholar
13. 1. Cotton PA

    (2003) Avian migration phenology and global climate change

    Proceedings of the National Academy of Sciences of USA 100:12219–12222.

    https://doi.org/10.1073/pnas.1930548100

    • Google Scholar
14. 1. Donohue K
    2. Burghardt LT
    3. Runcie D
    4. Bradford KJ
    5. Schmitt J

    (2014) Applying developmental threshold models to evolutionary ecology

    Trends in Ecology & Evolution 30:66–77.

    https://doi.org/10.1016/j.tree.2014.11.008

    • Google Scholar
15. 1. Fenner M

    (1991)

    The effects of parental environment on seed germinability

    Seed Science Research 1:75–81.

    • Google Scholar
16. 1. Fitter AH
    2. Fitter RS

    (2002) Rapid changes in flowering time in British plants

    Science 296:1689–1691.

    https://doi.org/10.1126/science.1071617

    • Google Scholar
17. 1. Footitt S
    2. Douterelo-Soler I
    3. Clay H
    4. Finch-Savage WE

    (2011) Dormancy cycling in Arabidopsis seeds is controlled by seasonally distinct hormone-signaling pathways

    Proceedings of the National Academy of Sciences of USA 108:20236–20241.

    https://doi.org/10.1073/pnas.111632510832

    • Google Scholar
18. 1. Footitt S
    2. Huang Z
    3. Clay HA
    4. Mead A
    5. Finch-Savage WE

    (2013) Temperature, light and nitrate sensing coordinate Arabidopsis seed dormancy cycling, resulting in winter and summer annual phenotypes

    The Plant Journal 74:1003–1015.

    https://doi.org/10.1111/tpj.12186

    • Google Scholar
19. 1. Grime JP

    (1988)

    Plant evolutionary biology

    371–393, Plant evolutionary biology.

    • Google Scholar
20. 1. Grime JP
    2. Mason G
    3. Curtis AV
    4. Rodman J
    5. Band SR
    6. Mowforth MA
    7. Neal AM
    8. Shaw SC

    (1981) A comparative study of germination characteristcs in local flora

    The Journal of Ecology 69:1017–1059.

    https://doi.org/10.2307/2259651

    • Google Scholar
21. 1. Gualano NA
    2. Benech-Arnold RL

    (2009) Predicting pre-harvest sprouting susceptibility in barley: Looking for ‘sensitivity windows’ to temperature throughout grain filling in various commercial cultivars

    Field Crops Research 114:35–44.

    https://doi.org/10.1016/j.fcr.2009.06.016

    • Google Scholar
22. 1. Kendall SL
    2. Hellwege A
    3. Marriot P
    4. Whalley C
    5. Graham IA
    6. Penfield S

    (2011) Induction of dormancy in Arabidopsis summer annuals requires parallel regulation of DOG1 and hormone metabolism by low temperature and CBF transcription factors

    The Plant Cell 23:2568–2580.

    https://doi.org/10.1105/tpc.111.087643

    • Google Scholar
23. 1. Li P
    2. Filiault D
    3. Box MS
    4. Kerdaffrec E
    5. van Oosterhout C
    6. Wilczek AM
    7. Schmitt J
    8. McMullan M
    9. Bergelson J
    10. Nordborg M
    11. Dean C

    (2014) Multiple FLC haplotypes defined by independent cis-regulatory variation underpin life history diversity in Arabidopsis thaliana

    Genes & Development 28:1635–1640.

    https://doi.org/10.1101/gad.245993.114

    • Google Scholar
24. 1. Lundemo S
    2. Falahati-Anbaran M
    3. Stenoien HK

    (2009) Seed banks cause elevated generation times and effective population sizes of Arabidopsis thalisana in northern Europe

    Molecular Ecology 18:2798–2811.

    https://doi.org/10.1111/j.1365-294X.2009.04236

    • Google Scholar
25. 1. MacGregor DR
    2. Kendall SL
    3. Fedi F
    4. Florance H
    5. Paszkievicz K
    6. Smirnoff N
    7. Penfield S

    (2015) Seed production temperature regulation of primary dormancy occurs through control of seed coat phenylpropanoid metabolism

    The New Phytologist 205:642–652.

    https://doi.org/10.1111/nph.13090

    • Google Scholar
26. 1. Menzel A
    2. Fabian P

    (1999) Growing season extended in Europe

    Nature 397:659.

    https://doi.org/10.1038/17709

    • Google Scholar
27. 1. Montesinos A
    2. Tonsor SJ
    3. Alonso-Blanco C
    4. Picó FX

    (2009) Demographic and genetic patterns of variation among populations of Arabidopsis thaliana from contrasting native environments

    PLOS ONE 4:e7213.

    https://doi.org/10.1371/journal.pone.0007213

    • Google Scholar
28. 1. Montesinos-Navarro A
    2. Picó FX
    3. Tonsor SJ

    (2012) Clinal variation in seed traits influencing life cycle timing in Arabidopsis thaliana

    Evolution 66:3417–3431.

    https://doi.org/10.1111/j.1558-5646.2012.01689.x

    • Google Scholar
29. 1. Parmesan C
    2. Yohe G

    (2003) A globally coherent fingerprint of climate change impacts across natural systems

    Nature 421:37–42.

    https://doi.org/10.1038/nature01286

    • Google Scholar
30. 1. Robbelen G

    (1965)

    The laibach standard collection of natural races

    Arabidopsis Information Service 02S:.

    • Google Scholar
31. 1. Schmuths H
    2. Bachmann K
    3. Weber WE
    4. Horres R
    5. Hoffmann MH

    (2006) Effects of preconditioning and temperature during germination of 73 natural accessions of Arabidopsis thaliana

    Annals of Botany 97:623–634.

    https://doi.org/10.1093/aob/mcl012

    • Google Scholar
32. 1. Shindo C
    2. Lister C
    3. Crevillen P
    4. Nordborg M
    5. Dean C

    (2006) Variation in the epigenetic silencing of FLC contributes to natural variation in Arabidopsis vernalization response

    Genes & Development 20:3079–3083.

    https://doi.org/10.1101/gad.405306

    • Google Scholar
33. 1. Sung S
    2. Amasino RM

    (2004) Vernalization in Arabidopsis thaliana is mediated by the PHD finger protein VIN3

    Nature 427:159–164.

    https://doi.org/10.1038/nature02195

    • Google Scholar
34. 1. Thomas JF
    2. Raper CD

    (1976) Photoperiodic control of seed filling for soybeans

    Crop Science 16:667–672.

    https://doi.org/10.2135/cropsci1976.0011183X001600050017x

    • Google Scholar
35. 1. Thompson L

    (1994) The spatiotemporal effects of nitrogen and litter on the population dynamics of Arabidopsis thaliana

    The Journal of Ecology 82:63–68.

    https://doi.org/10.2307/2261386

    • Google Scholar
36. 1. Toomajian C
    2. Hu TT
    3. Aranzana MJ
    4. Lister C
    5. Tang C
    6. Zheng H
    7. Zhao K
    8. Calabrese P
    9. Dean C
    10. Nordborg M

    (2006) A nonparametric test reveals selection for rapid flowering in the Arabidopsis genome

    PLOS Biology 4:e137.

    https://doi.org/10.1371/journal.pbio.0040137

    • Google Scholar
37. 1. Toorop PE
    2. Cuerva RC
    3. Begg GS
    4. Locardi B
    5. Squire GR
    6. Iannetta PP

    (2012) Co-adaptation of seed dormancy and flowering time in the arable weed Capsella bursa-pastoris (shepherd's purse)

    Annals of Botany 109:481–489.

    https://doi.org/10.1093/aob/mcr301

    • Google Scholar
38. 1. Totterdell S
    2. Roberts EH

    (1979) Effects of low temperatures on the loss of innate dormancy and the development of induced dormancy in seeds of Rumex obtusifolius L. and Rumex crispus L

    Plant, Cell & Environment 2:131–137.

    https://doi.org/10.1111/j.1365-3040.1979.tb00783.x

    • Google Scholar
39. 1. Wilczek AM
    2. Roe JL
    3. Knapp MC
    4. Cooper MD
    5. Lopez-Gallego C
    6. Martin LJ
    7. Muir CD
    8. Sim S
    9. Walker A
    10. Anderson J
    11. Egan JF
    12. Moyers BT
    13. Petipas R
    14. Giakountis A
    15. Charbit E
    16. Coupland G
    17. Welch SM
    18. Schmitt J

    (2009) Effects of genetic perturbation on seasonal life history plasticity

    Science 323:930–934.

    https://doi.org/10.1126/science.1165826

    • Google Scholar
40. 1. Willis CG
    2. Ruhfel B
    3. Primack RB
    4. Miller-Rushing AJ
    5. Davis CC

    (2008) Phylogenetic patterns of species loss in Thoreau's woods are driven by climate change

    Proceedings of the National Academy of Sciences of USA 105:17029–17033.

    https://doi.org/10.1073/pnas.0806446105

    • Google Scholar

Author details

1. Vicki Springthorpe

   Department of Biology, University of York, York, United Kingdom

   Contribution

   VS, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0002-7430-3712

2. Steven Penfield

   Department of Crop Genetics, John Innes Centre, Norwich, United Kingdom

   Contribution

   SP, Conception and design, Analysis and interpretation of data, Drafting or revising the article

   For correspondence

   steven.penfield@jic.ac.uk

   Competing interests

   The authors declare that no competing interests exist.

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