Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Winter temperatures predominate in spring phenological responses to warming

Abstract

Research on woody plant species highlights three major cues that shape spring phenological events: chilling, forcing and photoperiod. Increasing research on the phenological impacts of climate change has led to debate over whether chilling and/or photoperiod cues have slowed phenological responses to warming in recent years. Here we use a global meta-analysis of all published experiments to test the relative effects of these cues. Almost all species show strong responses to all three cues, with chilling being the strongest and photoperiod the weakest. Forecasts from our findings for Central Europe suggest that spring phenology will continue to advance, as stalling effects of chilling generally appear above 4 °C warming in this region. Our results unify both sides of the debate over phenological cues: while all species may respond to all cues strongly in experimental conditions, in current environmental conditions the dominant signal of climate change is from increased forcing.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Short-term experiments on woody plant phenology manipulate photoperiod and temperature to estimate chilling, forcing and photoperiod cues.
Fig. 2: Estimated effects of chilling, forcing and photoperiod on budburst timing across 65 species.
Fig. 3: Estimates of budburst across a range of forcing temperatures and estimated chilling.
Fig. 4: Implications of warming on budburst timing varies across species and sites, depending strongly on prewarming climate conditions related to chilling for each site.

Data availability

The OSPREE budburst database used in this manuscript is publicly archived in the Knowledge Network for Biocomplexity30.

Code availability

The code for models used in this manuscript is publicly archived in the Knowledge Network for Biocomplexity30.

References

  1. 1.

    IPCC Climate Change 2014: Impacts, Adaptation, and Vulnerability (eds Field, C. B. et al.) (Cambridge Univ. Press, 2014).

  2. 2.

    Miller-Rushing, A. J. & Primack, R. B. Global warming and flowering times in Thoreau’s Concord: a community perspective. Ecology 89, 332—341 (2008).

    Google Scholar 

  3. 3.

    Menzel, A. et al. European phenological response to climate change matches the warming pattern. Glob. Change Biol. 12, 1969–1976 (2006).

    Google Scholar 

  4. 4.

    Cleland, E. E., Chuine, I., Menzel, A., Mooney, H. A. & Schwartz, M. D. Shifting plant phenology in response to global change. Trends Ecol. Evol. 22, 357–365 (2007).

    Google Scholar 

  5. 5.

    Wolkovich, E. M. et al. Warming experiments underpredict plant phenological responses to climate change. Nature 485, 494–497 (2012).

    CAS  Google Scholar 

  6. 6.

    Rutishauser, T., Luterbacher, J., Defila, C., Frank, D. & Wanner, H. Swiss spring plant phenology 2007: extremes, a multi-century perspective, and changes in temperature sensitivity. Geophys. Res. Lett. 35, L05703 (2008).

    Google Scholar 

  7. 7.

    Yu, H. Y., Luedeling, E. & Xu, J. C. Winter and spring warming result in delayed spring phenology on the Tibetan Plateau. Proc. Natl Acad. Sci. USA 107, 22151–22156 (2010).

    CAS  Google Scholar 

  8. 8.

    Wang, X. et al. No trends in spring and autumn phenology during the global warming hiatus. Nat. Commun. 10, 2389 (2019).

    Google Scholar 

  9. 9.

    Fu, Y. S. H. et al. Declining global warming effects on the phenology of spring leaf unfolding. Nature 526, 104–107 (2015).

    CAS  Google Scholar 

  10. 10.

    Chuine, I. et al. Can phenological models predict tree phenology accurately in the future? The unrevealed hurdle of endodormancy break. Glob. Change Biol. 22, 3444–3460 (2016).

    Google Scholar 

  11. 11.

    Harrington, C. A. & Gould, P. J. Tradeoffs between chilling and forcing in satisfying dormancy requirements for Pacific Northwest tree species. Front. Plant Sci. 6, 120 (2015).

    Google Scholar 

  12. 12.

    Zohner, C. M., Benito, B. M., Svenning, J. C. & Renner, S. S. Day length unlikely to constrain climate-driven shifts in leaf-out times of northern woody plants. Nat. Clim. Change 6, 1120–1123 (2016).

    Google Scholar 

  13. 13.

    Basler, D. & Körner, C. Photoperiod and temperature responses of bud swelling and bud burst in four temperate forest tree species. Tree Physiol. 34, 377–388 (2014).

    Google Scholar 

  14. 14.

    Caffarra, A., Donnelly, A., Chuine, I. & Jones, M. B. Modelling the timing of Betula pubescens bud-burst. I. Temperature and photoperiod: a conceptual model. Clim. Res. 46, 147–157 (2011).

    Google Scholar 

  15. 15.

    Flynn, D. F. B. & Wolkovich, E. M. Temperature and photoperiod drive spring phenology across all species in a temperate forest community. New Phytol. 219, 1353–1362 (2018).

    CAS  Google Scholar 

  16. 16.

    Caffarra, A., Donnelly, A. & Chuine, I. Modelling the timing of Betula pubescens budburst. II. Integrating complex effects of photoperiod into process-based models. Clim. Res. 46, 159–170 (2011).

    Google Scholar 

  17. 17.

    Fraga, H., Pinto, J. G. & Santos, J. A. Climate Change projections for chilling and heat forcing conditions in European vineyards and olive orchards: a multi-model assessment. Climatic Change 152, 179–193 (2019).

    Google Scholar 

  18. 18.

    Heide, O. Daylength and thermal time responses of budburst during dormancy release in some northern deciduous trees. Physiol. Plant. 88, 531–540 (1993).

    CAS  Google Scholar 

  19. 19.

    Singh, R. K., Svystun, T., AlDahmash, B., Jönsson, A. M. & Bhalerao, R. P. Photoperiod- and temperature-mediated control of phenology in trees—a molecular perspective. New Phytol. 213, 511–524 (2017).

    CAS  Google Scholar 

  20. 20.

    Vitasse, Y. & Basler, D. What role for photoperiod in the bud burst phenology of European beech. Eur. J. For. Res. 132, 1–8 (2013).

    Google Scholar 

  21. 21.

    Vitasse, Y. & Basler, D. Is the use of cuttings a good proxy to explore phenological responses of temperate forests in warming and photoperiod experiments? Tree Physiol. 34, 174–183 (2014).

    Google Scholar 

  22. 22.

    Laube, J. et al. Chilling outweighs photoperiod in preventing precocious spring development. Glob. Change Biol. 20, 170–182 (2014).

    Google Scholar 

  23. 23.

    Basler, D. & Körner, C. Photoperiod sensitivity of bud burst in 14 temperate forest tree species. Agric. For. Meteorol. 165, 73–81 (2012).

    Google Scholar 

  24. 24.

    Caffarra, A. & Donnelly, A. The ecological significance of phenology in four different tree species: effects of light and temperature on bud burst. Int. J. Biometeorol. 55, 711–721 (2011).

    Google Scholar 

  25. 25.

    Ohlemüller, R., Gritti, E. S., Sykes, M. T. & Thomas, C. D. Towards European climate risk surfaces: the extent and distribution of analogous and non-analogous climates 1931–2100. Glob. Ecol. Biogeogr. 15, 395–405 (2006).

    Google Scholar 

  26. 26.

    Williams, J. W. & Jackson, S. T. Novel climates, no-analog communities, and ecological surprises. Front. Ecol. Environ. 5, 475–482 (2007).

    Google Scholar 

  27. 27.

    Williams, J. W., Jackson, S. T. & Kutzbacht, J. E. Projected distributions of novel and disappearing climates by 2100 AD. Proc. Natl Acad. Sci. USA 104, 5738–5742 (2007).

    CAS  Google Scholar 

  28. 28.

    IPCC Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).

  29. 29.

    Xu, Y., Ramanathan, V. & Victor, D. G. Global warming will happen faster than we think. Nature 564, 30–32 (2018).

  30. 30.

    Wolkovich, E. M. et al. Observed Spring Phenology Responses in Experimental Environments (OSPREE) (Knowledge Network for Biocomplexity, 2019); https://doi.org/10.5063/F1CZ35KB

  31. 31.

    Richardson, E. A model for estimating the completion of rest for ‘Redhaven’ and ’Elberta’ peach trees. HortScience 9, 331–332 (1974).

    Google Scholar 

  32. 32.

    Dennis, F. Problems in standardizing methods for evaluating the chilling requirements for the breaking of dormancy in buds of woody plants. HortScience 38, 347–350 (2003).

    Google Scholar 

  33. 33.

    Gelman, A. & Hill, J. Data Analysis Using Regression and Multilevel/Hierarchical Models (Cambridge Univ. Press, 2006).

  34. 34.

    Fu, Y. H. et al. Short photoperiod reduces the temperature sensitivity of leaf-out in saplings of Fagus sylvatica but not in horse chestnut. Glob. Change Biol. 25, 1696–1703 (2019).

    Google Scholar 

  35. 35.

    Bradley, N. L., Leopold, A. C., Ross, J. & Huffaker, W. Phenological changes reflect climate change in Wisconsin. Proc. Natl Acad. Sci. USA 96, 9701–9704 (1999).

    CAS  Google Scholar 

  36. 36.

    Gauzere, J., Lucas, C., Ronce, O., Davi, H. & Chuine, I. Sensitivity analysis of tree phenology models reveals increasing sensitivity of their predictions to winter chilling temperature and photoperiod with warming climate. Ecol. Model. 441, 108805 (2019).

    Google Scholar 

  37. 37.

    Heide, O. & Prestrud, A. Low temperature, but not photoperiod, controls growth cessation and dormancy induction and release in apple and pear. Tree Physiol. 25, 109–114 (2005).

    CAS  Google Scholar 

  38. 38.

    van der Schoot, C., Paul, L. K. & Rinne, P. L. H. The embryonic shoot: a lifeline through winter. J. Exp. Bot. 65, 1699–1712 (2014).

    Google Scholar 

  39. 39.

    Fishman, S., Erez, A. & Couvillon, G. The temperature dependence of dormancy breaking in plants: mathematical analysis of a two-step model involving a cooperative transition. J. Theor. Biol. 124, 473–483 (1987).

    Google Scholar 

  40. 40.

    Weinberger, J. H. et al. Chilling requirements of peach varieties. Proc. J. Am. Soc. Hort. Sci. 56, 122–128 (1950).

  41. 41.

    Polgar, C. A., Primack, R. B., Williams, E. H., Stichter, S. & Hitchcock, C. Climate effects on the flight period of Lycaenid butterflies in Massachusetts. Biol. Conserv. 160, 25–31 (2013).

    Google Scholar 

  42. 42.

    Vitasse, Y. Ontogenic changes rather than difference in temperature cause understory trees to leaf out earlier. New Phytol. 198, 149–155 (2013).

    Google Scholar 

  43. 43.

    Laube, J., Sparks, T. H., Estrella, N. & Menzel, A. Does humidity trigger tree phenology? Proposal for an air humidity based framework for bud development in spring. New Phytol. 202, 350–355 (2014).

    Google Scholar 

  44. 44.

    Li, C., Stevens, B. & Marotzke, J. Eurasian winter cooling in the warming hiatus of 1998–2012. Geophys. Res. Lett. 42, 8131–8139 (2015).

    Google Scholar 

  45. 45.

    Balling, R. C. J., Michaels, P. J. & Knappenberger, P. C. Analysis of winter and summer warming rates in gridded temperature time series. Clim. Res. 9, 175–181 (1998).

    Google Scholar 

  46. 46.

    Hänninen, H. Effects of climatic change on trees from cool and temperate regions: an ecophysiological approach to modelling of bud burst phenology. Can. J. Bot. 73, 183–199 (1995).

    Google Scholar 

  47. 47.

    Güsewell, S., Furrer, R., Gehrig, R. & Pietragalla, B. Changes in temperature sensitivity of spring phenology with recent climate warming in Switzerland are related to shifts of the preseason. Glob. Change Biol.23, 5189–5202 (2017).

    Google Scholar 

  48. 48.

    Roberts, A. M., Tansey, C., Smithers, R. J. & Phillimore, A. B. Predicting a change in the order of spring phenology in temperate forests. Glob. Change Biol.21, 2603–2611 (2015).

    Google Scholar 

  49. 49.

    Moher, D., Liberati, A., Tetzlaff, J. & Altman, D. G. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Ann. Intern. Med. 151, 264–269 (2009).

    Google Scholar 

  50. 50.

    Kicinski, M. Publication bias in recent meta-analyses. PLoS ONE 8, e81823 (2013).

  51. 51.

    Gurevitch, J., Morrow, L. L., Wallace, A. & Walsh, J. S. A meta-analysis of competition in field experiments. Am. Nat. 140, 539–572 (1992).

    Google Scholar 

  52. 52.

    Gurevitch, J. & Hedges, L. V. Statistical issues in ecological meta-analyses. Ecology 80, 1142–1149 (1999).

    Google Scholar 

  53. 53.

    Lin, L. F. & Chu, H. T. Quantifying publication bias in meta-analysis. Biometrics 74, 785–794 (2018).

    Google Scholar 

  54. 54.

    Luedeling, E. & Brown, P. H. A global analysis of the comparability of winter chill models for fruit and nut trees. Int. J. Biometeorol. 55, 411–421 (2011).

    Google Scholar 

  55. 55.

    R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2017).

  56. 56.

    Luedeling, E. chillR: statistical methods for phenology analysis in temperate fruit trees. R package version 0.70.17 (2019).

  57. 57.

    Cornes, R. C., van der Schrier, G., van den Besselaar, E. J. & Jones, P. D. An ensemble version of the E-OBS temperature and precipitation data sets. J. Geophys. Res. Atmos. 123, 9391–9409 (2018).

    Google Scholar 

  58. 58.

    Livneh, B.et al. A spatially comprehensive, hydrometeorological data set for Mexico, the US, and Southern Canada 1950–2013. Sci. Data 2, 150042 (2015).

  59. 59.

    Harrington, C. A., Gould, P. J. & St Clair, J. B. Modeling the effects of winter environment on dormancy release of Douglas-fir. For. Ecol. Manag. 259, 798–808 (2010).

    Google Scholar 

  60. 60.

    Carpenter, B. et al. Stan: a probabilistic programming language. J. Stat. Softw. https://doi.org/10.18637/jss.v076.i01(2017).

  61. 61.

    Stan Development Team. RStan: the R interface to Stan. R package version 2.17.3 (2018).

  62. 62.

    Gelman, A. et al. Bayesian Data Analysis (CRC Press, 2014).

  63. 63.

    Gauzere, J. et al. Integrating interactive effects of chilling and photoperiod in phenological process-based models. A case study with two European tree species: Fagus sylvatica and Quercus petraea. Agric. For. Meteorol. 244, 9–20 (2017).

    Google Scholar 

  64. 64.

    Saikkonen, K. et al. Climate change-driven species’ range shifts filtered by photoperiodism. Nat. Clim. Change 2, 239 (2012).

    Google Scholar 

  65. 65.

    Way, D. A. & Montgomery, R. A. Photoperiod constraints on tree phenology, performance and migration in a warming world. Plant Cell Environ. 38, 1725–1736 (2015).

    Google Scholar 

  66. 66.

    Chuine, I., Garcia de Cortazar Atauri, I., Hanninen, H. & Kramer, K. in Phenology: An Integrative Environmental Science (ed. Schwartz M.) 275–293 (Springer, 2013).

  67. 67.

    Stan Development Team Stan User’s Guide v.2.19 (Stan, 2019).

Download references

Acknowledgements

We thank the many researchers who conducted the experiments synthesized in this manuscript. We thank B. Cook for help with climate data, E. Forrestel for assisting with data scraping and C. Zohner for sharing tables. We also thank J. Davies, S. Elmendorf and J. HilleRisLambers for helpful comments that improved the manuscript. The National Science Foundation (grant no. DBI 14-01854 to A.K.E.), NSERC Discovery Award (grant no. RGPIN-05038 to E.M.W.), Canada Research Chair in Temporal Ecology (E.M.W.) and Spanish Ministry for Science and Innovation (grant no. CGL2017-86926-P and PID2019/109711RJ-I00 to I.M.-C.) provided funding. Any opinion, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

Author information

Affiliations

Authors

Contributions

D.F.B.F., T.S. and E.M.W. conceived of the OSPREE database, which gave rise to this manuscript. All authors worked tirelessly to build the database and all contributed data analysis and/or code. C.J.C., D.M.B., E.M.W., I.M.-C. and A.K.E. created the figures. A.K.E. and E.M.W. wrote most of the manuscript, with substantial contributions from C.J.C., D.M.B. and I.M.-C. All authors reviewed and revised the manuscript.

Corresponding author

Correspondence to A. K. Ettinger.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Climate Change thanks Albert Phillimore, Constantin Zohner and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Map of days to budburst experiments in the OSPREE database.

Legend shows each dataset included in the main OSPREE model with all species and treatments (Supplementary Tables 5, 6); symbols outlined in black represent datasets included in the main budburst model; triangles represent studies in which chilling was manipulated experimentally or through multiple field sample dates; circles represent studies that did not manipulate chilling. See Supplementary Table 1 for the reference associated with each dataset.

Extended Data Fig. 2 Map of maximum and minimum chilling, forcing, and photoperiod treatments, across all data included in our main model, and the locations where each experiment was conducted.

We did not find strong positive spatial autocorrelation—that is, indicating higher similarity in the treatments applied to experiments in nearby locations- in either minimum (A,C,E) or maximum (B,D,F) treatments, as measured by Moran’s I (shown here for European sites). Insets show relationship of each cue’s treatment level with the latitude of the experiment.

Extended Data Fig. 3 The diversity of study designs used in analyses.

Heatmaps show the range and commonness of different forcing (x-axis in all panels) by photoperiod (y-axis in all panels) combinations and with which chilling they were combined. In (A, top and bottom) we show our estimated chill units, which integrate across field (B, top and bottom) and experimental chilling (C, top and bottom). The top row shows all data included in the full model with 203 species, while the bottom row shows the data included in the main model with a subset of species well-represented across treatments and studies. Grey squares indicate a treatment was not applied (that is, the prevalence of grey squares in (C) highlights how few studies include experimental chilling). Field sample dates are counted as any reported sampling dates more than 14 days apart.

Extended Data Fig. 4 Chilling accumulates differently in experiments with constant temperatures versus natural systems, in which temperature is more strongly correlated with chilling.

See ‘Estimating chilling’ in Methods for a detailed description of ‘Field’ climate data, for which we use historical climate data from Europe. Fig. 3 uses ‘Field’ relationships (that is, climate data and relationships from field chilling conditions to convert chill temperature to total chilling), whereas Supplementary Fig. 2 uses ‘Constant temperature’ conditions (analogous to most experimental conditions) to estimate total chilling.

Extended Data Fig. 5 Estimates for effects of chilling exceeded estimates for forcing, photoperiod, provenance latitude, and the interaction between latitude and photoperiod, for most species, in the latitude budburst model.

Using Utah units for chilling (Supplementary Table 10) and standardized predictor variables, which allow comparisons across cues, we show that, as with the main budburst model (Fig. 2), most species (smaller symbols) are responsive to most cues. Chilling is the strongest cue when considering overall estimates across species (larger, dark blue circles).

Extended Data Fig. 6 Forecasted changes in chilling and spring phenology vary with amount of warming across European locations included in the PEP725 database.

Changes in chilling (top panel) and budburst for Betula pendula (bottom panel) are calculated relative to the mean chilling and leafout dates during a prewarming time period (1951-1960) for each location. Arrows indicate sites shown in Fig. 4a and Supplementary Fig. 4A (latitude = 46.8N, longitude = 12.8E, 659 m above sea level) and Fig. 4b and Supplementary Fig. 4B (latitude = 48.3N, longitude = 15.8E, 210 m above sea level).

Extended Data Fig. 7 Budburst is affected by climate change induced shifts in photoperiod, especially at high latitudes, although effects vary by site and are minor compared to effects of warming.

We show forecasted effects of varying levels of warming on Fagus sylvatica, the most photoperiod-sensitive species in our database, across three latitudes within its range, as predicted by the latitude model. The low latitude site (A) is located at 46.8N, 15.7E; the mid-latitude site (B) is located at 47.7N, 16.3E; and the high-latitude (C) site is located at 48.8N, 15.4E.

Extended Data Fig. 8 Declining sensitivities observed in long-term European data for a suite of common trees may be explained by a statistical artefact.

We compared the sensitivity estimated from linear regressions of day of leafout versus mean spring temperature (estimated thus as days/°C) from PEP725 data for Betula pendula from 45 sites (‘European data’) with estimated declines using simulations where the cues were held constant but spring temperatures warmed by 1-4°C (‘Simulations’) and found the estimated temperature sensitivity measured as days/°C declined even though the underlying cues had not changed. See Potential statistical artefacts in declines of temperature sensitivity in observational long-term data in the Supplementary Information for further details.

Extended Data Fig. 9 Day of leafout varies with chilling, growing degree-days, and mean spring temperature.

These relationships are shown prewarming (left panels, 1951-1960) and post-warming (right panels, 2000-2010) for PEP725 sites in Germany where Betula pendula phenology has been monitored for decades.

Extended Data Fig. 10 Growing degree days (GDD) versus chill units at the time of budburst from the OSPREE database for common species in the PEP725 long-term phenological database.

The black line shows the range of chilling (10-90% quantiles) accumulated from 1 September to 1 March for 45 sites for Betula pendula (see also Potential statistical artefacts in declines of temperature sensitivity in observational long-term data in the Supplementary Information). We calculated GDD here as the average daily forcing temperature multiplied by days to budburst.

Supplementary information

Supplementary Information

Supplementary text, Figs. 1–5, Tables 1–14 and references.

Supplementary Appendix 1

PRISMA checklist.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Ettinger, A.K., Chamberlain, C.J., Morales-Castilla, I. et al. Winter temperatures predominate in spring phenological responses to warming. Nat. Clim. Chang. 10, 1137–1142 (2020). https://doi.org/10.1038/s41558-020-00917-3

Download citation

Further reading

Search

Quick links