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Leaf senescence exhibits stronger climatic responses during warm than during cold autumns

Abstract

A warmer world could extend the growing seasons for plants. Changes in spring phenology have been studied, yet autumn phenology remains poorly understood. Using >500,000 phenological records of four temperate tree species between 1951 and 2013 in Europe, we show that leaf senescence in warm autumns exhibits stronger climate responses, with a higher phenological plasticity, than in cold autumns, indicating a nonlinear response to climate. The onset of leaf senescence in warm autumns was delayed due to the stronger climate response, primarily caused by night-time warming. However, daytime warming, especially during warm autumns, imposes a drought stress which advances leaf senescence. This may counteract the extension of growing season under global warming. These findings provide guidance for more reliable predictions of plant phenology and biosphere–atmosphere feedbacks in the context of global warming.

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Fig. 1: Climatic responses of leaf senescence of four temperate tree species in Europe during 1951–2013.
Fig. 2: Coefficient of variation of the leaf senescence onset day of four temperate tree species in Europe during 1951–2013.
Fig. 3: Average leaf senescence dates of four temperate tree species at different phenological observation site groups in Europe during 1951–2013.
Fig. 4: Growing degree days at different site groups in Europe during 1951–2013.
Fig. 5: Climatic responses of leaf senescence of four temperate tree species in Europe during 1951–2013.

Data availability

Phenology data are available from the Pan European Phenology (PEP) network (www.pep725.eu). Climate data can be downloaded from E-OBS site: http://ensembles-eu.metoffice.com.

Code availability

The codes used for data processing and analysis in this study are available on Figshare at https://doi.org/10.6084/m9.figshare.12291245.v6.

References

  1. 1.

    Richardson, A. D. et al. Climate change, phenology, and phenological control of vegetation feedbacks to the climate system. Agric. Meteorol. 169, 156–173 (2013).

    Article  Google Scholar 

  2. 2.

    Chuine, I. & Beaubien, E. G. Phenology is a major determinant of tree species range. Ecol. Lett. 4, 500–510 (2001).

    Article  Google Scholar 

  3. 3.

    Edwards, M. & Richardson, A. J. Impact of climate change on marine pelagic phenology and trophic mismatch. Nature 430, 881–884 (2004).

    CAS  Article  Google Scholar 

  4. 4.

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

    Article  Google Scholar 

  5. 5.

    Gill, A. L. et al. Changes in autumn senescence in northern hemisphere deciduous trees: a meta-analysis of autumn phenology studies. Ann. Bot. 116, 875–888 (2015).

    CAS  Article  Google Scholar 

  6. 6.

    Piao, S. et al. Plant phenology and global climate change: current progresses and challenges. Glob. Change Biol. 25, 1922–1940 (2019).

    Article  Google Scholar 

  7. 7.

    Delpierre, N. et al. Modelling interannual and spatial variability of leaf senescence for three deciduous tree species in France. Agric. Meteorol. 149, 938–948 (2009).

    Article  Google Scholar 

  8. 8.

    Chuine, I., de Cortazar-Atauri, I. G., Kramer, K. & Hänninen, H. in Phenology: An Integrative Environmental Science (Ed. Schwartz, M D.) 275–293 (Springer, 2013).

  9. 9.

    Richardson, A. D. et al. Terrestrial biosphere models need better representation of vegetation phenology: results from the North American Carbon Program Site Synthesis. Glob. Change Biol. 18, 566–584 (2012).

    Article  Google Scholar 

  10. 10.

    Estiarte, M. & Penuelas, J. Alteration of the phenology of leaf senescence and fall in winter deciduous species by climate change: effects on nutrient proficiency. Glob. Change Biol. 21, 1005–1017 (2015).

    Article  Google Scholar 

  11. 11.

    Liu, Q. et al. Delayed autumn phenology in the Northern Hemisphere is related to change in both climate and spring phenology. Glob. Change Biol. 22, 3702–3711 (2016).

    Article  Google Scholar 

  12. 12.

    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).

    Article  Google Scholar 

  13. 13.

    Körner, C. & Basler, D. Phenology under global warming. Science 327, 1461–1462 (2010).

    Article  Google Scholar 

  14. 14.

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

    CAS  Article  Google Scholar 

  15. 15.

    Wu, C. et al. Contrasting responses of autumn-leaf senescence to daytime and night-time warming. Nat. Clim. Change 8, 1092–1096 (2018).

    CAS  Article  Google Scholar 

  16. 16.

    Vicente-Serrano, S. M., Beguería, S. & López-Moreno, J. I. A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. J. Clim. 23, 1696–1718 (2010).

    Article  Google Scholar 

  17. 17.

    Graham, M. H. Confronting multicollinearity in ecological multiple regression. Ecology 84, 2809–2815 (2003).

    Article  Google Scholar 

  18. 18.

    Hoerl, A. E. & Kennard, R. W. Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12, 55–67 (1970).

    Article  Google Scholar 

  19. 19.

    Schielzeth, H. Simple means to improve the interpretability of regression coefficients. Methods Ecol. Evol. 1, 103–113 (2010).

    Article  Google Scholar 

  20. 20.

    Fu, Y. S. et al. Variation in leaf flushing date influences autumnal senescence and next year’s flushing date in two temperate tree species. Proc. Natl Acad. Sci. USA 111, 7355–7360 (2014).

    CAS  Article  Google Scholar 

  21. 21.

    Keenan, T. F. & Richardson, A. D. The timing of autumn senescence is affected by the timing of spring phenology: implications for predictive models. Glob. Change Biol. 21, 2634–2641 (2015).

    Article  Google Scholar 

  22. 22.

    Sakai, A. & Larcher, W. Frost Survival of Plants: Responses and Adaptation to Freezing Stress Vol. 62 (Springer Science & Business Media, 1987).

  23. 23.

    Piao, S. et al. Leaf onset in the northern hemisphere triggered by daytime temperature. Nat. Commun. 6, 6911 (2015).

    CAS  Article  Google Scholar 

  24. 24.

    Mariën, B. et al. Detecting the onset of autumn leaf senescence in deciduous forest trees of the temperate zone. New Phytol. 224, 166–176 (2019).

    Article  Google Scholar 

  25. 25.

    Engelbrecht, B. M. et al. Drought sensitivity shapes species distribution patterns in tropical forests. Nature 447, 80–82 (2007).

    CAS  Article  Google Scholar 

  26. 26.

    Bartlett, M. K., Scoffoni, C. & Sack, L. The determinants of leaf turgor loss point and prediction of drought tolerance of species and biomes: a global meta-analysis. Ecol. Lett. 15, 393–405 (2012).

    Article  Google Scholar 

  27. 27.

    Kay, J. E. et al. The Community Earth System Model (CESM) large ensemble project: a community resource for studying climate change in the presence of internal climate variability. Bull. Am. Meteorol. Soc. 96, 1333–1349 (2015).

    Article  Google Scholar 

  28. 28.

    Templ, B. et al. Pan European Phenological database (PEP725): a single point of access for European data. Int. J. Biometeorol. 62, 1109–1113 (2018).

    Article  Google Scholar 

  29. 29.

    Leys, C., Ley, C., Klein, O., Bernard, P. & Licata, L. Detecting outliers: do not use standard deviation around the mean, use absolute deviation around the median. J. Exp. Soc. Psychol. 49, 764–766 (2013).

    Article  Google Scholar 

  30. 30.

    Vitasse, Y., Signarbieux, C. & Fu, Y. H. Global warming leads to more uniform spring phenology across elevations. Proc. Natl Acad. Sci. USA 115, 1004–1008 (2018).

    CAS  Article  Google Scholar 

  31. 31.

    Wohlfahrt, G., Tomelleri, E. & Hammerle, A. The urban imprint on plant phenology. Nat. Ecol. Evol. 3, 1668–1674 (2019).

    Article  Google Scholar 

  32. 32.

    Hijmans, R. J. et al. raster: geographic data analysis and modeling. R package version 2.3-24 (2015).

  33. 33.

    R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2018).

  34. 34.

    Zuur, A., Ieno, E. N., Walker, N., Saveliev, A. A. & Smith, G. M. Mixed Effects Models and Extensions in Ecology with R (Springer Science & Business Media, 2009).

  35. 35.

    McDonald, J. H. Handbook of Biological Statistics Vol. 2 (Sparky House, 2009).

  36. 36.

    Fu, Y. H. et al. Daylength helps temperate deciduous trees to leaf-out at the optimal time. Glob. Change Biol. https://doi.org/10.1111/gcb.14633 (2019).

  37. 37.

    Riahi, K. et al. RCP 8.5—A scenario of comparatively high greenhouse gas emissions. Clim. Change 109, 33 (2011).

    CAS  Article  Google Scholar 

Download references

Acknowledgements

We acknowledge all members of the PEP725 network for collecting and providing the phenological data. We acknowledge the CESM-LENS Project and supercomputing resources provided by NSF/CISL/Yellowstone. This research was supported by the Strategic Priority Research Program of Chinese Academy of Sciences grant no. XDB31010300), the National Key Research and Development Program of China grant no. 2017YFC0505203, National Natural Science Foundation of China grant nos. 31590821 and 315611230010, the Starting Research Fund from Sichuan University grant no. 1082204112291 and the Fundamental Research Funds for the Central Universities of China grant no. SCU2019D013.

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L.C., J.L. and Z.L. designed this research. L.C. and Z.L. performed the data analysis. L.C. drafted the paper with the inputs of H.H., S. R., N.G.S., Z.L., S.P., G.F., J.G. and J.L. All authors contributed to the interpretation of the results and approved the final manuscript.

Corresponding authors

Correspondence to Lei Chen or Zhiyong Liu or Jianquan Liu.

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The authors declare no competing interests.

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

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Supplementary information

Supplementary Information

Supplementary Figs. 1–10 and Tables 1 and 2.

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Chen, L., Hänninen, H., Rossi, S. et al. Leaf senescence exhibits stronger climatic responses during warm than during cold autumns. Nat. Clim. Chang. 10, 777–780 (2020). https://doi.org/10.1038/s41558-020-0820-2

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