Letter | Published:

Ecosystem warming extends vegetation activity but heightens vulnerability to cold temperatures

Naturevolume 560pages368371 (2018) | Download Citation


Shifts in vegetation phenology are a key example of the biological effects of climate change1,2,3. However, there is substantial uncertainty about whether these temperature-driven trends will continue, or whether other factors—for example, photoperiod—will become more important as warming exceeds the bounds of historical variability4,5. Here we use phenological transition dates derived from digital repeat photography6 to show that experimental whole-ecosystem warming treatments7 of up to +9 °C linearly correlate with a delayed autumn green-down and advanced spring green-up of the dominant woody species in a boreal PiceaSphagnum bog. Results were confirmed by direct observation of both vegetative and reproductive phenology of these and other bog plant species, and by multiple years of observations. There was little evidence that the observed responses were constrained by photoperiod. Our results indicate a likely extension of the period of vegetation activity by 1–2 weeks under a ‘CO2 stabilization’ climate scenario (+2.6 ± 0.7 °C), and 3–6 weeks under a ‘high-CO2 emission’ scenario (+5.9 ± 1.1 °C), by the end of the twenty-first century. We also observed severe tissue mortality in the warmest enclosures after a severe spring frost event. Failure to cue to photoperiod resulted in precocious green-up and a premature loss of frost hardiness8, which suggests that vulnerability to spring frost damage will increase in a warmer world9,10. Vegetation strategies that have evolved to balance tradeoffs associated with phenological temperature tracking may be optimal under historical climates, but these strategies may not be optimized for future climate regimes. These in situ experimental results are of particular importance because boreal forests have both a circumpolar distribution and a key role in the global carbon cycle11.

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  1. 1.

    Settele, J. et al. in Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (eds. Field, C. B. et al.) 271–359 (Cambridge Univ. Press, Cambridge, 2014).

  2. 2.

    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).

  3. 3.

    Morisette, J. T. et al. Tracking the rhythm of the seasons in the face of global change: phenological research in the 21st century. Front. Ecol. Environ. 7, 253–260 (2009).

  4. 4.

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

  5. 5.

    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).

  6. 6.

    Sonnentag, O. et al. Digital repeat photography for phenological research in forest ecosystems. Agric. For. Meteorol. 152, 159–177 (2012).

  7. 7.

    Hanson, P. J. et al. Attaining whole-ecosystem warming using air and deep-soil heating methods with an elevated CO2 atmosphere. Biogeosciences 14, 861–883 (2017).

  8. 8.

    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).

  9. 9.

    Augspurger, C. K. Reconstructing patterns of temperature, phenology, and frost damage over 124 years: spring damage risk is increasing. Ecology 94, 41–50 (2013).

  10. 10.

    Gu, L. et al. The 2007 Eastern US spring freeze: increased cold damage in a warming world. Bioscience 58, 253–262 (2008).

  11. 11.

    Bonan, G. B. Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science 320, 1444–1449 (2008).

  12. 12.

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

  13. 13.

    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).

  14. 14.

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

  15. 15.

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

  16. 16.

    Migliavacca, M. et al. On the uncertainty of phenological responses to climate change, and implications for a terrestrial biosphere model. Biogeosciences 9, 2063–2083 (2012).

  17. 17.

    Slaney, M., Wallin, G., Medhurst, J. & Linder, S. Impact of elevated carbon dioxide concentration and temperature on bud burst and shoot growth of boreal Norway spruce. Tree Physiol. 27, 301–312 (2007).

  18. 18.

    Gunderson, C. A. et al. Forest phenology and a warmer climate – growing season extension in relation to climatic provenance. Glob. Change Biol. 18, 2008–2025 (2012).

  19. 19.

    Stinziano, J. R., Hüner, N. P. A. & Way, D. A. Warming delays autumn declines in photosynthetic capacity in a boreal conifer, Norway spruce (Picea abies). Tree Physiol. 35, 1303–1313 (2015).

  20. 20.

    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).

  21. 21.

    Morin, X., Roy, J., Sonié, L. & Chuine, I. Changes in leaf phenology of three European oak species in response to experimental climate change. New Phytol. 186, 900–910 (2010).

  22. 22.

    Brekke, L., Thrasher, B., Maurer, E. & Pruitt, T. Downscaled CMIP3 and CMIP5 Climate Projections (US Department of the Interior, Bureau of Reclamation, Technical Services Center, 2013).

  23. 23.

    Peichl, M., Sonnentag, O. & Nilsson, M. B. Bringing color into the picture: using digital repeat photography to investigate phenology controls of the carbon dioxide exchange in a boreal mire. Ecosystems 18, 115–131 (2015).

  24. 24.

    Bowling, D. R. et al. Limitations to winter and spring photosynthesis of a Rocky Mountain subalpine forest. Agric. For. Meteorol. 252, 241–255 (2018).

  25. 25.

    Richardson, A. D. et al. Influence of spring phenology on seasonal and annual carbon balance in two contrasting New England forests. Tree Physiol. 29, 321–331 (2009).

  26. 26.

    Wilson, R. M. et al. Stability of peatland carbon to rising temperatures. Nat. Commun. 7, 13723 (2016).

  27. 27.

    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).

  28. 28.

    Zohner, C. M. & Renner, S. S. Perception of photoperiod in individual buds of mature trees regulates leaf-out. New Phytol. 208, 1023–1030 (2015).

  29. 29.

    Hufkens, K. et al. Ecological impacts of a widespread frost event following early spring leaf-out. Glob. Change Biol. 18, 2365–2377 (2012).

  30. 30.

    Kolka, R., Sebestyen, S., Verry, E. S. & Brooks, K. Peatland Biogeochemistry and Watershed Hydrology at the Marcell Experimental Forest (CRC, Boca Raton, 2011).

  31. 31.

    Richardson, A. D. et al. Use of digital webcam images to track spring green-up in a deciduous broadleaf forest. Oecologia 152, 323–334 (2007).

  32. 32.

    Brown, T. B. et al. Using phenocams to monitor our changing Earth: toward a global phenocam network. Front. Ecol. Environ. 14, 84–93 (2016).

  33. 33.

    Keenan, T. F. et al. Tracking forest phenology and seasonal physiology using digital repeat photography: a critical assessment. Ecol. Appl. 24, 1478–1489 (2014).

  34. 34.

    Richardson, A. D., Klosterman, S. & Toomey, M. in Phenology: An Integrative Environmental Science (ed. Schwartz, M. D.) 413–430 (Springer, Dordrecht, 2013).

  35. 35.

    Toomey, M. et al. Greenness indices from digital cameras predict the timing and seasonal dynamics of canopy-scale photosynthesis. Ecol. Appl. 25, 99–115 (2015).

  36. 36.

    Richardson, A. D. et al. Tracking vegetation phenology across diverse North American biomes using PhenoCam imagery. Sci. Data 5, 180028 (2018).

  37. 37.

    Petach, A. R., Toomey, M., Aubrecht, D. M. & Richardson, A. D. Monitoring vegetation phenology using an infrared-enabled security camera. Agric. For. Meteorol. 195–196, 143–151 (2014).

  38. 38.

    Richardson, A. D., Braswell, B. H., Hollinger, D. Y., Jenkins, J. P. & Ollinger, S. V. Near-surface remote sensing of spatial and temporal variation in canopy phenology. Ecol. Appl. 19, 1417–1428 (2009).

  39. 39.

    Hanson, P., Riggs, J., Nettles, W., Krassovski, M. & Hook, L. SPRUCE Whole Ecosystems Warming (WEW) Environmental Data Beginning August 2015 https://doi.org/10.3334/CDIAC/spruce.032 (2016).

  40. 40.

    Maurer, E. P., Brekke, L., Pruitt, T. & Duffy, P. B. Fine-resolution climate projections enhance regional climate change impact studies. Eos 88, 504 (2007).

  41. 41.

    Riahi, K. et al. RCP 8.5—a scenario of comparatively high greenhouse gas emissions. Clim. Change 109, 33–57 (2011).

  42. 42.

    Thomson, A. M. et al. RCP4.5: a pathway for stabilization of radiative forcing by 2100. Clim. Change 109, 77–94 (2011).

  43. 43.

    Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference: a Practical Information–Theoretic Approach (Springer, New York, 2002).

  44. 44.

    Richardson, A. D. et al. SPRUCE Ground Observations of Phenology in Experimental Plots 2016–2017 https://doi.org/10.3334/CDIAC/spruce.044 (2018).

  45. 45.

    Richardson, A. D. et al. SPRUCE Vegetation Phenology in Experimental Plots from Phenocam Imagery 2015–2017 https://doi.org/10.3334/CDIAC/spruce.045 (2018).

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This material is based upon work supported by the US Department of Energy (DOE), Office of Science, Office of Biological and Environmental Research. Oak Ridge National Laboratory is managed by UT-Battelle, LLC, for DOE under contract DE-AC05-00OR22725. Support for PhenoCam has come from the National Science Foundation (EF-1065029, EF-1702697). D. Hollinger, M. Carbone and C. Iverson provided feedback on a draft manuscript. E. Ward assisted with litter collection. For CMIP, we acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling. We thank the climate modelling groups (listed in Supplementary Note 3) for making their model output available. DOE’s Program for Climate Model Diagnosis and Intercomparison additionally provides coordinating support and led development of software infrastructure for CMIP in partnership with the Global Organization for Earth System Science Portals.

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Nature thanks M. Tjoelker and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Author information


  1. Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA

    • Andrew D. Richardson
    • , Koen Hufkens
    • , Donald M. Aubrecht
    • , Morgan E. Furze
    •  & Bijan Seyednasrollah
  2. School of Informatics, Computing and Cyber Systems, Northern Arizona University, Flagstaff, AZ, USA

    • Andrew D. Richardson
    •  & Bijan Seyednasrollah
  3. Center for Ecosystem Science and Society, Northern Arizona University, Flagstaff, AZ, USA

    • Andrew D. Richardson
    •  & Bijan Seyednasrollah
  4. Institute for the Study of Earth, Oceans and Space, University of New Hampshire, Durham, NH, USA

    • Thomas Milliman
  5. Climate Change Science Institute and Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA

    • Misha B. Krassovski
    • , John M. Latimer
    • , W. Robert Nettles
    • , Ryan R. Heiderman
    • , Jeffrey M. Warren
    •  & Paul J. Hanson


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A.D.R. designed the study with input from P.J.H. A.D.R., K.H., D.M.A., T.M., M.E.F., B.S. and M.B.K. contributed PhenoCam imagery and derived data. J.M.L., W.R.N., J.M.W. and R.R.H. contributed phenological observations. J.M.W. contributed data on frost damage. M.B.K., W.R.N. and P.J.H. maintained site infrastructure including warming treatments and meteorological observations. A.D.R. assembled datasets and conducted the analysis. A.D.R. drafted the manuscript. All authors commented on and approved the final manuscript.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Andrew D. Richardson.

Extended data figures and tables

  1. Extended Data Fig. 1 Air temperature and precipitation in the SPRUCE S1 bog (August 2015 to December 2017) relative to long-term (1960–2000) means and variability.

    a, Long-term daily mean temperature (°C, ± 1 s.d. indicated by shading), compared with daily mean temperature (calculated from 30-min means, based on n = 2 sensors mounted at 2-m height in each enclosure) in a +0 °C enclosure (unheated control) and a +9.0 °C enclosure. b, Long-term monthly mean temperature (mean daily maximum and mean daily minimum indicated by shaded bars), compared with monthly mean temperature (calculated from daily means, as in a) in different experimental treatments. c, Long-term monthly mean precipitation (mm, ± 1 s.d. indicated by shading, with maxima and minima indicated by dotted lines), compared with measured monthly precipitation (n = 1 rain gauge) in the S1 bog. Source Data

  2. Extended Data Fig. 2 Decadal mean temperature change (relative to 2006–2015 mean) projections from ten CMIP5 earth system models for the SPRUCE site.

    a, Stabilization climate scenario (RCP4.5). b, High emission climate scenario (RCP8.5). Source Data

  3. Extended Data Fig. 3 Relationships between air temperature and the start and end of the photosynthetic uptake period, as derived from FLUXNET data for evergreen conifer-dominated sites.

    ad, Across-site patterns in spring (a) and autumn (b) in relation to mean annual temperature (n = 12 sites), and within-sites patterns in spring (c) and autumn (d) in relation to seasonal temperature anomalies (n = 86 site-years). Source Data

  4. Extended Data Fig. 4 Unusually warm weather in late winter, followed by extreme cold in early April, resulted in severe frost damage in the warmest enclosures at SPRUCE in 2016.

    a, Time series of daily mean air temperature, comparing plot 17 (+9.0 °C warming) and plot 19 (unheated enclosure), during the winter and spring of 2016. By the time the frost event occurred (grey shading), the daily mean temperature in plot 17 had been above freezing for over a month, but had repeatedly dropped below freezing in plot 19. b, Time series of 30-min air temperature—again comparing plot 17 and plot 19—leading up to and immediately following the frost event, which occurred on the morning of 9 April and again on 12 April. The thin red lines indicate the variability (maximum and minimum) across n = 5 temperature sensors in plot 17. c, Time series of daily GCC, the green chromatic coordinate, for Picea trees in plot 17 and plot 19. Arrows denote spring green-up dates (progressively larger arrows corresponding to 10%, 25% and 50% of seasonal amplitude) estimated from GCC. The pronounced decline in GCC in plot 17 following the frost event (grey shading) is readily apparent. Trees in plot 19 retained sufficient frost hardiness that they were undamaged, despite experiencing much colder temperatures. d, Brown frost-damaged Larix foliage in plot 17. e, Picea branches in plot 17, showing loss of most foliage from previous years, with green foliage from the 2015 flush retained only at branch tips. f, Picea branches with frost-damaged foliage from previous years, but healthy green foliage from the 2016 flush. Source Data

  5. Extended Data Table 1 Mean daily air temperature and temperature differentials associated with whole-ecosystem warming
  6. Extended Data Table 2 Effect of SPRUCE warming treatments on spring green-up and autumn green-down
  7. Extended Data Table 3 Projected future extension of the period of vegetation activity
  8. Extended Data Table 4 Effect of SPRUCE warming treatments on observed vegetative and reproductive phenological transitions (2016)
  9. Extended Data Table 5 Effect of SPRUCE warming treatments on observed vegetative and reproductive phenological transitions (2017)
  10. Extended Data Table 6 Impact of premature foliar senescence on nutrient content of L. laricina and P. mariana litter

Supplementary information

  1. Supplementary Information

    This file contains Supplementary Notes 1-6 and associated references.

  2. Reporting Summary

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