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.

  • Brief Communication
  • Published:

Global buffering of temperatures under forest canopies

Abstract

Macroclimate warming is often assumed to occur within forests despite the potential for tree cover to modify microclimates. Here, using paired measurements, we compared the temperatures under the canopy versus in the open at 98 sites across 5 continents. We show that forests function as a thermal insulator, cooling the understory when ambient temperatures are hot and warming the understory when ambient temperatures are cold. The understory versus open temperature offset is magnified as temperatures become more extreme and is of greater magnitude than the warming of land temperatures over the past century. Tree canopies may thus reduce the severity of warming impacts on forest biodiversity and functioning.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Forests buffer temperatures under canopies globally.
Fig. 2: Forest temperature offsets under canopies are negatively related to warming air temperatures and dependent on the biome.

Similar content being viewed by others

Data availability

The datasets and code generated and analysed during the current study are available in the figshare repository37, with the identifier 10.6084/m9.figshare.7604849.

References

  1. Willis, K. J. & Bhagwat, S. A. Science 326, 806–807 (2009).

    CAS  PubMed  Google Scholar 

  2. Scheffers, B. R. et al. Science 354, aaf7671 (2016).

    PubMed  Google Scholar 

  3. Lenoir, J. & Svenning, J. C. Ecography 38, 15–28 (2015).

    Google Scholar 

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

  5. Moritz, C. & Agudo, R. Science 341, 504–508 (2013).

    CAS  PubMed  Google Scholar 

  6. Devictor, V. et al. Nat. Clim. Change 2, 121–124 (2012).

    Google Scholar 

  7. Dullinger, S. et al. Nat. Clim. Change 2, 619–622 (2012).

    Google Scholar 

  8. Bertrand, R. et al. Nature 479, 517–520 (2011).

    CAS  PubMed  Google Scholar 

  9. Ash, J. D., Givnish, T. J. & Waller, D. M. Glob. Change Biol. 23, 1305–1315 (2017).

    Google Scholar 

  10. De Frenne, P. et al. Proc. Natl Acad. Sci. USA 110, 18561–18565 (2013).

    PubMed  Google Scholar 

  11. Scheffers, B. R. et al. Glob. Change Biol. 20, 495–503 (2013).

    Google Scholar 

  12. Senior, R. A. et al. Glob. Change Biol. 24, 1267–1278 (2018).

    Google Scholar 

  13. Frey, S. J. K. et al. Sci. Adv. 2, e1501392 (2016).

    PubMed  PubMed Central  Google Scholar 

  14. Dobrowski, S. Z. Glob. Change Biol. 17, 1022–1035 (2011).

    Google Scholar 

  15. Potter, K. A., Arthur, W. H. & Pincebourde, S. Glob. Change Biol. 19, 2932–2939 (2013).

    Google Scholar 

  16. Lenoir, J., Hattab, T. & Pierre, G. Ecography 40, 253–266 (2017).

    Google Scholar 

  17. Bramer, I. et al. in Advances in Ecological Research Vol. 58 (eds Bohan, D. A. et al.) 101–161 (Elsevier, 2018).

  18. Geiger, R. Aron, R. H. & Todhunter, P. The Climate Near the Ground 7th edn (Rowman & Littlefield, 2009).

  19. Guide to Meteorological Instruments and Methods of Observation WMO report No. 8 (World Meteorological Organization, 2008).

  20. De Frenne, P. & Verheyen, K. Science 351, 234 (2016).

    PubMed  Google Scholar 

  21. Jenkins, C. N., Pimm, S. L. & Joppa, L. N. Proc. Natl Acad. Sci. USA 110, E2602–E2610 (2013).

    CAS  PubMed  Google Scholar 

  22. Millennium Ecosystem Assessment Ecosystems and Human Well-being: Biodiversity Synthesis (World Resources Institute, 2005).

  23. Global Forest Resources Assessment (FAO, 2015).

  24. Jucker, T. et al. Glob. Change Biol. 24, 5243–5258 (2018).

    Google Scholar 

  25. Mayhew, P. J., Jenkins, G. B. & Benton, T. G. Proc. R. Soc. B. 275, 47–53 (2008).

    PubMed  Google Scholar 

  26. Lejeune, Q. et al. Nat. Clim. Change 8, 386–390 (2018).

    Google Scholar 

  27. Hansen, M. C. et al. Science 342, 850–853 (2013).

    CAS  PubMed  Google Scholar 

  28. Watson, J. E. M. et al. Nat. Ecol. Evol. 2, 599–610 (2018).

    PubMed  Google Scholar 

  29. Good, S. P., Noone, D. & Bowen, G. Science 349, 175–177 (2015).

    CAS  PubMed  Google Scholar 

  30. Wickham, H. & Bryan, J. Readxl: read excel files. R package v1.0.0. https://CRAN.R-project.org/package=readxl (2017).

  31. Wickham, H. et al. Dplyr: a grammar of data manipulation. R package v0.7.4. https://CRAN.R-project.org/package=dplyr (2017).

  32. Zizka, A. CoordinateCleaner: automated cleaning of occurrence records from biological collections. R package v1.0.7. https://CRAN.R-project.org/package=CoordinateCleaner (2018).

  33. Xie, Y. Knitr: a general-purpose package for dynamic report generation in R. R package v1.2.0. https://yihui.name/knitr/ (2018).

  34. Allaire, J. J. et al. Rmarkdown: dynamic documents for R. R package v1.9. https://CRAN.R-project.org/package=rmarkdown (2018).

  35. Wickham, H Ggplot2: Elegant Graphics for Data Analysis (Springer, 2009).

  36. Wilke, C. Cowplot: streamlined plot theme and plot annotations for ‘Ggplot2’. R package v0.9.2. https://CRAN.R-project.org/package=cowplot (2017).

  37. De Frenne, P., Lenoir, J. & Rodríguez-Sánchez, F. Global buffering of temperatures under forest canopies data and code. Figshare https://doi.org/10.6084/m9.figshare.7604849 (2019).

  38. Amatulli, G. et al. Sci. Data 5, 180040 (2018).

    PubMed  PubMed Central  Google Scholar 

  39. Fick, S. E. & Hijmans, R. J. Int. J. Clim. 37, 4302–4315 (2017).

    Google Scholar 

  40. Bates, D. J. Stat. Softw. 67, 1–48 (2015).

    Google Scholar 

  41. Zuur, A. F. et al. Mixed Effects Models and Extensions in Ecology with R (Springer, 2009).

  42. Gurevitch, J. et al. Nature 555, 175–182 (2018).

    CAS  PubMed  Google Scholar 

  43. Nakagawa, S. & Schielzeth, H. Methods Ecol. Evol. 4, 133–142 (2013).

    Google Scholar 

  44. Barton, K. MuMIn: Multi-Model Inference. R package v1.40.4. https://CRAN.R-project.org/package=MuMIn (2018).

  45. Wood, S. N. Generalized Additive Models: An Introduction with R 2nd edn (Chapman and Hall/CRC, 2017).

  46. Zhu, H., Xu, Z. F., Wang, H. & Li, B. G. Biodivers. Conserv. 13, 1355–1372 (2004).

    Google Scholar 

  47. R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2017); http://www.R-project.org/

  48. André, M. F. et al. Earth Surf. Process. Landf. 37, 519–532 (2012).

    Google Scholar 

  49. Arunachalam, A. & Arunachalam, K. Plant Soil 223, 187–195 (2000).

    Google Scholar 

  50. Asbjornsen, H., Ashton, M. S., Vogt, D. J. & Palacios, S. Agric. Ecosyst. Environ. 103, 481–495 (2004).

    Google Scholar 

  51. Barg, A. K. & Edmonds, R. L. Can. J. For. Res. 29, 705–713 (1999).

  52. Belsky, A. J. et al. J. Appl. Ecol. 26, 1005–1024 (1989).

    Google Scholar 

  53. Blennow, K. Agric. For. Meteorol. 91, 223–235 (1998).

    Google Scholar 

  54. Brower, L. P. et al. Insect Conserv. Divers. 2, 163–175 (2009).

    Google Scholar 

  55. Cachan, P. Ann. Fac. Sci. Dakar 8, 89–155 (1963).

    Google Scholar 

  56. Carlson, D. W. & Groot, A. Agric. For. Meteorol. 87, 313–329 (1997).

    Google Scholar 

  57. Chen, J., Franklin, J. F. & Spies, T. A. Agric. For. Meteorol. 63, 219–237 (1993).

    Google Scholar 

  58. Chen, J. et al. Bioscience 49, 288–297 (1999).

    Google Scholar 

  59. Childs, S. W. & Flint, L. E. For. Ecol. Manage. 18, 205–217 (1987).

    Google Scholar 

  60. Currylow, A. F., MacGowan, B. J. & Williams, R. N. PLoS ONE 7, e40473 (2012).

  61. Daily, G. C. & Ehrlich, P. R. Proc. Natl Acad. Sci. USA 93, 11709–11712 (1996).

  62. Davies-Colley, R. J., Payne, G. W. & van Elswijk, M. N. Z. J. Ecol. 24, 111–121 (2000).

    Google Scholar 

  63. Denslow, J. S. Biotropica 12, 47–55 (1980).

    Google Scholar 

  64. Didham, R. K. & Ewers, R. M. Pac. Sci. 68, 493–508 (2014).

    Google Scholar 

  65. Dovčiak, M. & Brown, J. New For. 45, 733–744 (2014).

    Google Scholar 

  66. Evans, G. C . J. Ecol. 27, 436–482 (1939).

    CAS  Google Scholar 

  67. Fetcher, N., Oberbauer, S. F. & Strain, B. R. Int. J. Biometeorol. 29, 145–155 (1985).

    Google Scholar 

  68. Fridley, J. D. J. Appl. Meteorol. Climatol. 48, 1033–1049 (2009).

    Google Scholar 

  69. Gaudio, N., Gendre, X., Saudreau, M., Seigner, V. & Balandier, P. Agric. For. Meteorol. 237-238, 71–79 (2017).

    Google Scholar 

  70. Ghuman, B. S. & Lal, R. Agric. For. Meteorol. 40, 17–29 (1987).

    Google Scholar 

  71. Graae, B. J. et al. Oikos 121, 3–19 (2012).

    Google Scholar 

  72. Granberg, H. B., Ottosson-Löfvenius, M. & Odin, H. Agric. For. Meteorol. 63, 171–188 (1993).

    Google Scholar 

  73. Groot, A. & Carlson, D. W. Can. J. For. Res. 26, 1531–1538 (1996).

  74. Grubb, P. J. & Whitmore, T. C. J. Ecol. 54, 303–333 (1966).

  75. Heithecker, T. D. & Halpern, C. B. For. Ecol. Manage. 248, 163–173 (2007).

  76. Holl, K. D. Biotropica 31, 229–242 (1999).

  77. Honnay, O., Verheyen, K. & Hermy, M. For. Ecol. Manage. 161, 109–122 (2002).

  78. Hopkins, B. J. Ecol. 53, 125–138 (1965).

    Google Scholar 

  79. Ibanez, T., Hély, C. & Gaucherel, C. Austral. Ecol. 38, 680–687 (2013).

    Google Scholar 

  80. Jiménez, C., Tejedor, M. & Rodríguez, M. Eur. J. Soil Sci. 58, 445–449 (2007).

    Google Scholar 

  81. Johansson, D. Acta Phytogeogr. Suec. 59, 1–136 (1974).

    Google Scholar 

  82. Joly, D. Climatologie 11, 19–33 (2014).

    Google Scholar 

  83. Karki, U. & Goodman, M. S. Agrofor. Syst. 89, 319–325 (2015).

    Google Scholar 

  84. Korb, J. & Linsenmair, K. E. Insectes Soc. 45, 51–65 (1998).

    Google Scholar 

  85. Kubin, E. & Kemppainen, L. Acta For. Fenn. 225, (1991).

  86. Lal, R. & Cummings, D. J. F. Crop. Res 2, 91–107 (1979).

    Google Scholar 

  87. Langvall, O. & Ottosson Löfvenius, M. For. Ecol. Manage. 168, 149–161 (2002).

    Google Scholar 

  88. Latimer, C. E. & Zuckerberg, B. Ecography 40, 158–170 (2017).

    Google Scholar 

  89. Lawson, G. W., Armstrong-Mensah, K. O. & Hall, J. B. J. Ecol. 58, 371–398 (1970).

    Google Scholar 

  90. Locosselli, G. M., Cardim, R. H. & Ceccantini, G. Int. J. Biometeorol. 60, 639–649 (2016).

    PubMed  Google Scholar 

  91. Lofvenius, M. O. Temperature and radiation regimes in pine shelterwood and clear-cut area. PhD thesis, Swedish University of Agricultural Sciences (1993).

  92. Lüdi, W. & Zoller, H. Über den Einfluss der Waldnähe auf das Lokalklima: Untersuchungen im Gebiete des Hardwaldes bei Muttenz (Base) (in German) (Geobotanisches Forschungsinstitut Rübel Zürich, 2018).

  93. Luskin, M. S. & Potts, M. D. Basic Appl. Ecol. 12, 540–551 (2011).

    Google Scholar 

  94. Matlack, G. R. Biol. Conserv. 66, 185–194 (1993).

    Google Scholar 

  95. Meleason, M. A. & Quinn, J. M. For. Ecol. Manage. 191, 365–371 (2004).

    Google Scholar 

  96. Morecroft, M. D., Taylor, M. E. & Oliver, H. R. Agric. For. Meteorol. 90, 141–156 (1998).

    Google Scholar 

  97. Nunez, M. & Bowman, D. M. J. S. Aust. For. Res. 16, 185–197 (1986).

    Google Scholar 

  98. Odin, H., Magnusson, B. & Bäckström, P.-O. Effect of low shelterwood on minimum temperature near the ground. in Ecology and Management of Forest Biomass Production Systems (Perttu, K. ed.) 77–99 (Swedish Unioersity of Agricultural Sciences, Department of Ecology and Environmental Research, Report 15, 1984).

  99. Porté, A., Huard, F. & Dreyfus, P. Agric. For. Meteorol. 126, 175–182 (2004).

    Google Scholar 

  100. Potter, B. E., Teclaw, R. M. & Zasada, J. C. Agric. For. Meteorol. 106, 331–336 (2001).

    Google Scholar 

  101. Renaud, V., Innes, J. L., Dobbertin, M. & Rebetez, M. Theor. Appl. Climatol. 105, 119–127 (2011).

    Google Scholar 

  102. Rodríguez-Sánchez, F., Pérez-Barrales, R., Ojeda, F., Vargas, P. & Arroyo, J. Quat. Sci. Rev. 27, 2100–2117 (2008).

    Google Scholar 

  103. Scheffers, B. R. et al. Proc. R. Soc. B Biol. Sci. 280, 20131581 (2013).

  104. Schulz, J. P. Meded. Bot. Museum en Herb. R.U.U. 163, 1–267 (1960).

    Google Scholar 

  105. Seebacher, F. & Alfrod, R. A. J. Herpetol. 36, 95–98 (2002).

    Google Scholar 

  106. Shanks, R. E. & Norris, F. H. Ecology 31, 532–539 (1950).

    Google Scholar 

  107. Shoo, L. P., Storlie, C., Williams, Y. M. & Williams, S. E. Int. J. Biometeorol. 54, 475–478 (2010).

    PubMed  Google Scholar 

  108. Sporn, S. G., Bos, M. M., Kessler, M. & Gradstein, S. R. Biodivers. Conserv. 19, 745–760 (2010).

    Google Scholar 

  109. Suggitt, A. J. et al. Oikos 120, 1–8 (2011).

    Google Scholar 

  110. Vajda, A. & Venäläinen, A. Boreal Environ. Res. 10, 299–314 (2005).

    Google Scholar 

  111. Valigura, R. A. J. Environ. Manage. 40, 283–295 (1994).

    Google Scholar 

  112. van Dam, O. Forest filled with gaps: Effects of gap size on water and nutrient cycling in tropical rain forest. PhD thesis, Utrecht University, 2001.

  113. Varner, J. & Dearing, M. D. PLoS One 9, e104648 (2014).

  114. Vitt, L. & Avila-Pires, T. Conserv. Biol. 12, 654–664 (1998).

    Google Scholar 

  115. Williams-Linera, G. J. Ecol. 78, 356–373 (1990).

    Google Scholar 

  116. Xu, M., Qi, Y., Chen, J. & Song, B. Plant Ecol. 173, 39–57 (2004).

    Google Scholar 

  117. Yan, M., Zhong, Z. & Liu, J. Front. Biol. China 2, 62–68 (2007).

    Google Scholar 

  118. Yanoviak, S. P. Selbyana 20, 106–115 (1999).

    Google Scholar 

  119. Young, A. & Mitchell, N. 67, 63–72 (1994).

  120. Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G. & The PRISMA Group PLoS Med. 6, e1000097 (2009).

Download references

Acknowledgements

P.D.F. received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (ERC Starting Grant FORMICA 757833). K.V. received funding through ERC Consolidator Grant PASTFORWARD 614839. F.R.-S. was funded by a postdoctoral fellowship from the Spanish Ministry of Economy and Competitiveness (FPD-2013-16756). F.Z. was funded by the Swiss National Science Foundation (project 172198). M.V. was funded by the Natural Sciences and Engineering Research Council, Canada.

Author information

Authors and Affiliations

Authors

Contributions

P.D.F., F.Z. and J.L. conceived and designed the research. P.D.F., F.Z., J.L. and F.R.-S. assembled and revised the database and analysed the data. All authors compiled data and wrote the manuscript.

Corresponding author

Correspondence to Pieter De Frenne.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

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

Supplementary information

Supplementary Information

Supplementary Figures 1–8 and Supplementary Tables 1–8

Reporting Summary

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

De Frenne, P., Zellweger, F., Rodríguez-Sánchez, F. et al. Global buffering of temperatures under forest canopies. Nat Ecol Evol 3, 744–749 (2019). https://doi.org/10.1038/s41559-019-0842-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41559-019-0842-1

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing