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.

Temperature thresholds of ecosystem respiration at a global scale

Nature Ecology & Evolution volume 5, pages 487–494 (2021)Cite this article

Subjects

Abstract

Ecosystem respiration is a major component of the global terrestrial carbon cycle and is strongly influenced by temperature. The global extent of the temperature–ecosystem respiration relationship, however, has not been fully explored. Here, we test linear and threshold models of ecosystem respiration across 210 globally distributed eddy covariance sites over an extensive temperature range. We find thresholds to the global temperature–ecosystem respiration relationship at high and low air temperatures and mid soil temperatures, which represent transitions in the temperature dependence and sensitivity of ecosystem respiration. Annual ecosystem respiration rates show a markedly reduced temperature dependence and sensitivity compared to half-hourly rates, and a single mid-temperature threshold for both air and soil temperature. Our study indicates a distinction in the influence of environmental factors, including temperature, on ecosystem respiration between latitudinal and climate gradients at short (half-hourly) and long (annual) timescales. Such climatological differences in the temperature sensitivity of ecosystem respiration have important consequences for the terrestrial net carbon sink under ongoing climate change.

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

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Learn more

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

Fig. 1: Global distribution of the FLUXNET sites.
Fig. 2: Global extent of the temperature–ecosystem respiration (Re) relationship.
Fig. 3: The global soil temperature–ecosystem respiration relationship.
Fig. 4: Temperature thresholds of ecosystem respiration (Re) across five climates.
Fig. 5: Long-term temperature thresholds of ecosystem respiration (Re).

Data availability

The data analysed during the current study are available on the FLUXNET website (https://fluxnet.fluxdata.org/data/fluxnet2015-dataset/) and are subject to data policy restrictions (https://fluxnet.org/data/data-policy). Summaries for each FLUXNET site are provided in Supplementary Data 1.

Code availability

The R code used for analysis during the current study is available on Zenodo (https://doi.org/10.5281/zenodo.4506798).

References

  1. Cao, M. & Woodward, F. I. Dynamic responses of terrestrial ecosystem carbon cycling to global climate change. Nature 393, 249–252 (1998).

    Article  CAS  Google Scholar 

  2. Heimann, M. & Reichstein, M. Terrestrial ecosystem carbon dynamics and climate feedbacks. Nature 451, 289–292 (2008).

    Article  CAS  Google Scholar 

  3. Allen, A. P., Gillooly, J. F. & Brown, J. H. Linking the global carbon cycle to individual metabolism. Funct. Ecol. 19, 202–213 (2005).

    Article  Google Scholar 

  4. Enquist, B. J. et al. Scaling metabolism from organisms to ecosystems. Nature 423, 639–642 (2003).

    Article  CAS  Google Scholar 

  5. Gillooly, J. F., Brown, J. H., West, G. B., Savage, V. M. & Charnov, E. L. Effects of size and temperature on metabolic rate. Science 293, 2248–2251 (2001).

    Article  CAS  Google Scholar 

  6. Brown, J. H., Gillooly, J. F., Allen, A. P., Savage, V. M. & West, G. B. Toward a metabolic theory of ecology. Ecology 85, 1771–1789 (2004).

    Article  Google Scholar 

  7. Friedlingstein, P. et al. Uncertainties in CMIP5 climate projections due to carbon cycle feedbacks. J. Clim. 27, 511–526 (2014).

    Article  Google Scholar 

  8. Davidson, E. A. & Janssens, I. A. Temperature sensitivity of soil carbon decomposition and feedbacks to climate change. Nature 440, 165–173 (2006).

    Article  CAS  Google Scholar 

  9. Lenton, T. M. & Huntingford, C. Global terrestrial carbon storage and uncertainties in its temperature sensitivity examined with a simple model. Glob. Change Biol. 9, 1333–1352 (2003).

    Article  Google Scholar 

  10. Song, B. et al. Divergent apparent temperature sensitivity of terrestrial ecosystem respiration. J. Plant Ecol. 7, 419–428 (2014).

    Article  Google Scholar 

  11. Lloyd, J. & Taylor, J. A. On the temperature dependence of soil respiration. Funct. Ecol. 8, 315–323 (1994).

  12. Mahecha, M. D. et al. Global convergence in the temperature sensitivity of respiration at ecosystem level. Science 329, 838–840 (2010).

    Article  CAS  Google Scholar 

  13. Yvon-Durocher, G. et al. Reconciling the temperature dependence of respiration across timescales and ecosystem types. Nature 487, 472–476 (2012).

    Article  CAS  Google Scholar 

  14. Johnston, A. S. A. & Sibly, R. M. The influence of soil communities on the temperature sensitivity of soil respiration. Nat. Ecol. Evol. 2, 1597–1602 (2018).

    Article  Google Scholar 

  15. Dell, A. I., Pawar, S. & Savage, V. M. Systematic variation in the temperature dependence of physiological and ecological traits. Proc. Natl Acad. Sci. USA 108, 10591–10596 (2011).

    Article  CAS  Google Scholar 

  16. Buckley, L. B. & Huey, R. B. Temperature extremes: geographic patterns, recent changes, and implications for organismal vulnerabilities. Glob. Change Biol. 22, 3829–3842 (2016).

    Article  Google Scholar 

  17. Gill, A. L. & Finzi, A. C. Belowground carbon flux links biogeochemical cycles and resource-use efficiency at the global scale. Ecol. Lett. 19, 1419–1428 (2016).

    Article  Google Scholar 

  18. Green, J. K. et al. Large influence of soil moisture on long-term terrestrial carbon uptake. Nature 565, 476–479 (2019).

    Article  CAS  Google Scholar 

  19. Allison, S. D., Wallenstein, M. D. & Bradford, M. A. Soil-carbon response to warming dependent on microbial physiology. Nat. Geosci. 3, 336–340 (2010).

    Article  CAS  Google Scholar 

  20. Michaletz, S. T., Cheng, D., Kerkhoff, A. J. & Enquist, B. J. Convergence of terrestrial plant production across global climate gradients. Nature 512, 39–43 (2014).

    Article  CAS  Google Scholar 

  21. Pastorello, G. et al. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data. Sci. Data 7, 225 (2020).

    Article  Google Scholar 

  22. Monson, R. K. et al. Winter forest soil respiration controlled by climate and microbial community composition. Nature 439, 711–714 (2006).

    Article  CAS  Google Scholar 

  23. Mauder, M. et al. A strategy for quality and uncertainty assessment of long-term eddy-covariance measurements. Agric. Meteorol. 169, 122–135 (2013).

    Article  Google Scholar 

  24. Kim, D.-G., Vargas, R., Bond-Lamberty, B. & Turetsky, M. R. Effects of soil rewetting and thawing on soil gas fluxes: a review of current literature and suggestions for future research. Biogeosciences 9, 2459–2483 (2012).

    Article  CAS  Google Scholar 

  25. Du, E. et al. Winter soil respiration during soil-freezing process in a boreal forest in Northeast China. J. Plant Ecol. 6, 349–357 (2013).

    Article  Google Scholar 

  26. Schuur, E. A. et al. Climate change and the permafrost carbon feedback. Nature 520, 171–179 (2015).

    Article  CAS  Google Scholar 

  27. Koven, C. D., Hugelius, G., Lawrence, D. M. & Wieder, W. R. Higher climatological temperature sensitivity of soil carbon in cold than warm climates. Nat. Clim. Change 7, 817–822 (2017).

    Article  CAS  Google Scholar 

  28. Bond-Lamberty, B. P. & Thomson, A. M. A Global Database of Soil Respiration Data Version 4.0 (ORNL DAAC, 2018); https://doi.org/10.3334/ORNLDAAC/1578

  29. Zhang, Z. et al. A temperature threshold to identify the driving climate forces of the respiratory process in terrestrial ecosystems. Eur. J. Soil Biol. 89, 1–8 (2018).

    Article  Google Scholar 

  30. Yang, Y., Donohue, R. J., McVicar, T. R., Roderick, M. L. & Beck, H. E. Long-term CO2 fertilization increases vegetation productivity and has little effect on hydrological partitioning in tropical rainforests. J. Geophys. Res. Biogeosci. 121, 2125–2140 (2016).

    Article  Google Scholar 

  31. Fleischer, K. et al. Amazon forest response to CO2 fertilization dependent on plant phosphorus acquisition. Nat. Geosci. 12, 736–741 (2019).

    Article  CAS  Google Scholar 

  32. Padfield, D. et al. Metabolic compensation constrains the temperature dependence of gross primary production. Ecol. Lett. 20, 1250–1260 (2017).

    Article  Google Scholar 

  33. Atkin, O. K. & Tjoelker, M. G. Thermal acclimation and the dynamic response of plant respiration to temperature. Trends Plant Sci. 8, 343–351 (2003).

    Article  CAS  Google Scholar 

  34. Huntingford, C. et al. Implications of improved representations of plant respiration in a changing climate. Nat. Commun. 8, 1602 (2017).

    Article  Google Scholar 

  35. Niu, S. et al. Thermal optimality of net ecosystem exchange of carbon dioxide and underlying mechanisms. New Phytol. 194, 775–783 (2012).

    Article  Google Scholar 

  36. Rind, D. The consequences of not knowing low- and high-latitude climate sensitivity. Bull. Am. Meteorol. Soc. 89, 855–864 (2008).

    Article  Google Scholar 

  37. Liu, Z. et al. Increased high-latitude photosynthetic carbon gain offset by respiration carbon loss during an anomalous warm winter to spring transition. Glob. Change Biol. 26, 682–696 (2020).

    Article  Google Scholar 

  38. Haverd, V. et al. Higher than expected CO2 fertilization inferred from leaf to global observations. Glob. Change Biol. 26, 2390–2402 (2020).

    Article  Google Scholar 

  39. Tagesson, T. et al. Recent divergence in the contributions of tropical and boreal forests to the terrestrial carbon sink. Nat. Ecol. Evol. 4, 202–209 (2020).

    Article  Google Scholar 

  40. Climate Research Unit, University of East Anglia Average Annual Temperature. Atlas Biosphere (Center for Sustainability and the Global Environment, accessed 6 February 2020); https://nelson.wisc.edu/sage/data-and-models/atlas/maps.php

Download references

Acknowledgements

This work used eddy covariance data acquired and shared by the FLUXNET community and was supported by a Leverhulme Trust Research Project Grant (RPG-2017-071) and a Leverhulme Trust Research Leadership Award (RL-2019-012) to C.V. A.M. was supported by BBSRC (BB/S019952/1) and the Leverhulme Trust (RPG-2019-170), P.D.B. by the US Department of Energy Office of Science (7094866), D.B. by French Agence Nationale de la Recherche (ANR-10-LABX-25-01; ANR-11-LABX-0002-01), J.D. by the Ministry of Education, Youth and Sports of the Czech Republic (LM2015061), C.G. by a National Science Foundation Award (1655095) and A.V. by Russian Foundation for Basic Research project 19-04-01234-a. We also thank J. Baker, G. Butler and A. Navarro Campoy for helpful discussions.

Author information

Authors and Affiliations

  1. School of Water, Energy and Environment, Cranfield University, Bedford, UK

    Alice S. A. Johnston

  2. School of Biological Sciences, University of Reading, Reading, UK

    Alice S. A. Johnston, Andrew Meade & Chris Venditti

  3. Physical Geography and Ecosystem Science, Lund University, Lund, Sweden

    Jonas Ardö & Torbern Tagesson

  4. Joint Research Centre, European Commission, Ispra, Italy

    Nicola Arriga, Alessandro Cescatti, Ignacio Goded & Giovanni Manca

  5. Faculty of Land and Food Systems, University of British Columbia, Vancouver, British Columbia, Canada

    Andy Black & Rachhpal Jassal

  6. Department of Geography, University of Colorado, Boulder, CO, USA

    Peter D. Blanken

  7. Université de Lorraine, AgroParisTech, INRAE, UMR Silva, Nancy, France

    Damien Bonal

  8. Thünen Institute of Climate-Smart Agriculture, Braunschweig, Germany

    Christian Brümmer

  9. Global Change Research Institute of the Czech Academy of Sciences, Brno, Czech Republic

    Jiří Dušek

  10. Institute for Bio- and Geosciences: Agrosphere (IBG-3), Forschungszentrum Jülich, Jülich, Germany

    Alexander Graf

  11. Institute of Bioeconomy, CNR, Firenze, Italy

    Beniamino Gioli

  12. Department of Biology, Virginia Commonwealth University, Richmond, VA, USA

    Christopher M. Gough

  13. Institute for Agro-Environmental Sciences, National Agriculture and Food Research Organization, Tsukuba, Japan

    Hiroki Ikawa

  14. Research Institute for Global Change, Institute of Arctic Climate and Environment Research, Japan Agency for Marine-Earth Science and Technology, Tsukuba, Japan

    Hideki Kobayashi

  15. Institute for Mediterranean Agriculture and Forest Systems, CNR, Ercolano, Italy

    Vincenzo Magliulo

  16. Autonomous Province of Bolzano, Forest Services, Bolzano, Italy

    Leonardo Montagnani

  17. Faculty of Science and Technology, Free University of Bolzano, Bolzano, Italy

    Leonardo Montagnani

  18. Bioclimatology, University of Goettingen, Göttingen, Germany

    Fernando E. Moyano

  19. Department of Agroecology, Aarhus University, Tjele, Denmark

    Jørgen E. Olesen

  20. GFZ German Research Centre for Geoscience, Potsdam, Germany

    Torsten Sachs

  21. Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China

    Changliang Shao

  22. Department of Geosciences and Natural Resources, University of Copenhagen, Copenhagen, Denmark

    Torbern Tagesson

  23. Department of Ecology, University of Innsbruck, Innsbruck, Austria

    Georg Wohlfahrt

  24. Department of Environmental Systems Science, ETH Zurich, Zurich, Switzerland

    Sebastian Wolf

  25. Land & Water, Commonwealth Scientific and Industrial Research Organisation, Canberra, Australian Capital Territory, Australia

    William Woodgate

  26. School of Earth and Environmental Sciences, The University of Queensland, Brisbane, Queensland, Australia

    William Woodgate

  27. A.N. Severtsov Institute of Ecology and Evolution, Russian Academy of Sciences, Moscow, Russia

    Andrej Varlagin

Authors
  1. Alice S. A. Johnston
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andrew Meade
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jonas Ardö
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Nicola Arriga
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Andy Black
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Peter D. Blanken
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Damien Bonal
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Christian Brümmer
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Alessandro Cescatti
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Jiří Dušek
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Alexander Graf
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Beniamino Gioli
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Ignacio Goded
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Christopher M. Gough
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Hiroki Ikawa
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Rachhpal Jassal
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Hideki Kobayashi
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Vincenzo Magliulo
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Giovanni Manca
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Leonardo Montagnani
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Fernando E. Moyano
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Jørgen E. Olesen
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Torsten Sachs
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Changliang Shao
    View author publications

    You can also search for this author in PubMed Google Scholar

  25. Torbern Tagesson
    View author publications

    You can also search for this author in PubMed Google Scholar

  26. Georg Wohlfahrt
    View author publications

    You can also search for this author in PubMed Google Scholar

  27. Sebastian Wolf
    View author publications

    You can also search for this author in PubMed Google Scholar

  28. William Woodgate
    View author publications

    You can also search for this author in PubMed Google Scholar

  29. Andrej Varlagin
    View author publications

    You can also search for this author in PubMed Google Scholar

  30. Chris Venditti
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

A.S.A.J. and C.V. developed the methodology and led the writing of the manuscript. A.S.A.J. and A.M. conducted the data analysis. J.A., N.A., D.B., A.B., P.D.B., C.B., A.C., J.D., A.G., B.G., I.G., C.M.G., H.I., R.J., H.K., V.M., G.M., L.M., F.E.M., J.E.O., T.S., C.S., T.T., G.W., S.W., W.W. and A.V. contributed data. All authors contributed to manuscript revisions.

Corresponding author

Correspondence to Alice S. A. Johnston.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Ecology & Evolution thanks Chris Huntingford and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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

Extended data

Extended Data Fig. 1 Short-term temperature and ecosystem respiration measurements in conventional units.

Night-time half-hourly ecosystem respiration measurements from the FLUXNET dataset (symbols, colours representing climate as in Fig. 2) for a) air and b) soil temperature. Plots show ecosystem respiration rates in mg C m−2 hr−1 and temperature in degrees Celsius units.

Extended Data Fig. 2 Identification of low frequency air temperature intervals.

Boxplot of the half-hourly ecosystem respiration measurements from the FLUXNET dataset (symbols, colours representing climate as in Fig. 2) presented in 5 °C air temperature intervals. Boxplots show median values (centre lines) and upper and lower quantiles, with black symbols representing outliers. Asterisks at the top indicate extreme high and low 5 °C temperature intervals with few measurements (< 1 % of the dataset, n < 235,521). The temperature intervals with asterisks (low frequency intervals) were removed from the dataset one by one as well as all together and the threshold model tested. The temperature breakpoints were robust to the removal of each temperature interval one by one but there was no support for a cold temperature breakpoint (−24.8 °C in Fig. 2b,c) when all low frequency intervals or all those < −19 °C were removed. A single temperature breakpoint emerged from the threshold model when all low frequency intervals were removed (Extended Data Fig. 3 and Supplementary Table 3).

Extended Data Fig. 3 Threshold model for ecosystem respiration rates and air temperature when all low frequency temperature intervals were removed.

Threshold model for half-hourly ecosystem respiration rates and air temperature when all low frequency temperature intervals shown in Extended Data Fig. 2 (identified by asterisks) were removed from the dataset. Threshold model predictions (solid line, for the fixed effects of temperature only in a) identified a single temperature threshold of 14.6 °C, with little support for a second temperature breakpoint (b, ΔAIC < 5 and p > 0.05). The dashed line in a indicate an activation energy of −7.50 K as predicted by metabolic theory and ΔAICs in b are between the linear and threshold model. Full details of the threshold mixed effects model are presented in Supplementary Table 3.

Extended Data Fig. 4 Correlation matrix between site variables and model goodness of fit.

Correlation matrix between FLUXNET site variables (latitude, maximum, minimum, mean and air temperature range (°C)) and the goodness of fit (adjusted r2) of the best performing model for predicting the temperature dependence of ecosystem respiration at the site level (threshold, n = 197; linear, n = 13; Supplementary Data 1).

Extended Data Fig. 5 Long-term temperature threshold for soil respiration.

Long-term temperature threshold for soil respiration (Rs), showing a) mean annual Rs from the global soil respiration database (symbols, colours representing climate as in Fig. 2) and the threshold model prediction (solid line, for the fixed effects of temperature only); and b) identification of a single temperature breakpoint of 5.5 °C, with little support for a second temperature breakpoint (ΔAIC < 5 and p > 0.05). Dashed lines indicate an activation energy of −7.50 K as predicted by metabolic theory and ΔAICs are between the linear and threshold model. Full details of the threshold mixed effects model are presented in Supplementary Table 6.

Supplementary information

Supplementary Information

Supplementary Tables 1–6.

Reporting Summary

Peer Review Information

Supplementary Data 1

Summary of the FLUXNET sites, indicating site names, latitude, climate, number of site years, mean air and soil temperature, ecosystem respiration rate and best fitting linear or threshold models at the site level.

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Johnston, A.S.A., Meade, A., Ardö, J. et al. Temperature thresholds of ecosystem respiration at a global scale. Nat Ecol Evol 5, 487–494 (2021). https://doi.org/10.1038/s41559-021-01398-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41559-021-01398-z

This article is cited by

Search

Advanced 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