Streams play a key role in the global carbon cycle. The balance between carbon intake through photosynthesis and carbon release via respiration influences carbon emissions from streams and depends on temperature. However, the lack of a comprehensive analysis of the temperature sensitivity of the metabolic balance in inland waters across latitudes and local climate conditions hinders an accurate projection of carbon emissions in a warmer future. Here, we use a model of diel dissolved oxygen dynamics, combined with high-frequency measurements of dissolved oxygen, light and temperature, to estimate the temperature sensitivities of gross primary production and ecosystem respiration in streams across six biomes, from the tropics to the arctic tundra. We find that the change in metabolic balance, that is, the ratio of gross primary production to ecosystem respiration, is a function of stream temperature and current metabolic balance. Applying this relationship to the global compilation of stream metabolism data, we find that a 1 °C increase in stream temperature leads to a convergence of metabolic balance and to a 23.6% overall decline in net ecosystem productivity across the streams studied. We suggest that if the relationship holds for similarly sized streams around the globe, the warming-induced shifts in metabolic balance will result in an increase of 0.0194 Pg carbon emitted from such streams every year.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Additional information

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


  1. 1.

    Battin, T. J. et al. The boundless carbon cycle. Nat. Geosci. 2, 598–600 (2009).

  2. 2.

    Butman, D. et al. Aquatic carbon cycling in the conterminous United States and implications for terrestrial carbon accounting. Proc. Natl Acad. Sci. USA 113, 58–63 (2016).

  3. 3.

    Cole, J. J. et al. Plumbing the global carbon cycle: integrating inland waters into the terrestrial carbon budget. Ecosystems 10, 172–185 (2007).

  4. 4.

    Raymond, P. A. et al. Global carbon dioxide emissions from inland waters. Nature 503, 355–359 (2013).

  5. 5.

    Ciais, P et al. in Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) 465–570 (IPCC, Cambridge Univ. Press, Cambridge, 2013).

  6. 6.

    Hotchkiss, E. et al. Sources of and processes controlling CO2 emissions change with the size of streams and rivers. Nat. Geosci. 8, 696–699 (2015).

  7. 7.

    Battin, T. J. et al. Biophysical controls on organic carbon fluxes in fluvial networks. Nat. Geosci. 1, 95–100 (2008).

  8. 8.

    Kaushal, S. S. et al. Rising stream and river temperatures in the United States. Front. Ecol. Environ. 8, 461–466 (2010).

  9. 9.

    Mohseni, O., Erickson, T. R. & Stefan, H. G. Sensitivity of stream temperatures in the United States to air temperatures projected under a global warming scenario. Water Resour. Res. 35, 3723–3733 (1999).

  10. 10.

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

  11. 11.

    Galmes, J., Kapralov, M., Copolovici, L., Hermida-Carrera, C. & Niinemets, Ü. Temperature responses of the Rubisco maximum carboxylase activity across domains of life: phylogenetic signals, trade-offs, and importance for carbon gain. Photosynth. Res. 123, 183–201 (2015).

  12. 12.

    Chen, B. & Laws, E. A. Is there a difference of temperature sensitivity between marine phytoplankton and heterotrophs? Limnol. Oceanogr. 62, 806–817 (2017).

  13. 13.

    Follstad Shah, J. J. et al. Global synthesis of the temperature sensitivity of leaf litter breakdown in streams and rivers. Glob. Change Biol. 23, 3064–3075 (2017).

  14. 14.

    Duan, S.-W. & Kaushal, S. Warming increases carbon and nutrient fluxes from sediments in streams across land use. Biogeosciences 10, 1193–1207 (2013).

  15. 15.

    Raven, J. A. & Geider, R. J. Temperature and algal growth. New Phytol. 110, 441–461 (1988).

  16. 16.

    Anderson-Teixeira, K. J., Vitousek, P. M. & Brown, J. H. Amplified temperature dependence in ecosystems developing on the lava flows of Mauna Loa, Hawai’i. Proc. Natl Acad. Sci. USA 105, 228–233 (2008).

  17. 17.

    Welter, J. R. et al. Does N2 fixation amplify the temperature dependence of ecosystem metabolism? Ecology 96, 603–610 (2015).

  18. 18.

    Sand-Jensen, K., Pedersen, N. L. & Søndergaard, M. Bacterial metabolism in small temperate streams under contemporary and future climates. Freshw. Biol. 52, 2340–2353 (2007).

  19. 19.

    López-Urrutia, Á. & Morán, X. A. G. Resource limitation of bacterial production distorts the temperature dependence of oceanic carbon cycling. Ecology 88, 817–822 (2007).

  20. 20.

    Boyero, L. et al. A global experiment suggests climate warming will not accelerate litter decomposition in streams but might reduce carbon sequestration. Ecol. Lett. 14, 289–294 (2011).

  21. 21.

    Acuna, V., Wolf, A., Uehlinger, U. & Tockner, K. Temperature dependence of stream benthic respiration in an alpine river network under global warming. Freshw. Biol. 53, 2076–2088 (2008).

  22. 22.

    Jankowski, K., Schindler, D. & Lisi, P. Temperature sensitivity of community respiration rates in streams is associated with watershed geomorphic features. Ecology 95, 2707–2714 (2014).

  23. 23.

    Yvon-Durocher, G., Jones, J. I., Trimmer, M., Woodward, G. & Montoya, J. M. Warming alters the metabolic balance of ecosystems. Philos. Trans. R. Soc. Lond. B 365, 2117–2126 (2010).

  24. 24.

    Sinsabaugh, R. L. Large-scale trends for stream benthic respiration. J. N. Am. Benthol. Soc. 16, 119–122 (1997).

  25. 25.

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

  26. 26.

    Huryn, A. D., Benstead, J. P. & Parker, S. M. Seasonal changes in light availability modify the temperature dependence of ecosystem metabolism in an arctic stream. Ecology 95, 2826–2839 (2014).

  27. 27.

    Demars, B. O. et al. Temperature and the metabolic balance of streams. Freshw. Biol. 56, 1106–1121 (2011).

  28. 28.

    Perkins, D. M. et al. Consistent temperature dependence of respiration across ecosystems contrasting in thermal history. Glob. Change Biol. 18, 1300–1311 (2012).

  29. 29.

    Demars, B., Manson, J., Olafsson, J., Gislason, G. & Friberg, N. Stream hydraulics and temperature determine the metabolism of geothermal icelandic streams. Knowl. Manag. Aquat. Ecosyst. 402, 05 (2011).

  30. 30.

    Cross, W. F., Hood, J. M., Benstead, J. P., Huryn, A. D. & Nelson, D. Interactions between temperature and nutrients across levels of ecological organization. Glob. Change Biol. 21, 1025–1040 (2015).

  31. 31.

    Williamson, T. J. et al. Warming alters coupled carbon and nutrient cycles in experimental streams. Glob. Change Biol. 22, 2152–2164 (2016).

  32. 32.

    Demars, B. O. et al. Impact of warming on CO2 emissions from streams countered by aquatic photosynthesis. Nat. Geosci. 9, 758–761 (2016).

  33. 33.

    Holtgrieve, G. W., Schindler, D. E. & Jankowski, K. Comment on Demars et al. 2015, ‘Stream metabolism and the open diel oxygen method: principles, practice, and perspectives’. Limnol. Oceanogr. Methods 14, 110–113 (2016).

  34. 34.

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

  35. 35.

    Standard Methods for the Examination of Water and Wastewater 19th edn (American Public Health Association, American Waterworks Association, Water Environment Federation, Washington, DC, 1995).

  36. 36.

    Jassby, A. D. & Platt, T. Mathematical formulation of the relationship between photosynthesis and light for phytoplankton. Limnol. Oceanogr. 21, 540–547 (1976).

  37. 37.

    Parkhill, K. L. & Gulliver, J. S. Modeling the effect of light on whole-stream respiration. Ecol. Modell. 117, 333–342 (1999).

  38. 38.

    Bott, T. L. in Methods in Stream Ecology (eds Hauer, F. R & Lamberti, G. A) 533–556 (Academic Press, San Diego, 2006).

  39. 39.

    Song, C., Dodds, W. K., Trentman, M. T., Rüegg, J. & Ballantyne, F. Methods of approximation influence aquatic ecosystem metabolism estimates. Limnol. Oceanogr. Methods 14, 557–569 (2016).

  40. 40.

    Sierra, C. A. Temperature sensitivity of organic matter decomposition in the Arrhenius equation: some theoretical considerations. Biogeochemistry 108, 1 (2012).

  41. 41.

    Descamps-Julien, B. & Gonzalez, A. Stable coexistence in a fluctuating environment: an experimental demonstration. Ecology 86, 2815–2824 (2005).

  42. 42.

    Jiang, L. & Morin, P. J. Temperature fluctuation facilitates coexistence of competing species in experimental microbial communities. J. Anim. Ecol. 76, 660–668 (2007).

  43. 43.

    Chesson, P. Mechanisms of maintenance of species diversity. Annu. Rev. Ecol. Syst. 31, 343–366 (2000).

  44. 44.

    Hoellein, T. J., Bruesewitz, D. A. & Richardson, D. C. Revisiting Odum (1956): a synthesis of aquatic ecosystem metabolism. Limnol. Oceanogr. 58, 2089–2100 (2013).

  45. 45.

    Wollheim, W. M. et al. Global N removal by freshwater aquatic systems using a spatially distributed, within-basin approach. Glob. Biogeochem. Cycles 22, GB2026 (2008).

  46. 46.

    Wetzel, R. G. & Likens, G. E. Limnological Analysis 3rd edn (Springer, Berlin, 2013).

  47. 47.

    Freeman, C., Evans, C., Monteith, D., Reynolds, B. & Fenner, N. Export of organic carbon from peat soils. Nature 412, 785–785 (2001).

  48. 48.

    Kominoski, J. S. et al. Forecasting functional implications of global changes in riparian plant communities. Front. Ecol. Environ. 11, 423–432 (2013).

  49. 49.

    Padfield, D., Yvon-Durocher, G., Buckling, A., Jennings, S. & Yvon-Durocher, G. Rapid evolution of metabolic traits explains thermal adaptation in phytoplankton. Ecol. Lett. 19, 133–142 (2016).

  50. 50.

    Demars, B. O., Thompson, J. & Manson, J. R. Stream metabolism and the open diel oxygen method: principles, practice, and perspectives. Limnol. Oceanogr. Methods 13, 356–374 (2015).

  51. 51.

    Rüegg, J. et al. Baseflow physical characteristics differ at multiple spatial scales in stream networks across diverse biomes. Landsc. Ecol. 31, 119–136 (2016).

  52. 52.

    Holtgrieve, G. W., Schindler, D. E., Branch, T. A. & A’mar, Z. T. Simultaneous quantification of aquatic ecosystem metabolism and reaeration using a Bayesian statistical model of oxygen dynamics. Limnol. Oceanogr. 55, 1047–1063 (2010).

  53. 53.

    Riley, A. J. & Dodds, W. K. Whole-stream metabolism: strategies for measuring and modeling diel trends of dissolved oxygen. Freshw. Sci. 32, 56–69 (2012).

  54. 54.

    Grace, M. R. et al. Fast processing of diel oxygen curves: estimating stream metabolism with BASE (BAyesian Single-station Estimation). Limnol. Oceanogr. Methods 13, 103–114 (2015).

  55. 55.

    Soetaert, K., Petzoldt, T. & Setzer, R. W. Solving differential equations in R: package desolve. J. Stat. Softw. 33, 1–25 (2010).

  56. 56.

    Haario, H Saksman, E. & Tamminen, J. An adaptive metropolis algorithm. Bernoulli 7, 223–242 2001).

  57. 57.

    Geyer, C. J. & Johnson, L. T. mcmc: Markov Chain Monte Carlo. R package v.0.9-3 (CRAN, 2014).

  58. 58.

    Geweke, J. in Bayesian Statistics (eds. Bernado, J. et al.) 169–193 (Clarendon Press, Oxford, 1992).

  59. 59.

    Plummer, M., Best, N., Cowles, K. & Vines, K. CODA: convergence diagnosis and output analysis for MCMC. R. News 6, 7–11 (2006).

  60. 60.

    Kenward, M. G., & Roger, J. H. Small sample inference for fixed effects from restricted maximum likelihood. Biometrics 53, 983–997 (1997).

  61. 61.

    Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).

  62. 62.

    Halekoh, U. & Højsgaard, S. A Kenward–Roger approximation and parametric bootstrap methods for tests in linear mixed models – the R package pbkrtest. J. Stat. Softw. 59, 1–30 (2014).

  63. 63.

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

Download references


The authors thank K. Gido for his contribution in obtaining funding and designing the field experiments. K. Gido, J. Drake, C. Osenberg and J. Minucci provided comments on earlier versions of this paper. Georgia Advanced Computing Resource Center provided the computing facility. This study was supported by the National Science Foundation (NSF, grant EF-1258994) and is part of the Scale, Consumers and Lotic Ecosystem Rates project supported by NSF grant EF-1065255. Data collection at each site was supported by NSF grants EF-1065286, EF-1065055, EF-1065682, EF-1065267, EF-1064998 and EF-1065377, and the Northern Australian Environmental Resources Hub of the National Environmental Science Program.

Author information


  1. Odum School of Ecology, University of Georgia, Athens, GA, USA

    • Chao Song
    • , Kaitlin J. Farrell
    • , John S. Kominoski
    • , Amy D. Rosemond
    •  & Ford Ballantyne IV
  2. Division of Biology, Kansas State University, Manhattan, KS, USA

    • Walter K. Dodds
    • , Janine Rüegg
    • , Shufang Jia
    • , Claire M. Ruffing
    •  & Matt T. Trentman
  3. Stream Biofilm and Ecosystem Research Laboratory, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

    • Janine Rüegg
  4. Department of Forest Engineering, Resources, and Management, Oregon State University, Corvallis, OR, USA

    • Alba Argerich
  5. School of Natural Resources, University of Missouri, Columbia, MO, USA

    • Alba Argerich
  6. Department of Biology and Wildlife and Institute of Arctic Biology, University of Alaska Fairbanks, Fairbanks, AK, USA

    • Christina L. Baker
    • , Tamara K. Harms
    • , Jeremy B. Jones
    •  & Claire M. Ruffing
  7. Rubenstein School of Environment and Natural Resources, University of Vermont, Burlington, VT, USA

    • William B. Bowden
    •  & Samuel P. Parker
  8. School of Biological Sciences, and School of Agriculture and Environment, University of Western Australia, Perth, WA, Australia

    • Michael M. Douglas
  9. Department of Biological Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA

    • Kaitlin J. Farrell
  10. Biological Sciences, Murray State University, Murray, KY, USA

    • Michael B. Flinn
  11. Research Institute for the Environment and Livelihoods, Charles Darwin University, Darwin, Northern Territory, Australia

    • Erica A. Garcia
    •  & Damien McMaster
  12. Department of Natural Resources and the Environment, and the Center for Environmental Sciences and Engineering, University of Connecticut, Storrs, CT, USA

    • Ashley M. Helton
    •  & Lauren E. Koenig
  13. Department of Natural Resources and the Environment, University of New Hampshire, Durham, NH, USA

    • Lauren E. Koenig
    • , William H. McDowell
    • , Ken R. Sheehan
    •  & Wilfred M. Wollheim
  14. Department of Biological Sciences, Florida International University, Miami, FL, USA

    • John S. Kominoski
  15. Southwest Biological Science Center, United States Geological Survey, Flagstaff, AZ, USA

    • Ken R. Sheehan
  16. Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, USA

    • Matt T. Trentman
  17. Department of Zoology and Center for Ecology, Southern Illinois University, Carbondale, IL, USA

    • Matt R. Whiles


  1. Search for Chao Song in:

  2. Search for Walter K. Dodds in:

  3. Search for Janine Rüegg in:

  4. Search for Alba Argerich in:

  5. Search for Christina L. Baker in:

  6. Search for William B. Bowden in:

  7. Search for Michael M. Douglas in:

  8. Search for Kaitlin J. Farrell in:

  9. Search for Michael B. Flinn in:

  10. Search for Erica A. Garcia in:

  11. Search for Ashley M. Helton in:

  12. Search for Tamara K. Harms in:

  13. Search for Shufang Jia in:

  14. Search for Jeremy B. Jones in:

  15. Search for Lauren E. Koenig in:

  16. Search for John S. Kominoski in:

  17. Search for William H. McDowell in:

  18. Search for Damien McMaster in:

  19. Search for Samuel P. Parker in:

  20. Search for Amy D. Rosemond in:

  21. Search for Claire M. Ruffing in:

  22. Search for Ken R. Sheehan in:

  23. Search for Matt T. Trentman in:

  24. Search for Matt R. Whiles in:

  25. Search for Wilfred M. Wollheim in:

  26. Search for Ford Ballantyne IV in:


C.S. and F.B. conceived the modelling approach to estimating temperature sensitivity. C.S. performed metabolism modelling, conducted the data analyses, and wrote the manuscript. W.K.D., J.R., A.A., W.B.B., M.M.D., M.B.F., E.A.G., A.M.H., T.K.H., J.B.J., J.S.K., W.H.M., A.D.R., M.R.W., W.M.W. and F.B. designed the field experiments. W.K.D., A.A., W.B.B., M.B.F., T.K.H., J.B.J., J.S.K., W.H.M., A.D.R., M.R.W., W.M.W. and F.B. obtained funding. J.R., A.A., C.L.B., K.J.F., E.A.G., L.E.K., D.M., S.P.P., C.M.R., K.R.S. and M.T.T. collected the data. S.J. and J.R. managed the database. All authors provided feedback on the manuscript.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Chao Song.

Supplementary information

  1. Supplementary Information

    Supplementary Figures

  2. Supplementary Dataset 1

    Supplementary Data

  3. Supplementary Dataset 2

    Supplementary Data

About this article

Publication history




Issue Date



Further reading