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Widespread deoxygenation of temperate lakes


The concentration of dissolved oxygen in aquatic systems helps to regulate biodiversity1,2, nutrient biogeochemistry3, greenhouse gas emissions4, and the quality of drinking water5. The long-term declines in dissolved oxygen concentrations in coastal and ocean waters have been linked to climate warming and human activity6,7, but little is known about the changes in dissolved oxygen concentrations in lakes. Although the solubility of dissolved oxygen decreases with increasing water temperatures, long-term lake trajectories are difficult to predict. Oxygen losses in warming lakes may be amplified by enhanced decomposition and stronger thermal stratification8,9 or oxygen may increase as a result of enhanced primary production10. Here we analyse a combined total of 45,148 dissolved oxygen and temperature profiles and calculate trends for 393 temperate lakes that span 1941 to 2017. We find that a decline in dissolved oxygen is widespread in surface and deep-water habitats. The decline in surface waters is primarily associated with reduced solubility under warmer water temperatures, although dissolved oxygen in surface waters increased in a subset of highly productive warming lakes, probably owing to increasing production of phytoplankton. By contrast, the decline in deep waters is associated with stronger thermal stratification and loss of water clarity, but not with changes in gas solubility. Our results suggest that climate change and declining water clarity have altered the physical and chemical environment of lakes. Declines in dissolved oxygen in freshwater are 2.75 to 9.3 times greater than observed in the world’s oceans6,7 and could threaten essential lake ecosystem services2,3,5,11.

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Fig. 1: Trends in dissolved oxygen and temperature.
Fig. 2: Solubility effects and changes in temperature and DO concentration over time.
Fig. 3: Interaction of productivity and temperature in surface waters.
Fig. 4: Effect of changes in water clarity and density difference on deep-water DO saturation change.

Data availability

Raw data used in this study are available in published datasets for all lakes except numbers 99, 100, 101 and 104 via the Freshwater Research and Environmental Database (number 3;, the INRAE data repository (numbers 102, 127;, or the Environmental Data Initiative (all others;,51,52. For numbers 99, 100, 101 and 104, permission was not granted from original data providers to make raw data publicly available. Original dataset sources and contact information for all sites are described in Supplementary Table 1. Supplementary Table 2 contains reported trends in dissolved oxygen and temperature for all lakes with more than 15 years of observations.


  1. Wetzel, R. G. In Limnology 3rd edn (ed. Wetzel, R. G.), Ch. 9, 151–168 (Academic Press, 2001).

  2. Schindler, D. Warmer climate squeezes aquatic predators out of their preferred habitat. Proc. Natl Acad. Sci. USA 114, 9764–9765 (2017).

    Article  CAS  ADS  Google Scholar 

  3. North, R. P., North, R. L., Livingstone, D. M., Köster, O. & Kipfer, R. Long-term changes in hypoxia and soluble reactive phosphorus in the hypolimnion of a large temperate lake: consequences of a climate regime shift. Glob. Change Biol. 20, 811–823 (2014).

    Article  ADS  Google Scholar 

  4. Fernández, J. E., Peeters, F. & Hofmann, H. Importance of the autumn overturn and anoxic conditions in the hypolimnion for the annual methane emissions from a temperate lake. Environ. Sci. Technol. 48, 7297–7304 (2014).

    Article  ADS  Google Scholar 

  5. Michalak, A. M. et al. Record-setting algal bloom in Lake Erie caused by agricultural and meteorological trends consistent with expected future conditions. Proc. Natl Acad. Sci. USA 110, 6448–6452 (2013).

    Article  CAS  ADS  Google Scholar 

  6. Schmidtko, S., Stramma, L. & Visbeck, M. Decline in global oceanic oxygen content during the past five decades. Nature 542, 335–339 (2017).

    Article  CAS  ADS  Google Scholar 

  7. Breitburg, D. et al. Declining oxygen in the global ocean and coastal waters. Science 359, (2018).

  8. Jankowski, J., Livingstone, D. M., Bührer, H., Forster, R. & Niederhauser, P. Consequences of the 2003 European heat wave for lake temperature profiles, thermal stability, and hypolimnetic oxygen depletion: implications for a warmer world. Limnol. Oceanogr. 51, 815–819 (2006).

    Article  ADS  Google Scholar 

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

    Article  Google Scholar 

  10. Seki, H., Takahashi, Y., Hara, Y. & Ichimura, S. Dynamics of dissolved oxygen during algal bloom in Lake Kasumigaura, Japan. Water Res. 14, 179–183 (1980).

    Article  CAS  Google Scholar 

  11. Jacobson, P. C., Stefan, H. G. & Pereira, D. L. Coldwater fish oxythermal habitat in Minnesota lakes: influence of total phosphorus, July air temperature, and relative depth. Can. J. Fish. Aquat. Sci. 67, 2002–2013 (2010).

    Article  CAS  Google Scholar 

  12. Harke, M. J. et al. A review of the global ecology, genomics, and biogeography of the toxic cyanobacterium, Microcystis spp. Harmful Algae 54, 4–20 (2016).

    Article  Google Scholar 

  13. Vaquer-Sunyer, R. & Duarte, C. M. Thresholds of hypoxia for marine biodiversity. Proc. Natl Acad. Sci. USA 105, 15452–15457 (2008).

    Article  CAS  ADS  Google Scholar 

  14. Woolway, R. I. & Merchant, C. J. Worldwide alteration of lake mixing regimes in response to climate change. Nat. Geosci. 12, 271–276 (2019).

    Article  CAS  ADS  Google Scholar 

  15. Livingstone, D. M. Impact of secular climate change on the thermal structure of a large temperate central European lake. Clim. Change 57, 205–225 (2003).

    Article  Google Scholar 

  16. Zhang, Y. et al. Dissolved oxygen stratification and response to thermal structure and long-term climate change in a large and deep subtropical reservoir (Lake Qiandaohu, China). Water Res. 75, 249–258 (2015).

    Article  CAS  Google Scholar 

  17. Bouffard, D., Ackerman, J. D. & Boegman, L. Factors affecting the development and dynamics of hypoxia in a large shallow stratified lake: hourly to seasonal patterns. Wat. Resour. Res. 49, 2380–2394 (2013).

    Article  CAS  ADS  Google Scholar 

  18. O’Reilly, C. M. et al. Rapid and highly variable warming of lake surface waters around the globe. Geophys. Res. Lett. 42, 10773–10781 (2015).

    ADS  Google Scholar 

  19. Nürnberg, G. K. Trophic state of clear and colored, soft- and hardwater lakes with special consideration of nutrients, anoxia, phytoplankton and fish. Lake Reserv. Manage. 12, 432–447 (1996).

    Article  Google Scholar 

  20. Ho, J. C., Michalak, A. M. & Pahlevan, N. Widespread global increase in intense lake phytoplankton blooms since the 1980s. Nature 574, 667–670 (2019).

    Article  CAS  ADS  Google Scholar 

  21. Kosten, S. et al. Warmer climates boost cyanobacterial dominance in shallow lakes. Glob. Change Biol. 18, 118–126 (2012).

    Article  ADS  Google Scholar 

  22. Müller, B., Bryant, L. D., Matzinger, A. & Wüest, A. Hypolimnetic oxygen depletion in eutrophic lakes. Environ. Sci. Technol. 46, 9964–9971 (2012).

    PubMed  Google Scholar 

  23. Winslow, L. A., Leach, T. A. & Rose, K. C. Global lake response to the recent warming hiatus. Environ. Res. Lett. 13, 054005 (2018).

    Article  ADS  Google Scholar 

  24. Livingstone, D. M. An example of the simultaneous occurrence of climate-driven “sawtooth” deep-water warming/cooling episodes in several Swiss lakes. Verh. Int. Ver. Limnol. 26, 822–828 (1997).

    Google Scholar 

  25. Williamson, C. E. et al. Ecological consequences of long-term browning in lakes. Sci. Rep. 5, (2015).

  26. Rose, K. C., Winslow, L. A., Read, J. S. & Hansen, G. J. A. Climate-induced warming of lakes can be either amplified or suppressed by trends in water clarity. Limnol. Oceanogr. Lett. 1, 44–53 (2016).

    Article  Google Scholar 

  27. Woolway, R. I. et al. Northern hemisphere atmospheric stilling accelerates lake thermal responses to a warming world. Geophys. Res. Lett. 46, 11983–11992 (2019).

    Article  ADS  Google Scholar 

  28. Carpenter, S. R., Stanley, E. H. & Vander Zanden, M. J. State of the world’s freshwater ecosystems: physical, chemical, and biological changes. Annu. Rev. Environ. Resour. 36, 75–99 (2011). 

    Article  Google Scholar 

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

  30. Borchers, H. W. pracma: Practical Numerical Math Functions. R package version 2.1.5 (2018).

  31. Winslow, L. A. et al. rLakeAnalyzer: Lake Physics Tools. R package version 1.11.4. (2017).

  32. Winslow, L. A. et al. LakeMetabolizer: an R package for estimating lake metabolism from free-water oxygen using diverse statistical models. Inland Waters 6, 622–636 (2016).

    Article  CAS  Google Scholar 

  33. Carslaw, D. C. & Ropkins, K. Openair – an R package for air quality data analysis. Environ. Model. Softw. 27-28, 52–61 (2012).

    Article  Google Scholar 

  34. Moran, P. A. P. The interpretation of statistical maps. J. R. Stat. Soc. B 10, 243–251 (1948).

    MathSciNet  MATH  Google Scholar 

  35. Kalogirou, S. lctools: Local Correlation, Spatial Inequalities, Geographically Weighted Regression and Other Tools. R package version 0.2-7. (2019).

  36. Copernicus Climate Change Service (C3S). ERA5: Climate Data Store (CDS)!/home (accessed 1 October 2019).

  37. Gelman, G. & Hill, J. Data Analysis Using Regression and Multilevel/Hierarchical Models (Cambridge Univ. Press, 2007).

  38. Quinn, G. P. & Keough, M. J. Experimental Design and Data Analysis for Biologists (Cambridge Univ. Press, 2002).

  39. Lumley, T. leaps: Regression Subset Selection. R package version 3.1. (2020).

  40. Wood, S. N. Generalized Additive Models: An Introduction with R 2nd edn (CRC Press, 2017).

    Book  Google Scholar 

  41. Wood, S. & Scheipl, F. gamm4: Generalized Additive Mixed Models using ‘mgcv’ and ‘lme4’. R package version 0.2-5. (2017).

  42. Pinheiro, J. C. & Bates, D. M. Mixed Effects Models in S and S-Plus (Springer, 2000).

  43. Burnham, K. P., Anderson, D. R. & Huyvaert, K. P. AIC model selection and multimodel inference in behavioral ecology: some background, observations, and comparisons. Behav. Ecol. Sociobiol. 65, 23–35 (2011).

    Article  Google Scholar 

  44. Hosmer, D. W. & Lemeshow, S. Applied Logistic Regression 2nd edn (John Wiley and Sons, Inc., 2000).

  45. Homer, C. G. et al. Completion of the 2011 National Land Cover Database for the conterminous United States – Representing a decade of land cover change information. Photogramm. Eng. Remote Sensing 81, 345–354 (2015).

    Google Scholar 

  46. Lele, S. R., Keim, J. L. & Solymos, P. ResourceSelection: Resource Selection (Probability) Functions for Use-Availability Data. R package version 0.3-2. (2017).

  47. Cutler, D. R. et al. Random forests for classification in ecology. Ecology 88, 2783–2792 (2007).

    Article  Google Scholar 

  48. Liaw, A. & Wiener, M. Classification and regression by randomForest. R News 2, 18–22 (2002).

    Google Scholar 

  49. Messager, M. L., Lehner, B., Grill, G., Nedeva, I. & Schmitt, O. Estimating the volume and age of water stored in global lakes using a geo-statistical approach. Nat. Commun. 7, 13603 (2016).

    Article  CAS  ADS  Google Scholar 

  50. Stetler, J. T., Jane, S. F., Mincer, J. L., Sanders, M. N. & Rose, K. C. Long-term lake dissolved oxygen and temperature data, 1941–2018 ver 2. Environmental Data Initiative (2021).

  51. Adrian, R., Jane, S. F., & Rose, K. C. Widespread deoxygenation of temperate lakes – Müggelsee data. IGB Leibniz-Institute of Freshwater Ecology and Inland Fisheries dataset. (2021).

  52. Jenny, J.-P. Time series dataset of dissolved oxygen, water temperature and Secchi depths profiles in Lakes Annecy and Geneva. Portail Data INRAE V1, (2021).

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This manuscript benefited from conversations at meetings of the Global Lake Ecological Observatory Network (GLEON; supported by funding from US NSF grants 1137327 and 1702991). S.F.J. and K.C.R. acknowledge support from US NSF grants 1638704, 1754265 and 1761805, and S.F.J. was supported by a US Fulbright Student grant to Uppsala University, Sweden. G.J.A.H. acknowledges the many employees of the Minnesota Department of Natural Resources, the Minnesota Pollution Control agency, and citizen volunteers for data collection and collation. B.M.K. acknowledges the 2017-2018 Belmont Forum and BiodivERsA joint call for research proposals under the BiodivScen ERA-Net COFUND programme and with funding from the German Science Foundation (AD 91/22-1). P.R.L. acknowledges support from a NSERC Discovery Grant, the Canada Research Chair Program, Canada Foundation for Innovation, the Province of Saskatchewan, University of Regina, and Queen’s University Belfast. R.L.N. and J.J. acknowledge support from the Missouri Department of Natural Resources and the Missouri Agricultural Experiment Station and many students that collected and processed reservoir samples under the leadership of Daniel V. Obrecht and Anthony P. Thorpe. R.M.P. and C.E.W. acknowledge support from NSF grants 1754276 and 1950170, Miami University Eminent Scholar Fund, and the Lacawac Sanctuary and Biological Field Station for access to Lake Lacawac and use of research facilities. R.I.W. acknowledges support from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 791812. S.C. acknowledges support of the Castle Lake Research Program through the University of Nevada and UC Davis via Charles R. Goldman. C.L.D. acknowledges the King County Environmental Laboratory for the long-term monitoring data for Lake Washington and Lake Sammamish. J.D. acknowledges support from the University of Warmia and Mazury in Olsztyn (Grant under the Senate Committee for International Cooperation financing) and staff at Department of Water Protection Engineering and Environmental Microbiology for long-term data collection and analysis. O.E. acknowledges support from the Russian Scientific Foundation (grant 19-77-30004) for Mozhaysk Reservoir. G.F. acknowledges support for long-term sampling of Lake Caldonazzo by the Fondazione Edmund Mach. H.P.G. acknowledges funding for long-term sampling of Lake Stechlin by the Leibniz association and assistance by members of the IGB team. K.D.H. acknowledges the Oklahoma Department of Wildlife Conservation, the Oklahoma Water Resources Board, the Grand River Dam Authority, the US Army Corps of Engineers, the City of Tulsa, W. M. Matthews, T. Clyde, R. M. Zamor, P. Koenig, and R. West for support, assistance, and data for Lakes Texoma, Thunderbird, Grand, Eucha, and Spavinaw. J.H. acknowledges support from the ERDF/ESF project Biomanipulation as a tool for improving water quality of dam reservoirs (no. CZ.02.1.01/0.0/0.0/16_025/0007417). B.L. acknowledges support from the FA-UNIMIB for long-term monitoring of Lake Iseo. E.B.M. acknowledges support from the UK Natural Environment Research Council funding for the long-term monitoring on Blelham Tarn and the staff of the Freshwater Biological Association and UK Centre for Ecology and Hydrology for carrying out the work. A.P. and J.A.R. acknowledge support from the Ontario Ministry of the Environment, Conservation and Parks for providing data from south-central Ontario lakes (‘Dorset lakes’) and staff and students at Ontario’s Dorset Environmental Science Centre for data collection and analysis. M.R. acknowledges the International Commission for the Protection of Italian-Swiss Waters (CIPAIS) for funding long-term research on Lake Maggiore. M.S. acknowledges the City of Zurich Water Supply and the cantonal agencies of the cantons of Bern (AWA, Gewässer- und Bodenschutzlabor), Zurich (AWEL), St. Gallen (AFU), and Neuchatel for providing data for the Swiss lakes, CIPEL and INRA for data from Lake Geneva, and IGKB for data from Lake Constance. R.S. acknowledges support from the LTSER platform Tyrolean Alps (LTER‐Austria).W.T. acknowledges support from the Belgian Science Policy Office through the research project EAGLES (CD/AR/02A) on Lake Kivu. P.V. acknowledges support from the councils of the regions of Waikato, West Coast, and Bay of Plenty for long-term sampling of lakes Taupo, Brunner, and Tarawera. K.Y. acknowledges support from the Clark Foundation for long-term monitoring of Otsego Lake and past and current members of SUNY Oneonta BFS for sampling. K.S. and J.S. acknowledge the National Park Service, W Gawley, Acadia National Park for providing data for Jordan Pond, Bubble Pond, and Eagle Lake in Maine. We acknowledge the support of other data contributors, including B. Adamovich, T. Zhukova and Belarusian State University; R. Adrian and the Leibniz Institute of Freshwater Ecology and Inland Fisheries; L. Bacon and the Maine Department of Environmental Protection; M. Cofrin and the New Hampshire Department of Environmental Services; S. Devlin, Flathead Lake Biological Station, and the University of Montana; E. Gaiser, H. Swain, K. Main, N. Deyrup and Archbold Biological Station; S. Higgins and the IISD Experimental Lakes Area; L. Kaminski and the Michigan Clean Water Corps and Michigan Department of Environment, Great Lakes, and Energy; Pernilla Rönnback and the Swedish University of Agricultural Sciences; S. Nierzwicki-Bauer, the Darrin Freshwater Institute, and Rensselaer Polytechnic Institute; C. Pedersen and the Minnesota Department of Natural Resources; P. Stangel and the Vermont Department of Environmental Conservation; E. Stanley and the North Temperate Lakes Long-Term Ecological Research site; and M. Vanni, M. Gonzalez and Miami University. We also acknowledge the assistance of C. Gries in making data publicly available via the Environmental Data Initiative. The views expressed in this Article are those of the authors and do not necessarily reflect views or policies of funding agencies.

Author information

Authors and Affiliations



S.F.J. and K.C.R. designed the study, compiled the data, conducted analyses, and drafted the manuscript. G.J.A.H., B.M.K., P.R.L., J.L.M., R.L.N., R.M.P., J.T.S., C.E.W. and R.I.W. helped design the study and conduct analyses, contributed data, and edited the manuscript. L.A., S.C., C.L.D., L.D., J.D., O.E., G.F., H.-P.G., K.D.H., C.H., J.H., L.L.J., J.-P.J., J.R.J., L.B.K., B.L., E.M., S.-I.S.M., C.M., D.C.M.-N., A.M.P., D.P., M.R., J.A.R., S.S., E.S.-T., M.S., R.S., W.T., P.V., K.C.W., G.A.W. and K.Y. contributed data, edited the manuscript, or both.

Corresponding author

Correspondence to Kevin C. Rose.

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The authors declare no competing interests.

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Peer review information Nature thanks Peter Raymond and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 Locations of lakes used in this study.

Red circles denote the study lakes.

Extended Data Fig. 2 Results of GAMM analysis of trends zoomed out to visualize distribution of residuals.

a, Surface-water temperature (°C). b, Deep-water temperature (°C). c, Surface-water DO (mg l−1). d, Deep-water DO concentration (mg l−1). The error bars are ±1  standard error from the smoothed estimate (as in Fig. 2c–f).

Extended Data Fig. 3 Drivers of deep-water change in percent dissolved oxygen saturation.

af, Partial dependency plots from a random forest algorithm of deep-water changes in the percentage of dissolved oxygen saturation (ΔSat) in the past five years of record relative to the first five years of record for each lake. Plots are ordered by predictor variable importance, decreasing in importance from the top left to the bottom right. Vertical red lines indicate zero change in predictor variable and hash marks on the x axis indicate lake distribution deciles. Partial dependencies indicate the relationship between predictor and response variables when holding other variables at their mean value. Lakes that experienced no change in either water clarity or density difference between surface and deep waters exhibited little change in deep-water saturation (see Fig. 4).

Extended Data Fig. 4 Drivers of the change in density difference between surface and deep waters.

af, Partial dependency plots from a random forest algorithm of deep-water change in water column density difference in the last five years of record relative to the first five years of record for each lake. Plots are ordered by predictor variable importance, decreasing in importance from the top left to the bottom right. Vertical red lines indicate zero values for predictor variable and hash marks on the x axis indicate lake distribution deciles. Partial dependencies indicate the relationship between predictor and response variables when holding other variables at their mean value.

Extended Data Table 1 Trends among subsets of temperate lakes, all temperate lakes, and all lakes including eight non-temperate lakes, as well as two time periods: 1980–2017 and 1990–2017
Extended Data Table 2 Trends in climate characteristics over study lakes, the entire temperate zone, and temperate zones in selected regions, 1980–2017
Extended Data Table 3 Coefficients and P values for the selected multiple regression model predicting lake surface temperature trends

Supplementary information

Supplementary Table 1

Raw data source and repository information.

Supplementary Table 2

Metadata and trend information for study lakes.

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Jane, S.F., Hansen, G.J.A., Kraemer, B.M. et al. Widespread deoxygenation of temperate lakes. Nature 594, 66–70 (2021).

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