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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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; https://doi.org/10.18728/568.0), the INRAE data repository (numbers 102, 127; https://doi.org/10.15454/BUJUSX), or the Environmental Data Initiative (all others; https://doi.org/10.6073/pasta/841f0472e19853b0676729221aedfb56)50,51,52. 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.
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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.
The authors declare no competing interests.
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
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).
a–f, 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).
a–f, 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.
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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). https://doi.org/10.1038/s41586-021-03550-y
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