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Imbalance of global nutrient cycles exacerbated by the greater retention of phosphorus over nitrogen in lakes


Imbalanced anthropogenic inputs of nitrogen (N) and phosphorus (P) have significantly increased the ratio between N and P globally, degrading ecosystem productivity and environmental quality. Lakes represent a large global nutrient sink, modifying the flow of N and P in the environment. It remains unknown, however, the relative retention of these two nutrients in global lakes and their role in the imbalance of the nutrient cycles. Here we compare the ratio between P and N in inflows and outflows of more than 5,000 lakes globally using a combination of nutrient budget model and generalized linear model. We show that over 80% of global lakes positively retain both N and P, and almost 90% of the lakes show preferential retention of P. The greater retention of P over N leads to a strong elevation in the ratios between N and P in the lake outflow, exacerbating the imbalance of N and P cycles unexpectedly and potentially leading to biodiversity losses within lakes and algal blooms in downstream N-limited coastal zones. The management of N or P in controlling lake eutrophication has long been debated. Our results suggest that eutrophication management that prioritizes the reduction of P in lakes—which causes a further decrease in P in outflows—may unintentionally aggravate N/P imbalances in global ecosystems. Our results also highlight the importance of nutrient retention stoichiometry in global lake management to benefit watershed and regional biogeochemical cycles.

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Fig. 1: Fluxes and stoichiometry of nutrient cycling across the trophic gradient.
Fig. 2: Global pattern of preferential nutrient retention.
Fig. 3: Imbalance of nutrient retention in global lakes.

Data availability

The data of National Lake Assessment 2012 were obtained from USEPA ( The Chlorophyll and Water Chemistry database was retrieved from Scientific Data ( The HydroLAKES dataset was retrieved from Global HydroLAB ( The processed data to reproduce the results in this study are available at GitHub ( and Zenodo ( Source data are provided with this paper.

Code availability

The CmdStan (version 2.28) software used for the nutrient budget model is available at Julia (version 1.6.5), used as the interface to run CmdStan, is available at R (version 4.1.2), used for GLM analysis, is available from the R Core Team ( Python (version 3.10), used for global up-scaling analysis, is available at The codes to reproduce the results in this study are available at


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We thank H. Guo, Y. Qin and L. Cao for helpful discussion. We also thank High-performance Computing Platform of Peking University for providing computing resources. The present work was financially supported by National Natural Science Foundation of China (42142047 to Y.L., 51721006 to Y.L. and J.N.), Simons Foundation Postdoctoral Fellowship (645921 to G.L.B.), Simons Collaboration on Ocean Processes and Ecology (SCOPE 329108 to M.J.F.), Simons Collaboration for Computational Biogeochemical Modeling of marine Ecosystems (CBIOMES 549931 to M.J.F.), Spanish Government Grant (PID2019-110521GB-I00 and PID2020-115770RB-I00 to J.P. and J.S.) and Fundación Ramón Areces Grant (ELEMENTAL-CLIMATE to J.P. and J.S.).

Author information

Authors and Affiliations



Z.W. and Y.L. conceived the study. Z.W. and G.L.B. developed the Bayesian mechanistic nutrient budget model. J.L., Q.J. and W.G. calculated NANI and NAPI for the nutrient budget model. Y.S. carried out the GLM analysis, and J.H. performed the global up-scaling analysis. Z.W. and Y.L. wrote the paper with direct contributions from J.P., J.S., J.C.F., G.L.B., M.J.F., B.Q., J.N. and S.H.

Corresponding author

Correspondence to Yong Liu.

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Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Geoscience thanks R. W. Howarth and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Xujia Jiang.

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Extended data

Extended Data Fig. 1 Spatial distribution of lakes studied in the NLA2012 dataset.

The number of lakes selected in NLA2012 dataset is 596. These lakes locate across 8 Level-I ecoregions of North America. Four trophic states are grouped by Chlorophyll-a concentration, oligotrophic (<2 µg/L), mesotrophic (2-7 µg/L), eutrophic (7-30 µg/L), and hyper-eutrophic (>30 µg/L). Of the NLA 2012 dataset, the number of oligotrophic, mesotrophic, eutrophic and hyper-eutrophic lakes are 98, 212, 149 and 137 respectively.

Source data

Extended Data Fig. 2 Spatial distribution of lakes studied in the Chlorophyll and Water Chemistry dataset.

A number of 5622 lakes are identified based on the intersection of the Chlorophyll and Water Chemistry datasets and HydroLAKES database. These lakes are mainly located in North America, Europe, and east China.

Source data

Extended Data Fig. 3 Comparison between the Chlorophyll and Water Chemistry database dataset and NLA2012 dataset.

Distribution of water residence time, surface water temperature, lake depth, lake area, and chlorophyll a (Chla) are shown in this figure. All of these variables share similar distributions between these two datasets.

Source data

Extended Data Fig. 4 Posterior distributions of the parameters for the nutrient budget model.

The numbers listed in the titles of each panel are the mean values for each parameter.

Source data

Extended Data Fig. 5 Global distributions of fluxes of in-lake enrichment and depletion processes.

The fluxes are estimated form HydroLAKES database by grouping the lakes in to 36 categories of different lake area and trophic state (see Supplementary Table 1 for detail).

Source data

Supplementary information

Supplementary Information

Supplementary Figs. 1–7 and Tables 1–7.

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Wu, Z., Li, J., Sun, Y. et al. Imbalance of global nutrient cycles exacerbated by the greater retention of phosphorus over nitrogen in lakes. Nat. Geosci. 15, 464–468 (2022).

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