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Optimal nitrogen-to-phosphorus stoichiometry of phytoplankton

Author

Listed:
  • Christopher A. Klausmeier

    (Princeton University
    Georgia Institute of Technology)

  • Elena Litchman

    (Georgia Institute of Technology
    Institute of Marine and Coastal Sciences, Rutgers University)

  • Tanguy Daufresne

    (Princeton University)

  • Simon A. Levin

    (Princeton University)

Abstract

Redfield noted the similarity between the average nitrogen-to-phosphorus ratio in plankton (N:P = 16 by atoms) and in deep oceanic waters (N:P = 15; refs 1, 2). He argued that this was neither a coincidence, nor the result of the plankton adapting to the oceanic stoichiometry, but rather that phytoplankton adjust the N:P stoichiometry of the ocean to meet their requirements through nitrogen fixation, an idea supported by recent modelling studies3,4. But what determines the N:P requirements of phytoplankton? Here we use a stoichiometrically explicit model of phytoplankton physiology and resource competition to derive from first principles the optimal phytoplankton stoichiometry under diverse ecological scenarios. Competitive equilibrium favours greater allocation to P-poor resource-acquisition machinery and therefore a higher N:P ratio; exponential growth favours greater allocation to P-rich assembly machinery and therefore a lower N:P ratio. P-limited environments favour slightly less allocation to assembly than N-limited or light-limited environments. The model predicts that optimal N:P ratios will vary from 8.2 to 45.0, depending on the ecological conditions. Our results show that the canonical Redfield N:P ratio of 16 is not a universal biochemical optimum, but instead represents an average of species-specific N:P ratios.

Suggested Citation

  • Christopher A. Klausmeier & Elena Litchman & Tanguy Daufresne & Simon A. Levin, 2004. "Optimal nitrogen-to-phosphorus stoichiometry of phytoplankton," Nature, Nature, vol. 429(6988), pages 171-174, May.
  • Handle: RePEc:nat:nature:v:429:y:2004:i:6988:d:10.1038_nature02454
    DOI: 10.1038/nature02454
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    Cited by:

    1. Tsakalakis, Ioannis & Pahlow, Markus & Oschlies, Andreas & Blasius, Bernd & Ryabov, Alexey B., 2018. "Diel light cycle as a key factor for modelling phytoplankton biogeography and diversity," Ecological Modelling, Elsevier, vol. 384(C), pages 241-248.
    2. Flynn, Kyle F. & Chapra, Steven C. & Suplee, Michael W., 2013. "Modeling the lateral variation of bottom-attached algae in rivers," Ecological Modelling, Elsevier, vol. 267(C), pages 11-25.
    3. Tarun De & Minati De & Subhajit Das & Chumki Chowdhury & Raghab Ray & Tapan Jana, 2011. "Phytoplankton abundance in relation to cultural eutrophication at the land-ocean boundary of Sunderbans, NE Coast of Bay of Bengal, India," Journal of Environmental Studies and Sciences, Springer;Association of Environmental Studies and Sciences, vol. 1(3), pages 169-180, September.
    4. Chen, Bingzhang & Smith, S. Lan, 2018. "Optimality-based approach for computationally efficient modeling of phytoplankton growth, chlorophyll-to-carbon, and nitrogen-to-carbon ratios," Ecological Modelling, Elsevier, vol. 385(C), pages 197-212.
    5. Merico, Agostino & Bruggeman, Jorn & Wirtz, Kai, 2009. "A trait-based approach for downscaling complexity in plankton ecosystem models," Ecological Modelling, Elsevier, vol. 220(21), pages 3001-3010.
    6. Dittrich, M. & Wehrli, B. & Reichert, P., 2009. "Lake sediments during the transient eutrophication period: Reactive-transport model and identifiability study," Ecological Modelling, Elsevier, vol. 220(20), pages 2751-2769.
    7. Wan, Zhenwen & Bi, Hongsheng, 2014. "Comparing model scenarios of variable plankton N/P ratio versus the constant one for the application in the Baltic Sea," Ecological Modelling, Elsevier, vol. 272(C), pages 28-39.
    8. Han, Yue & Zhou, Yuntao, 2022. "Investigating biophysical control of marine phytoplankton dynamics via Bayesian mechanistic modeling," Ecological Modelling, Elsevier, vol. 474(C).
    9. Jiancai Deng & Fang Chen & Weiping Hu & Xin Lu & Bin Xu & David P. Hamilton, 2019. "Variations in the Distribution of Chl- a and Simulation Using a Multiple Regression Model," IJERPH, MDPI, vol. 16(22), pages 1-16, November.
    10. Salama, El-Sayed & Kurade, Mayur B. & Abou-Shanab, Reda A.I. & El-Dalatony, Marwa M. & Yang, Il-Seung & Min, Booki & Jeon, Byong-Hun, 2017. "Recent progress in microalgal biomass production coupled with wastewater treatment for biofuel generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 1189-1211.
    11. Li, Yu & Waite, Anya M. & Gal, Gideon & Hipsey, Matthew R., 2013. "An analysis of the relationship between phytoplankton internal stoichiometry and water column N:P ratios in a dynamic lake environment," Ecological Modelling, Elsevier, vol. 252(C), pages 196-213.
    12. Daniel Graeber & Mark J. McCarthy & Tom Shatwell & Dietrich Borchardt & Erik Jeppesen & Martin Søndergaard & Torben L. Lauridsen & Thomas A. Davidson, 2024. "Consistent stoichiometric long-term relationships between nutrients and chlorophyll-a across shallow lakes," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    13. Arhonditsis, George B. & Stow, Craig A. & Paerl, Hans W. & Valdes-Weaver, Lexia M. & Steinberg, Laura J. & Reckhow, Kenneth H., 2007. "Delineation of the role of nutrient dynamics and hydrologic forcing on phytoplankton patterns along a freshwater–marine continuum," Ecological Modelling, Elsevier, vol. 208(2), pages 230-246.
    14. Clark, James R. & Daines, Stuart J. & Lenton, Timothy M. & Watson, Andrew J. & Williams, Hywel T.P., 2011. "Individual-based modelling of adaptation in marine microbial populations using genetically defined physiological parameters," Ecological Modelling, Elsevier, vol. 222(23), pages 3823-3837.
    15. Baklouti, M. & Chevalier, C. & Bouvy, M. & Corbin, D. & Pagano, M. & Troussellier, M. & Arfi, R., 2011. "A study of plankton dynamics under osmotic stress in the Senegal River Estuary, West Africa, using a 3D mechanistic model," Ecological Modelling, Elsevier, vol. 222(15), pages 2704-2721.

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