2004/01/31 by Christopher A. Klausmeier, Elena Litchman, Simon A. Levin · 360 citations
Chemistry · Earth and Planetary Sciences · Energy · Environmental Science · #Algal biology and biofuel production #Aquatic Ecosystems and Phytoplankton Dynamics #Biology #Chemistry #Chemostat #Dilution #Ecological stoichiometry #Ecology #Marine and coastal ecosystems #Nutrient #Physical chemistry #Physics #Phytoplankton #Stoichiometry #Thermodynamics
paper · doi:10.4319/lo.2004.49.4_part_2.1463
published in Limnology and Oceanography 49(4part2), 1463-1470 (Wiley)
openalex publication_date 2004/01/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/15
Phytoplankton growth and stoichiometry depend on the availability of multiple nutrients. We use a mathematical model of phytoplankton with flexible stoichiometry to explain patterns of phytoplankton composition in chemostat experiments and nutrient drawdown dynamics that are found in the field. Exponential growth and equilibrium represent two distinct phases, each amenable to mathematical analysis. In a chemostat at a fixed dilution (growth) rate, phytoplankton stoichiometry matches the nutrient supply stoichiometry over a wide range at low growth rates and over a narrow range at high growth rates. In a chemostat with a fixed nutrient supply stoichiometry, phytoplankton stoichiometry varies with dilution rate nonlinearly, between the supply stoichiometry at low dilution rates and a species‐specific optimal ratio at high dilution rates. The flexible‐stoichiometry model we study predicts low equilibrium concentrations of two nutrients over a wide range of supply ratios, contrary to the predictions of a traditional fixed‐stoichiometry model. The model is in quantitative agreement with experimental data, except at extreme nutrient supply ratios, which require a negative feedback from quota to uptake to fit the data. Our analysis points to the importance of better understanding the regulation of uptake rates in determining phytoplankton stoichiometry and incorporating this knowledge into phytoplankton models.