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Estimating phytoplankton stoichiometry from routinely collected monitoring data


Accurately estimating the elemental stoichiometry of phytoplankton is critical for understanding biogeochemical cycles. In laboratory experiments, stoichiometric ratios vary among species and with changes in environmental conditions. Field observations of total phosphorus (P) and total nitrogen (N) collected at regional and national scales can supplement and expand insights into factors influencing phytoplankton stoichiometry, but analyses applied to these data can introduce biases that affect interpretations of the observed patterns. We introduce an analytical approach for estimating the ratio between phytoplankton N and P from the particulate fraction of nutrient pools in lake samples. We use Bayesian models to represent observations of particulate P and N as the sum of contributions from nutrients bound within phytoplankton and nutrients associated with non-phytoplankton suspended sediment. Application of this approach to particulate nutrient data collected in Missouri impoundments yields estimates of the mass ratio of N:P in phytoplankton ranging from 8 to 10 across a variety of lakes and seasons. N:P in particulate matter ranged from 6 to 70, a variability driven by differences in nutrients bound to non-phytoplankton suspended sediment. We adapted the Bayesian models to estimate N:P using more commonly available measurements of total P and total N and applied this model to a continental-scale monitoring data set. We compared phytoplankton nutrient content estimated from the two analyses and found that when datasets lack direct measurements of particulate nutrient concentrations, the model estimate of phytoplankton nutrient content includes contributions from nutrients within phytoplankton and dissolved nutrients that are associated with changes in phytoplankton biomass.

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Data used in this analysis are publicly available at

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Analyses were conducted with R, a publicly available statistical software. Scripts specific to these analyses will be made available at


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The authors acknowledgement the data collection efforts of the National Lake Assessment sampling teams, and comments provided by G. Kaufman and B. Walsh. Missouri data were collected with support of the Missouri Department of Natural Resources, Missouri Agricultural Experiment Station and Food & Agriculture Policy Research Institute. We specifically thank Daniel Obrecht and Jennifer Graham for data collection and Carol Pollard for analytical work. The views expressed in this paper are those of the authors and do not reflect official policies of the U.S. Environmental Protection Agency.


This research was conducted as part of the author’s duties as an employee of the U.S. Environmental Protection Agency.

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Correspondence to Lester L. Yuan.

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Yuan, L.L., Jones, J.R. Estimating phytoplankton stoichiometry from routinely collected monitoring data. Biogeochemistry 159, 251–264 (2022).

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  • Lake
  • Nitrogen
  • Phosphorus
  • Phytoplankton
  • Redfield ratio
  • Stoichiometry