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.

准确估计浮游植物的元素化学计量对于了解生物地球化学循环至关重要。在实验室实验中，化学计量比因物种和环境条件的变化而异。在区域和国家尺度收集的总磷 (P) 和总氮 (N) 的现场观察可以补充和扩展对影响浮游植物化学计量的因素的见解，但对这些数据进行分析可能会引入影响对观察到的模式的解释的偏差。我们引入了一种分析方法，用于根据湖泊样本中营养物库的颗粒部分来估计浮游植物 N 和 P 之间的比率。我们使用贝叶斯模型将颗粒 P 和 N 的观测值表示为浮游植物内结合的营养物和与非浮游植物悬浮沉积物相关的营养物的贡献之和。将这种方法应用于密苏里州蓄水池收集的颗粒养分数据，可以估算出各种湖泊和季节浮游植物中 N:P 的质量比，范围为 8 到 10。颗粒物中的 N:P 范围为 6 至 70，这是由与非浮游植物悬浮沉积物结合的营养物差异驱动的变化。我们采用贝叶斯模型来使用更常见的总 P 和总 N 测量值来估计 N:P，并将该模型应用于大陆规模的监测数据集。我们比较了两次分析估计的浮游植物养分含量，发现当数据集缺乏颗粒养分浓度的直接测量时，浮游植物养分含量的模型估计包括浮游植物内养分和与浮游植物生物量变化相关的溶解养分的贡献。