Department of Plant Physiology, Institute of Biology, Leipzig University, Johannisallee 21-23, D-04103, Leipzig, Germany.
German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, D-04103, Leipzig, Germany.
J Phycol. 2019 Jun;55(3):543-551. doi: 10.1111/jpy.12858. Epub 2019 Apr 29.
Statistical growth rate modelling can be applied in a variety of ecological and biotechnological applications. Such models are frequently based on Monod or Droop equations and, especially for the latter, require reliable determination of model input parameters such as C:N quotas. Besides growth rate modelling, a C:N quota quantification can be useful for monitoring and interpretation of physiological acclimation to abiotic and biotic disturbances (e.g., nutrient limitations). However, as high throughput C:N quota determination is difficult to perform, alternatives need to be established. Fourier-transformed infrared (FTIR) spectroscopy is used to analyze a variety of biochemical, chemical, and physiological parameters in phytoplankton. Hence, a quantification of the C:N quota should also be feasible. Therefore, using FTIR spectroscopy, six phytoplankton species from among different phylogenetic groups have been analyzed to determine the effect of nutrient limitation on C:N quota patterns. The typical species-specific response to increasing nitrogen limitation was an increase in the C:N quota. Irrespective of this species specificity, we were able to develop a reliable multi-species C:N quota prediction model based on FTIR spectroscopy using the partial least square regression (PLSR) algorithm. Our data demonstrate that the PLSR approach is more robust in C:N quota quantification (R = 0.93) than linear correlation of C:N quota versus growth rate (R ranges from 0.74 to 0.86) or biochemical information based on FTIR spectra (R ranges from 0.82 to 0.89). This accurate prediction of C:N values may support high throughput measurements in a broad range of future approaches.
统计增长率模型可应用于各种生态和生物技术应用。这些模型通常基于 Monod 或 Droop 方程，特别是对于后者，需要可靠地确定模型输入参数，如 C:N 比例。除了生长率建模外，C:N 比例的量化对于监测和解释对非生物和生物干扰的生理适应（例如营养限制）也很有用。然而，由于高通量 C:N 比例的测定较为困难，因此需要建立替代方法。傅里叶变换红外（FTIR）光谱用于分析浮游植物中的各种生化、化学和生理参数。因此，C:N 比例的量化也应该是可行的。因此，使用 FTIR 光谱，分析了来自不同进化群的六种浮游植物物种，以确定营养限制对 C:N 比例模式的影响。典型的物种特异性对氮限制增加的反应是 C:N 比例增加。尽管存在这种物种特异性，但我们仍然能够使用偏最小二乘回归（PLSR）算法基于 FTIR 光谱开发出可靠的多物种 C:N 比例预测模型。我们的数据表明，PLSR 方法在 C:N 比例定量（R=0.93）方面比 C:N 比例与生长率的线性相关（R 范围从 0.74 到 0.86）或基于 FTIR 光谱的生化信息（R 范围从 0.82 到 0.89）更稳健。这种 C:N 值的准确预测可能会支持未来广泛应用中的高通量测量。
