To broaden our understanding of pelagic ecosystem responses to environmental change, it is essential that we improve the spatiotemporal resolution of in situ monitoring of phytoplankton communities. A key challenge for existing methods is in classifying and quantifying cells within the nanophytoplankton size range (2–20 μm). This is particularly difficult when there are similarities in morphology, making visual differentiation difficult for both trained taxonomists and machine learning-based approaches. Here we present a rapid fluoro-electrochemical technique for classifying nanophytoplankton, and using a library of 52 diverse strains of nanophytoplankton we assess the accuracy of this technique based on two measurements at the individual level: charge required to reduce per cell chlorophyll a fluorescence by 50% and cell radius. We demonstrate a high degree of accuracy overall (92%) in categorizing cells belonging to widely recognized key functional groups; however, this is reduced when we consider the broader diversity of “nano-phytoflagellates'.” Notably, we observe that some groups, for example, calcifying Isochrysidales, have much greater resilience to electrochemically driven oxidative conditions relative to others of a similar size, making them more easily categorized by the technique. The findings of this study present a promising step forward in advancing our toolkit for monitoring phytoplankton communities. We highlight that, for improved categorization accuracy, future iterations of the method can be enhanced by measuring additional predictor variables with minimal adjustments to the set-up. In doing so, we foresee this technique being highly applicable, and potentially invaluable, for in situ classification and enumeration of the nanophytoplankton size fraction.

中文翻译：

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一种用于分类多种海洋纳米浮游植物的新型氟电化学技术

为了扩大我们对中上层生态系统对环境变化响应的理解，我们必须提高浮游植物群落原位监测的时空分辨率。现有方法的一个关键挑战是对纳米浮游植物尺寸范围（2-20  μ m）内的细胞进行分类和量化。当形态存在相似性时，这一点尤其困难，使得训练有素的分类学家和基于机器学习的方法都难以进行视觉区分。在这里，我们提出了一种用于对纳米浮游植物进行分类的快速氟电化学技术，并使用包含 52 种不同菌株的纳米浮游植物的库，我们根据个体水平的两项测量来评估该技术的准确性：将每个细胞叶绿素a荧光减少 50 所需的电荷% 和单元半径。我们展示了对属于广泛认可的关键功能组的细胞进行分类的总体准确度（92%）；然而，当我们考虑“纳米植物鞭毛虫”的更广泛多样性时，这种情况就会减少。值得注意的是，我们观察到某些群体，例如钙化等鞭藻目，相对于其他类似大小的群体，对电化学驱动的氧化条件具有更大的适应能力，使它们更容易通过该技术进行分类。这项研究的结果在推进浮游植物群落监测工具包方面向前迈出了有希望的一步。我们强调，为了提高分类准确性，可以通过测量额外的预测变量并对设置进行最小的调整来增强该方法的未来迭代。在此过程中，我们预计该技术对于纳米浮游植物尺寸分数的原位分类和计数具有高度适用性，并且具有潜在的无价价值。