海洋浮游植物初级生产力及碳生物量的检测技术研究进展

浮游植物是海洋生态系统中的主要初级生产者，构建海洋食物网、生物泵和元素循环（包括碳循环、氮循环和硅循环等）的基石。因此，海洋生态系统中的元素循环和能量流动均与浮游植物的生长和代谢息息相关。海洋碳循环是全球碳循环的关键环节，也是全球生态系统中生物地化循环的重要组成部分。尽管浮游植物在海洋碳循环中起着至关重要的作用，但是直接测定浮游植物的初级生产力和碳生物量依旧受到传统技术和方法的限制。本文详细介绍了有关浮游植物初级生产力和碳生物量检测的各种技术和方法，列举了其各自的优缺点。目前，测定海洋浮游植物初级生产力的主要方法有黑白瓶法、遥感估算法、碳同位素测定、快速重复率荧光法；测定海洋浮游植物碳生物量的主要方法有细胞体积转换法、流式细胞术、电子探针X射线显微分析、分位数回归模型估算法。通过对比分析发现碳同位素与快速重复率荧光法相结合可以更高效测定出初级生产力，而最具优势与应用前景的碳生物量检测方法是基于分位数回归模型估算法。其中，基于分位数回归模型估算法具有拟合异常值、测定结果准确等优势，能够实现现场浮游植物群落以及各个功能群碳生物量的估算，并能够与卫星遥感技术手段相结合，可以应用于大尺度和长时间序列的海洋浮游植物碳生物量估算。通过本文的综述，一方面为海洋浮游植物初级生产力和碳含量的研究提供一个基本和系统的认识，另一方面为深入研究浮游植物在海洋碳循环以及全球碳循环中的作用提供参考。

Advances in primary productivity and carbon biomass detection of marine phytoplankton

Phytoplankton are major primary producers in marine ecosystems, supporting marine food webs, biological pumps, and elemental cycles (carbon, nitrogen, and silicon cycles). Therefore, elemental cycles and energy flow in marine ecosystems are closely related to phytoplankton growth and metabolism. The marine carbon (C) cycle is key to the global C cycle and an important part of the biogeochemical cycle. Although phytoplankton plays a critical role in the marine C cycle, the direct determination of phytoplankton primary productivity and C biomass remains limited to traditional methods. In this review, various methods associated with phytoplankton primary productivity and C biomass estimation are discussed in detail, along with their respective advantages and disadvantages. Currently, the main methods for determining the primary productivity of marine phytoplankton include the black-white bottle method, remote sensing estimation method, C isotope determination, and fast repetition rate fluorescence method (FRRF). The primary methods for determining the C biomass of marine phytoplankton include cell volume conversion, flow cytometry, electron probe X-ray microanalysis, and quantile regression model estimation. Through comparative analysis, we established that combining the C isotope and FRRF method can determine primary productivity more efficiently, while the most promising method for C biomass detection was quantile regression model estimation. This regression model has the advantages of fitting outliers and providing accurate measurement results, including C biomass estimations for phytoplankton communities and each functional group in the field. The approach can also be combined with satellite remote sensing technology to estimate biomass on a large scale and for a prolonged period. Although this review offers a basic and systematic understanding of phytoplankton primary productivity and C biomass analysis, it provides a valuable reference for future research on the role of phytoplankton in the marine and global C cycles.