| 引用本文: | 王庆轩,崔正国,曲克明,王庆奎,魏玉秋,孙军.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[J].海洋科学,2023,47(8):131-140. |
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| 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 |
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王庆轩1,2,3, 崔正国2,3, 曲克明2,3, 王庆奎1, 魏玉秋2,3, 孙军4
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1.天津农学院 水产学院 天津市水产生态及养殖重点实验室, 天津, 300392;2.中国水产科学研究院黄海水产研究所 农业农村部海洋渔业可持续发展重点实验室, 山东 青岛 266071;3.青岛海洋科学与技术试点国家实验室海洋渔业科学与食物产出过程功能实验室, 山东 青岛 266071;4.中国地质大学广州南沙地大滨海研究院, 广东 广州 511462
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| 摘要: |
| 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。 |
| 关键词: 浮游植物 初级生产力 碳生物量 碳循环 检测方法 分位数回归模型 |
| DOI：10.11759/hykx20230128003 |
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| 基金项目:国家自然科学基金资助项目（42206103）；中国博士后科学基金资助项目（2021M703590）；山东省博士后创新人才支持计划项目（SDBX2021014）；山东省自然科学基金资助项目（ZR2022QD133） |
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| Advances in primary productivity and carbon biomass detection of marine phytoplankton |
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WANG Qing-xuan1,2,3, CUI Zheng-guo2,3, QU Ke-ming2,3, WANG Qing-kui1, WEI Yu-qiu2,3, SUN Jun4
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1.Tianjin Key Laboratory of Aquatic Ecology and Aquaculture, College of Fisheries, Tianjin Agricultural University, Tianjin 300392, China;2.Key Laboratory of Sustainable Development of Marine Fisheries, Ministry of Agriculture and Rural Affairs, Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao 266071, China;3.Qingdao Pilot National Laboratory of Marine Science and Technology Marine Fishery Science and Food Production Process Function Laboratory, Qingdao 266071, China;4.Institute for Advanced Marine Research, China University of Geosciences, Guangzhou 511462, China
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| Abstract: |
| 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. |
| Key words: phytoplankton primary productivity carbon biomass carbon cycle detection method quantile regression model |
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