Lu Zhe, Qin Guoming, Zheng Lingling, Zhang Yanyan, Huang Lincheng, Zhou Jinge, Liu Yongxin, Zheng Mianhai, Hou Enqing, Song Lirong, Liu Hongbin, Jiao Nianzhi, Wang Faming
Guangdong Provincial Key Laboratory of Applied Botany, Xiaoliang Research Station for Tropical Coastal Ecosystems, and Key Laboratory of Vegetation Restoration and Management of Degraded Ecosystems, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou, P. R. China.
South China National Botanical Garden, Guangzhou, P.R. China.
Nat Commun. 2025 Aug 20;16(1):7742. doi: 10.1038/s41467-025-63105-x.
Phytoplankton-derived dissolved organic carbon (DOC) is a major pathway for atmospheric CO transfer to long-lived oceanic DOC reservoirs. Yet, current models rarely accounted for its molecular and taxonomic heterogeneity across growth seasons. Here, using ultra-high-resolution mass spectrometry (FT-ICR MS), we characterized DOC molecular signatures across diverse algal taxa. Recalcitrant DOC accounted for over 10% of their total organic carbon in all algal groups, highlighting a widespread and previously underappreciated trait. Additionally, we integrated these signatures with satellite-derived, taxon-resolved chlorophyll-a concentrations to develop machine learning models for predicting overall surficial DOC concentrations. Including taxon-specific carbon allocation markedly improved model performance (R = 0.92 and 0.80 for the growth and decline phases, respectively), substantially outperforming models without such data (R = 0.69 and 0.46). Furthermore, leveraging these optimized models, we generated a global marine DOC dataset and found that diatoms explained up to 63.8% of the variance in surface DOC. We further showed that algal recalcitrant DOC production was significantly higher during growth than decline seasons globally. These findings offer insights into how bloom duration and climate-driven shifts in phytoplankton composition reshape oceanic DOC dynamics.
浮游植物衍生的溶解有机碳(DOC)是大气中的碳转移到长期存在的海洋DOC库的主要途径。然而,目前的模型很少考虑其在生长季节的分子和分类异质性。在这里,我们使用超高分辨率质谱(傅里叶变换离子回旋共振质谱,FT-ICR MS)对不同藻类类群的DOC分子特征进行了表征。在所有藻类类群中,难降解DOC占其总有机碳的10%以上,突出了一个普遍存在但以前未被充分认识的特征。此外,我们将这些特征与卫星衍生的、分类解析的叶绿素a浓度相结合,开发了用于预测表层DOC总体浓度的机器学习模型。纳入特定分类群的碳分配显著提高了模型性能(生长阶段和衰退阶段的R分别为0.92和0.80),大大优于没有此类数据的模型(R分别为0.69和0.46)。此外,利用这些优化模型,我们生成了一个全球海洋DOC数据集,发现硅藻解释了表层DOC高达63.8%的变化。我们进一步表明,全球范围内,藻类难降解DOC的产量在生长季节显著高于衰退季节。这些发现为水华持续时间和气候驱动的浮游植物组成变化如何重塑海洋DOC动态提供了见解。