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| 论文摘要： | Phytoplankton-derived dissolved organic carbon (DOC) is a major pathway for atmospheric CO2 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 (R2 = 0.92 and 0.80 for the growth and decline phases, respectively), substantially outperforming models without such data (R2 = 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. |
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