第一作者：梁婕 教授

通讯作者：梁婕 教授

论文DOI：10.1016/j.watres.2025.124496

全文速览

湖泊是生物地球化学过程的重要调节器，也是N₂O排放的热点，同时保留了大量的磷负荷。然而，总磷（TP）在形成N₂O过程中的作用仍然知之甚少。本文采用基于国家水质标准的分类框架，通过GRiMe数据集和机器学习方法进行验证，研究了TP介导的洞庭湖N₂O浓度变化。结果显示，TP通过复杂的微生物途径强烈影响N₂O动态。低TP条件下，N₂O浓度显著升高。随着TP的增加，微生物群落结构发生变化，氮循环基因丰度显著提高，反硝化途径更完整，增强了N₂O还原能力。然而，这种作用并非线性，高碳氮水平会放大N₂O产生，而过量TP可能引发生物毒性效应，降低缓解能力。总体而言，该研究为TP如何影响淡水生态系统中N₂O浓度提供了新的机制见解，并强调了磷管理在气候变化中缓解富营养化和减少温室气体排放战略中的关键重要性。

图文摘要

图文导读

Figure 1. Sampling sites in Dongting Lake

本文基于TP浓度分布及中国《地表水环境质量标准》（GB 3838-2002），建立了湖泊TP分类框架，并用  GRiMe全球数据库结合RF、SHAP和GAM进行验证。结果显示，TP对N₂O的影响呈U型趋势，在   0.10  mg/L和1.58 mg/L出现两个拐点，第一个拐点与国家标准高度一致，支持我们将洞庭湖样品分为低TP（<0.10 mg/L）和中TP（0.10–1.58 mg/L）两类 (Fig. S1)。

Figure 2. (a) PCoA of bacterial communities based on Bray-Curtis distance for L and M groups; (b) Comparison of Beta diversity of bacterial communities between L and M groups.

TP显著影响微生物群落多样性、结构和组装机制：低TP下，古菌α多样性显著更高，细菌群落呈更强随机性；而在中等TP水平，β多样性和群落异质性升高，古菌和真菌的随机性增强。不同域呈现差异化响应，反映了TP调控下的生态策略变化，并可能影响N₂O的浓度(Fig. S3-S6)。

Figure 3. Lefse multi-level species discrimination analysis between L and M groups in Dongting Lake.

微生物组成分析显示，L组中与反硝化过程相关细菌属的相对丰度高于M组，如：Pseudarthrobacter、Flavobacterium和Sideroxydans，可能促进N₂O生成。古菌中，氨氧化古菌Candidatus Nitrosocosmicus在M组显著富集，而该菌在硝化及N₂O产生中具有重要作用 (Fig. S7 and S8)。

Figure 4. Comparative analysis of 12 physical and chemical parameters between L and M groups in Dongting Lake. Asterisks in the figures indicate statistical significance: *0.01 < P ≤ 0.05, **0.001 < P ≤ 0.01, ***P ≤ 0.001.

Figure 5. CCA analysis based on bacterial OTU level in Dongting Lake L group (a) and M group (b). The red arrows in the two figures are significant. (P<0.05)

Figure 6. Heatmaps of correlations between environmental factors and bacteria at the genus level in the (a) L group and (b) M group of Dongting Lake. Different colors in the heatmap represent positive and negative correlations, with the intensity of the color indicating the strength of the correlation. Asterisks within the color blocks denote statistical significance: *0.01 < P ≤ 0.05, **0.001 < P ≤ 0.01, ***P ≤ 0.001.

Figure 7. Mantel test analysis of environmental factors and microbial community structure for the L and M groups in Dongting Lake. The lines in the figure represent the correlation between the microbial communities and environmental factors, while the heatmap shows the correlation among environmental factors. The thickness of the lines reflects the strength of the correlation between the communities and environmental factors, based on the absolute value of Mantel’s r (|R|). The “relationship” indicates whether the correlation is positive or negative. Different colors in the heatmap represent positive and negative correlations, with the intensity of the color indicating the strength of the correlation. Asterisks within the color blocks denote statistical significance: *0.01 < P ≤ 0.05, **0.001 < P ≤ 0.01, ***P ≤ 0.001.

Figure 8. PLS-SEM Framework and Results for Environmental, Microbial, and Functional Gene Abundance Effects on N₂O Concentrations. Large rectangles represent latent variables, and small rectangles represent observed variables. The latent variable Microbial diversity includes bacterial (Bac), archaeal (Arc), and fungal (Fun) diversity metrics, measured by ACE (Ace) and Shannon (Sha) indices. Functional gene abundance refers to the quantified abundance of key N₂O-related functional genes. Black thick solid lines indicate significant effects (p < 0.05), while black thick dashed lines indicate non-significant effects (p > 0.05). Numbers on thick black lines are structural path coefficients, and numbers on thin blue lines are indicator loadings.

TP和N₂O浓度之间的相互作用主要由微生物功能潜力介导，而非直接的化学效应。路径分析进一步表明，TP 主要通过两种途径影响 N₂O 浓度：增强功能基因的丰度和降低微生物多样性。此外，TP也可能通过影响水质，进而调控微生物对N₂O的产生。

Figure 9. Comparative analysis of functional genes between DTL L and M groups. Asterisks in the figures indicate statistical significance: *0.01 < P ≤ 0.05, **0.001 < P ≤ 0.01, ***P ≤ 0.001.

小结与展望

本研究首次在大型淡水湖泊尺度上，系统揭示了TP通过微生物途径调控N₂O排放的机制，并强调了TP效应的非线性与情境依赖性。研究不仅为理解磷负荷、微生物过程与温室气体排放之间的复杂关系提供了新证据，也为制定基于营养盐管理的减排策略提供了科学支撑。面对气候变化与富营养化双重压力，如何在控制TP的同时，协同管理碳氮元素，将是未来淡水生态系统温室气体减排的关键方向。