Interactions between microbiomes and metabolites play crucial roles in the environment, yet how these interactions drive greenhouse gas emissions during ecosystem changes remains unclear. Here we analysed microbial and metabolite composition across a permafrost thaw gradient in Stordalen Mire, Sweden, using paired genome-resolved metagenomics and high-resolution Fourier transform ion cyclotron resonance mass spectrometry guided by principles from community assembly theory to test whether microorganisms and metabolites show concordant responses to changing drivers. Our analysis revealed divergence between the inferred microbial versus metabolite assembly processes, suggesting distinct responses to the same selective pressures. This contradicts common assumptions in trait-based microbial models and highlights the limitations of measuring microbial community-level data alone. Furthermore, feature-scale analysis revealed connections between microbial taxa, metabolites and observed CO₂ and CH₄ porewater variations. Our study showcases insights gained by using feature-level data and microorganism–metabolite interactions to better understand metabolic processes that drive greenhouse gas emissions during ecosystem changes.

微生物组和代谢物之间的相互作用在环境中起着至关重要的作用，但这些相互作用如何在生态系统变化期间推动温室气体排放仍不清楚。在这里，我们分析了瑞典 Stordalen Mire 永久冻土融化梯度中的微生物和代谢物组成，使用配对基因组分辨宏基因组学和高分辨率傅里叶变换离子回旋共振质谱法，以群落组装理论的原理为指导，以测试微生物和代谢物是否对不断变化的驱动因素表现出一致的反应。我们的分析揭示了推断的微生物与代谢物组装过程之间的差异，表明对相同选择压力的反应不同。这与基于性状的微生物模型中的常见假设相矛盾，并突出了单独测量微生物群落水平数据的局限性。此外，特征尺度分析揭示了微生物分类群、代谢物与观察到的 CO₂ 和 CH₄ 孔隙水变化之间的联系。我们的研究展示了通过使用特征级数据和微生物-代谢物相互作用来更好地了解生态系统变化期间驱动温室气体排放的代谢过程而获得的见解。