Current estimates of wetland contributions to the global methane budget carry high uncertainty, particularly in accurately predicting emissions from high methane-emitting wetlands. Microorganisms drive methane cycling, but little is known about their conservation across wetlands. To address this, we integrate 16S rRNA amplicon datasets, metagenomes, metatranscriptomes, and annual methane flux data across 9 wetlands, creating the Multi-Omics for Understanding Climate Change (MUCC) v2.0.0 database. This resource is used to link microbiome composition to function and methane emissions, focusing on methane-cycling microbes and the networks driving carbon decomposition. We identify eight methane-cycling genera shared across wetlands and show wetland-specific metabolic interactions in marshes, revealing low connections between methanogens and methanotrophs in high-emitting wetlands. Methanoregula emerged as a hub methanogen across networks and is a strong predictor of methane flux. In these wetlands it also displays the functional potential for methylotrophic methanogenesis, highlighting the importance of this pathway in these ecosystems. Collectively, our findings illuminate trends between microbial decomposition networks and methane flux while providing an extensive publicly available database to advance future wetland research.
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http://dx.doi.org/10.1038/s41467-025-56133-0 | DOI Listing |
Nat Commun
January 2025
Department of Soil and Crop Sciences, Colorado State University, Fort Collins, CO, USA.
Current estimates of wetland contributions to the global methane budget carry high uncertainty, particularly in accurately predicting emissions from high methane-emitting wetlands. Microorganisms drive methane cycling, but little is known about their conservation across wetlands. To address this, we integrate 16S rRNA amplicon datasets, metagenomes, metatranscriptomes, and annual methane flux data across 9 wetlands, creating the Multi-Omics for Understanding Climate Change (MUCC) v2.
View Article and Find Full Text PDFNew Phytol
January 2025
Centre of Excellence PLECO (Plants and Ecosystems), Department of Biology, University of Antwerp, Universiteitsplein 1, B-2610, Wilrijk, Belgium.
Recent studies have shown that stem fluxes, although highly variable among trees, can alter the strength of the methane (CH) sink or nitrous oxide (NO) source in some forests, but the patterns and magnitudes of these fluxes remain unclear. This study investigated the drivers of subdaily and seasonal variations in stem and soil CH, NO and carbon dioxide (CO) fluxes. CH, NO and CO fluxes were measured continuously for 19 months in individual stems of two tree species, Eperua falcata (Aubl.
View Article and Find Full Text PDFNat Commun
January 2025
Research Center for Solar Driven Carbon Neutrality, School of Physics Science and Technology, In-stitute of Life Science and Green Development, Hebei University, Baoding, 071002, PR China.
Photo-oxidation of methane (CH) using hydrogen peroxide (HO) synthesized in situ from air and water under sunlight offers an attractive route for producing green methanol while storing intermittent solar energy. However, in commonly used aqueous-phase systems, photocatalysis efficiency is severely limited due to the ultralow availability of CH gas and HO intermediate at the flooded interface. Here, we report an atomically modified metal-organic framework (MOF) membrane nanoreactor that promotes direct CH photo-oxidation to methanol at the gas-solid interface in a reticular open framework.
View Article and Find Full Text PDFSci Total Environ
January 2025
Sarawak Tropical Peat Research Institute, Kota Samarahan, Malaysia.
Tropical peatlands are significant sources of methane (CH₄), but their contribution to the global CH₄ budget remains poorly quantified due to the lack of long-term, continuous and high-frequency flux measurements. To address this gap, we measured net ecosystem CH exchange (NEE-CH) using eddy covariance technique throughout the conversion of a tropical peat swamp forest to an oil palm plantation. This encompassed the periods before, during and after conversion periods from 2014 to 2020, during which substantial environmental shifts were observed.
View Article and Find Full Text PDFEnviron Res
January 2025
Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China; Earth Critical Zone and Flux Research Station of Xing'an Mountains, Chinese Academy of 15 Sciences, Daxing'anling, 165200, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 10049, China. Electronic address:
Accurate quantifying of methane (CH) emissions is a critical aspect of current research on regional carbon budgets. However, due to limitations in observational data, research methodologies, and an incomplete understanding of process mechanisms, significant uncertainties persist in the assessment of wetland CH fluxes in China. In this study, we developed a machine learning model by integrating measured CH fluxes with related environmental data to produce a high-resolution (1 km) dataset of CH fluxes from China's wetlands for the period 2000-2020.
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