JOURNAL ARTICLE
Soil pH modulates the activity of low-affinity methane oxidation in soils from the Amazon region.
Published In: Journal of Applied Microbiology, 2025, v. 136, n. 1. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Fonseca de Souza, Leandro; Nakamura, Fernanda Mancini; Kroeger, Marie; Obregon, Dasiel; de Moraes, Moacir Tuzzin; Vicente, Mariana Gomes; Moreira, Marcelo Zacharias; Pellizari, Vivian Helena; Tsai, Siu Mui; Nüsslein, Klaus 3 of 3
Abstract
This article investigates the impact of soil pH adjustment through liming on methane uptake and the active methane-cycling microbial community in Amazonian forest and pasture soils. Using stable isotope probing with ^13CH_4 at high methane concentrations (~10,000 ppm), the study found that liming increased methane oxidation by approximately 10% in forest soils and 25% in pasture soils, with a notable stimulation of methanotrophic bacteria such as Methylocaldum spp. (Gammaproteobacteria) and members of the Beijerinckiaceae family (Alphaproteobacteria) in limed pasture soils. In limed forest soils, ammonia-oxidizing archaea from the Nitrososphaeraceae family were identified as active in methane assimilation, suggesting alternative methane oxidation pathways. The findings highlight liming as a potential management practice to enhance methane oxidation and mitigate greenhouse gas emissions in degraded tropical pasture soils, while indicating a more limited effect in forest soils.
Additional Information
- Source:Journal of Applied Microbiology. 2025/01, Vol. 136, Issue 1, p1
- Document Type:Article
- Subject Area:Political Science
- Publication Date:2025
- ISSN:1364-5072
- DOI:10.1093/jambio/lxae303
- Accession Number:182905271
- Copyright Statement:Copyright of Journal of Applied Microbiology is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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