Divergence of microbial carbon use efficiency and soil organic carbon along a tidal flooding gradient in a subtropical coastal wetland

IF 11.4 1区 环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL Water Research Pub Date : 2025-03-21 DOI:10.1016/j.watres.2025.123527
Ji Tan, Jiafang Huang, Wenhui Quan, Lifei Su, Yi Liu, YuanBin Cai, Shihua Li, Pingping Guo, Min Luo
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Abstract

Microbial carbon use efficiency (CUE) typically promotes soil organic carbon (SOC) storage in terrestrial ecosystems. However, this relationship remains poorly understood in coastal wetlands, where tidal flooding creates unique environmental conditions, facilitates lateral transfer and SOC loss, and mediates organic matter exchange between terrestrial and marine systems. Here we examined the CUE-SOC relationship across a tidal flooding gradient (4–25% frequency) in a subtropical coastal wetland. Along this gradient, SOC decreased by 65% while microbial CUE increased from 0.24 to 0.32. This inverse relationship coincided with marked compositional shifts: plant debris declined from 57% to 18%, while microbial necromass increased from 21% to 35%. The enhanced CUE was accompanied by increased turnover times alongside decreased metabolic quotient (qCO2), C-acquiring enzyme activities, soil basal respiration, and microbial biomass carbon (MBC). This enhanced efficiency stemmed from substrate-microbe interactions rather than environmental stresses, as communities transitioned from oligotrophic taxa (α-proteobacteria, Basidiomycota) specializing in recalcitrant terrestrial substrates to copiotrophic microorganisms (γ-proteobacteria, Bacteroidota, Ascomycota) efficiently metabolizing labile marine compounds. Contrary to terrestrial patterns, enhanced CUE did not promote SOC storage due to three key mechanisms: (i) enhanced CUE from marine substrates could not compensate for declining plant debris accumulation; (ii) reduced microbial biomass limited necromass formation despite higher CUE; and (iii) metabolic benefits from high CUE (reduced enzyme activities and respiration rates) could not offset the substantial decrease in SOC inputs. Our findings reveal distinct CUE-SOC relationships in coastal wetlands compared to terrestrial ecosystems, highlighting the importance of considering both terrestrial and marine processes in understanding carbon cycling in these transitional environments.

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来源期刊
Water Research
Water Research 环境科学-工程:环境
CiteScore
20.80
自引率
9.40%
发文量
1307
审稿时长
38 days
期刊介绍: Water Research, along with its open access companion journal Water Research X, serves as a platform for publishing original research papers covering various aspects of the science and technology related to the anthropogenic water cycle, water quality, and its management worldwide. The audience targeted by the journal comprises biologists, chemical engineers, chemists, civil engineers, environmental engineers, limnologists, and microbiologists. The scope of the journal include: •Treatment processes for water and wastewaters (municipal, agricultural, industrial, and on-site treatment), including resource recovery and residuals management; •Urban hydrology including sewer systems, stormwater management, and green infrastructure; •Drinking water treatment and distribution; •Potable and non-potable water reuse; •Sanitation, public health, and risk assessment; •Anaerobic digestion, solid and hazardous waste management, including source characterization and the effects and control of leachates and gaseous emissions; •Contaminants (chemical, microbial, anthropogenic particles such as nanoparticles or microplastics) and related water quality sensing, monitoring, fate, and assessment; •Anthropogenic impacts on inland, tidal, coastal and urban waters, focusing on surface and ground waters, and point and non-point sources of pollution; •Environmental restoration, linked to surface water, groundwater and groundwater remediation; •Analysis of the interfaces between sediments and water, and between water and atmosphere, focusing specifically on anthropogenic impacts; •Mathematical modelling, systems analysis, machine learning, and beneficial use of big data related to the anthropogenic water cycle; •Socio-economic, policy, and regulations studies.
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