Impact of wetting and drying on soil carbon cycling and microbial community composition: An artificial soil column experiment
收藏NIAID Data Ecosystem2026-03-10 收录
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https://www.ncbi.nlm.nih.gov/sra/ERP110501
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资源简介:
Water table fluctuations in the capillary fringe lead to highly spatial heterogeneous conditions that are characterised by steep physical and chemical gradients and high microbial diversity, and are considered hotspots of biogeochemical activity. However the effect of water table fluctuations in these systems is still not well understood. We carried out an integrated column experiment to determine the effect of wetting and drying and the consequently fluctuating redox potential, on the initial development of artificial soil model systems composed from well-defined materials incubated in a soil column experiment. Three replicate columns were incubated up to 329 days under either static moisture content, or 4-week drying and wetting periods. The redox potential and greenhouse gas effluxes were monitored during incubation. Columns were sliced after 99 and 329 days and analysed for organic carbon content, microbial biomass, ATP content, and microbial community composition as determined by sequencing of 16S rRNA genes, with depth. The results show a clear effect of drying and wetting on redox potential and CO2 efflux from the columns. A depth-dependent microbial community established within 99 days, but no clear effect of treatment on species distribution was observed. Although the cumulative organic carbon loss from the columns was not significantly different between treatments, drying and wetting lead to a preferential depletion of organic carbon in the moisture and redox potential fluctuation zone after 329 days. This zone was characterized by lower microbial biomass, higher ATP contents, and a lower carbon use efficiency. Overall, this study shows that transient soil moisture and consequent oxygen availability affects microbial carbon use efficiency, which needs to be included in soil models in order to accurately predict SOC turnover.
创建时间:
2018-10-25



