Microbial Community Succession Along a Chronosequence in Constructed Salt Marsh Soils

Microbial ecology(2023)

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摘要
In this study, we examined the succession of soil microbial communities across a chronosequence of newly constructed salt marshes constructed primarily of fine-grained dredge material, using 16S rRNA amplicon sequences. Alpha diversity in the subsurface horizons was initially low and increased to reference levels within 3 years of marsh construction, while alpha diversity in the newly accumulating organic matter-rich surface soils was initially high and remained unchanged. Microbial community succession was fastest in the surface horizon (~ 24 years to reference equivalency) and became progressively slower with depth in the subsurface horizons (~ 30–67 years). Random forest linear regression analysis was used to identify important taxa driving the trajectories toward reference conditions. In the parent material, putative sulfate-reducers ( Desulfobacterota ), methanogens ( Crenarchaeota , especially Methanosaeta ), and fermenters ( Chloroflexi and Clostridia ) increased over time, suggesting an enrichment of these metabolisms over time, similar to natural marshes. Concurrently in the surface soils, the relative abundances of putative methane-, methyl-, and sulfide oxidizers, especially among Gammaproteobacteria , increased over time, suggesting the co-development of sulfide and methane removal metabolisms in marsh soils. Finally, we observed that the surface soil communities at one of the marshes did not follow the trajectory of the others, exhibiting a greater relative abundance of anaerobic taxa. Uniquely in this dataset, this marsh was developing signs of excessive inundation stress in terms of vegetation coverage and soil geochemistry. Therefore, we suggest that soil microbial community structure may be effective bioindicators of salt marsh inundation and are worthy of further targeted investigation.
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关键词
Salt marsh restoration,Salt marsh construction,Microbial indicators,Chesapeake Bay,Poplar Island,Regression random forest analysis
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