Abstract
Shifts in agricultural land use over the past 200 years have led to a loss of nearly 50% of existing wetlands in the USA, and agricultural activities contribute up to 65% of the nutrients that reach the Mississippi River Basin, directly contributing to biological disasters such as the hypoxic Gulf of Mexico “Dead” Zone. Federal efforts to construct and restore wetland habitats have been employed to mitigate the detrimental effects of eutrophication, with an emphasis on the restoration of ecosystem services such as nutrient cycling and retention. Soil microbial assemblages drive biogeochemical cycles and offer a unique and sensitive framework for the accurate evaluation, restoration, and management of ecosystem services. The purpose of this study was to elucidate patterns of soil bacteria within and among wetlands by developing diversity profiles from high-throughput sequencing data, link functional gene copy number of nitrogen cycling genes to measured nutrient flux rates collected from flow-through incubation cores, and predict nutrient flux using microbial assemblage composition. Soil microbial assemblages showed fine-scale turnover in soil cores collected across the topsoil horizon (0–5 cm; top vs bottom partitions) and were structured by restoration practices on the easements (tree planting, shallow water, remnant forest). Connections between soil assemblage composition, functional gene copy number, and nutrient flux rates show the potential for soil bacterial assemblages to be used as bioindicators for nutrient cycling on the landscape. In addition, the predictive accuracy of flux rates was improved when implementing deep learning models that paired connected samples across time.
| Original language | English |
|---|---|
| Article number | 22 |
| Journal | Microbial Ecology |
| Volume | 88 |
| Issue number | 1 |
| DOIs | |
| State | Published - Dec 2025 |
| Externally published | Yes |
Funding
This work was supported by the Natural Resource Conservation Services sub-award 220858, National Science Foundation grants EF-2125065 and CAREER 2236580 to D. Walker, the Molecular Biosciences Program at MTSU, and National Science Foundation grant DEB 1933925 to J. Phillips. The authors would like to thank Alexander Romer, Steven Levenhagen for their technical assistance on this project. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation, or Natural Resource Conservation Service. This work was supported by the Natural Resource Conservation Services sub-award 220858, the National Science Foundation grants EF-2125065 and CAREER 2236580 to D. Walker, the Molecular Biosciences Program at MTSU, and the National Science Foundation grant DEB 1933925 to J. Phillips.
Keywords
- Biogeochemical cycling
- Deep learning
- Nitrogen cycle
- Nutrient cycling
- Wetland restoration