Evaluating relationships between plants, water chemistry, and denitrification potential in palustrine freshwater marshes
Wetlands are hotspots for various biogeochemical processes, including denitrification. Despite its ecological and economic importance, denitrification has proven difficult to measure. Plant community composition, water chemistry, and physical habitat characteristics are all known to play roles in re...
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| Autores principales: | , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Elsevier
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/5c60e1d8e0c54f6e8d48ebbae2b9b0d3 |
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| Sumario: | Wetlands are hotspots for various biogeochemical processes, including denitrification. Despite its ecological and economic importance, denitrification has proven difficult to measure. Plant community composition, water chemistry, and physical habitat characteristics are all known to play roles in regulating denitrification rates in wetlands. By investigating these relationships, we sought to not only better understand the factors related to denitrification within wetlands, but to build a predictive model capable of estimating the denitrification potential of those wetlands in a more accessible and efficient manner. To accomplish this, we combined a survey of 27 inland marshes throughout Michigan’s Lower Peninsula with a genetic analysis of denitrification potential within their soils. We found that denitrification, and the spatial variation therein, did not vary significantly between vegetation types at either the individual replicate or site levels. Dissolved oxygen and specific conductance were strong predictors of denitrification potential at all levels of analysis, while correlations with plant community composition varied amongst vegetation types. Spatial variation in denitrification potential, both vertically through the soil column and horizontally across the marshes, was most strongly correlated with plant community composition. Lastly, the metric-based predictive model constructed from these relationships was found to be highly predictive of denitrification potential. This model represents both a more accessible method for estimating denitrification in wetlands, and a framework for building similar models in other wetland systems. |
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