Keunyea Song

Creating Wetlands: Primary Succession, Water Quality Changes, and Self-Design over 15 Years

The succession of vegetation, soil development, water quality changes, and carbon and nitrogen dynamics are summarized in this article for a pair of 1-hectare flow-through-created riverine wetlands for their first 15 years. Wetland plant richness increased from 13 originally planted species to 116 species overall after 15 years, with most of the increase occurring in the first 5 years. The planted wetland had a higher plant community diversity index for 15 years, whereas the unplanted wetland was more productive. Wetland soils turned hydric within a few years; soil organic carbon doubled in 10 years and almost tripled in 15 years. Nutrient removal was similar in the two wetlands in most years, with a trend of decreased removal over 15 years for phosphorus. Denitrification accounted for a small percentage of the nitrogen reduction in the wetlands. The wetlands were effective carbon sinks with retention rates of 1800–2700 kilograms of carbon per hectare per year, higher than in comparable reference wetlands and more commonly studied boreal peatlands. Methane emission rates are low enough to create little concern that the wetlands are net sources of climate change radiative forcing. Planting appears to have influenced carbon accumulation, methane emissions, and macrophyte community diversity.

The application of Artificial Neural Network (ANN) model to the simulation of denitrification rates in mesocosm-scale wetlands

Abstract Denitrification and its regulating factors are of great importance to aquatic ecosystems, as denitrification is a critical process to nitrogen removal. Additionally, a by-product of denitrification, nitrous oxide, is a much more potent greenhouse gas than carbon dioxide. However, the estimation of denitrification rates is usually clouded with uncertainty, mainly due to high spatial and temporal variations, as well as complex regulating factors within wetlands. This hampers the development of general mechanistic models for denitrification as well, as most previously developed models were empirical or exhibited low predictability with numerous assumptions. In this study, we tested Artificial Neural Network (ANN) as an alternative to classic empirical models for simulating denitrification rates in wetlands. ANN, multiple linear regression (MLR) with two different methods, and simplified mechanistic models were applied to estimate the denitrification rates of 2-year observations in a mesocosm-scale constructed wetland system. MLR and simplified mechanistic models resulted in lower prediction power and higher residuals compared to ANN. Although the stepwise linear regression model estimated similar average values of denitrification rates, it could not capture the fluctuation patterns accurately. In contrast, ANN model achieved a fairly high predictability, with an R 2 of 0.78 for model validation, 0.93 for model calibration (training), and a low root mean square error (RMSE) together with low bias, indicating a high capacity to simulate the dynamics of denitrification. According to a sensitivity analysis of the ANN, non-linear relationships between input variables and denitrification rates were well explained. In addition, we found that water temperature, denitrifying enzyme activity (DEA), and DO accounted for 70% of denitrification rates. Our results suggest that the ANN developed in this study has a greater performance in simulating variations in denitrification rates than multivariate linear regressions or simplified nonlinear mechanistic model.

Effects of Phragmites invasion on soil microbial activity and structure in a brackish marsh

Background and aimsThe spread of invasive plants in wetlands associated with human activity has become a serious environmental problem because of the negative effects of these species on biodiversity and biogeochemistry in ecosystems. Unlike their impacts on aboveground biodiversity, the responses of soil microbial communities and related soil characteristics to invasive plants are largely unknown. In this study, we assessed the structural and functional responses of soil microorganisms and belowground biogeochemistry to the invasion of Phragemites australis, which has heavily invaded in wetland areas globally, in brackish marsh areas in Korea.MethodsWe measured soil biogeochemical characteristics including extracellular enzyme activities and microbial community structure (t-RFLP) in the marsh, both undisturbed and invaded areas over a year period.ResultsWe found higher extracellular enzyme activity in invaded areas compared to the undisturbed region dominated by the native species Scirpus planiculmis, and this response was profound during the growing season. Fungal and bacterial community structure, analyzed by terminal-restriction fragment length polymorphism, indicated that invasion by Phragmites had little effect on these communities. However, significantly higher microbial diversity was found in intermediately invaded areas in which Scirpus and Phragmites were co-dominant.ConclusionsThis result suggests that microbial diversity was affected by plant diversity, rather than invasion by or presence of a particular species. Our results suggest that physicochemical conditions related to dominant plant species alter microbial activity, while plant diversity is a more important regulator of microbial community structure and diversity.