• Korean Journal of Ecology and Environment
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• v.54 no.4
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• pp.303-314
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• 2021

To understand how to efficiently observe the biomass and community of phytoplankton, phytoplankton sampling was carried out from June to October 2019 at the Yeongju dam sediment control reservoir(YJ) and Bohyeonsan dam reservoir(BH1 and BH2). The results derived from microscopic observation, such as the conventional phytoplankton qualitative/quantitative analysis, and from the CHEMTAX method based on the pigments, were compared. The relative contribution of phytoplankton, calculated by the microscopy and CHEMTAX methods, showed a significant difference in all four classes: cryptophyta, chlorophyta, cyanobacteria, and diatoms. In addition, the correlation between the two observation methods was poor. This might be caused by methodological differences in microscopy that do not consider the varying cell sizes among phytoplankton species. In this study, by converting the cells into carbon, the slope between both carbon biomasses based on microscopy and CHEMTAX was improved close to the 1 : 1 line, and the y-intercept was closer to 0 for cryptophyta and diatoms. For cyanobacteria, the slope increased, the y-intercept decreased, and the plot approached 1 : 1 although the correlation coefficients were not improved in all classes. The present study suggests that application of CHEMTAX based on pigment analysis could be a possible approach to efficiently determine the relative carbon proportions of individual classes of phytoplankton community composition.

• Korean Journal of Environmental Biology
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• v.40 no.1
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• pp.112-123
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• 2022

To assess the influence of environmental factors on the phytoplankton community structure and total phytoplankton biomass during four seasons in 2014, we investigated the abiotic and biotic factors at 25 stations in the Busan coastal region. The phytoplankton community and total phytoplankton biomass were strongly dependent on the discharge from the Nakdong River, and the high density of phytoplankton was related with the introduction of the Tsushima Warm Current (TWC), particularly in the thermohaline fronts of the fall season. The relationship between the salinity and nutrient (Dissolved inorganic nitrogen=DIN: R²=0.72, p<0.001 and Dissolved inorganic silicon=DSi: R²=0.78, p<0.001) highly correlated with the river discharge, implying that those nutrients have played a crucial role in the growth of diatom and cryptophyta. The total phytoplankton biomass was highest in the summer followed by autumn, spring, and winter. Diatom and cryptophyta species were dominant species during the four seasons. Additionally, there were strong positive correlations between Chlorophyll a and total phytoplankton biomass (R²=0.84, p<0.001), cryptophyta (R²=0.76, p<0.001) and diatom (R²=0.50, p<0.001), respectively. In particular, we found that there were significant differences in the nutrients, phytoplankton community compositions, and total phytoplankton biomass between the inner and the outer coastal region of Busan, depending on the amount of river discharge from the Nakdong River, particularly during rainy seasons. Therefore, the seasonal change of TWC and river discharge from the Nakdong River serve an important role in determining phytoplankton population dynamics in the Busan coastal region.

• Korean Journal of Ecology and Environment
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• v.55 no.1
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• pp.60-75
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• 2022

The Bayesian algorithm model is a model algorithm that calculates probabilities based on input data and is mainly used for complex disasters, water quality management, the ecological structure between living things or living-non-living factors. In this study, we analyzed the main factors affected Korean Estuary Trophic Diatom Index (KETDI) change based on the Bayesian network analysis using the diatom community and physicochemical factors in the domestic estuarine aquatic ecosystem. For Bayesian analysis, estuarine diatom habitat data and estuarine aquatic diatom health (2008~2019) data were used. Data were classified into habitat, physical, chemical, and biological factors. Each data was input to the Bayesian network model (GeNIE model) and performed estuary aquatic network analysis along with the nationwide and each coast. From 2008 to 2019, a total of 625 taxa of diatoms were identified, consisting of 2 orders, 5 suborders, 18 families, 141 genera, 595 species, 29 varieties, and 1 species. Nitzschia inconspicua had the highest cumulative cell density, followed by Nitzschia palea, Pseudostaurosira elliptica and Achnanthidium minutissimum. As a result of analyzing the ecological network of diatom health assessment in the estuary ecosystem using the Bayesian network model, the biological factor was the most sensitive factor influencing the health assessment score was. In contrast, the habitat and physicochemical factors had relatively low sensitivity. The most sensitive taxa of diatoms to the assessment of estuarine aquatic health were Nitzschia inconspicua, N. fonticola, Achnanthes convergens, and Pseudostaurosira elliptica. In addition, the ratio of industrial area and cattle shed near the habitat was sensitively linked to the health assessment. The major taxa sensitive to diatom health evaluation differed according to coast. Bayesian network analysis was useful to identify major variables including diatom taxa affecting aquatic health even in complex ecological structures such as estuary ecosystems. In addition, it is possible to identify the restoration target accurately when restoring the consequently damaged estuary aquatic ecosystem.