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Article

Correlations Between Spatiotemporal Variations in Phytoplankton Community Structure and Physicochemical Parameters in the Seungchon and Juksan Weirs

by
Hyeonsu Chung
,
Misun Son
,
Taesung Kim
*,
Jonghwan Park
and
Won-Seok Lee
Yeongsan River Environment Research Center, National Institute of Environmental Research, Gwangju 61011, Republic of Korea
*
Author to whom correspondence should be addressed.
Water 2024, 16(20), 2976; https://doi.org/10.3390/w16202976
Submission received: 27 August 2024 / Revised: 10 October 2024 / Accepted: 17 October 2024 / Published: 18 October 2024
(This article belongs to the Special Issue Aquatic Ecosystems: Biodiversity and Conservation)
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Abstract

:
The Yeongsan River is one of the four major rivers in South Korea. Since the construction of two weirs as part of the Four Major Rivers Project to secure water resources in 2011, issues with algal blooms have frequently arisen, prompting the Ministry of Environment of Korea to conduct continuous monitoring of water quality and algal outbreaks. This study, conducted between 2019 and 2023, examined the relationship between the phytoplankton community structure and physicochemical factors at the Seungchon and Juksan weirs. Phytoplankton were categorized into four groups (Bacillariophyceae, Chlorophyceae, Cyanophyceae, and other phytoplankton), and 20 dominant genera were selected for analysis. As microalgal species vary depending on environmental conditions, understanding the specific relationships among the microalgae observed in the study area can help explain their occurrence mechanisms and contribute to the development of effective management strategies. Therefore, we used principal component analysis (PCA) to analyze the seasonal variation patterns of the four microalgal groups and visualize key data features through dimensionality reduction. Additionally, PCA was employed to identify and visualize environmental factors related to seasonal variations in phytoplankton communities. PCA helped elucidate how different environmental factors influence phytoplankton fluctuations across seasons. We used canonical correspondence analysis (CCA) to investigate the relationships among the 20 dominant genera in each group and environmental factors. Additionally, CCA was used to analyze the relationship between the distribution of the top five dominant phytoplankton taxa in each group and various environmental factors. CCA allowed for a detailed examination of how these dominant taxa interact with environmental conditions. PCA revealed significant correlations between other phytoplankton and Chl-a in spring and Cyanophyceae and water temperature in summer. Bacillariophyceae was positively correlated with nitrogen-based nutrients but negatively with phosphate phosphorus (PO4-P). CCA revealed significant correlations between dominant genera and environmental factors. Stephanodiscus sp. was associated with nitrogen-based nutrients, whereas Microcystis sp. and Dolichospermum sp. were associated with water temperature and PO4-P. Stephanodiscus sp. affected water treatment through filtration and sedimentation issues, whereas Microcystis sp. and Dolichospermum sp. produced the toxin microcystin. These findings offer valuable insights for water quality management.

1. Introduction

Freshwater ecosystems provide water resources, purify pollutants, and serve as habitats for diverse biological communities [1]. However, because of agricultural activities and urbanization, several natural rivers have been transformed into artificial environments designed to serve various purposes such as preventing floods, ensuring adequate water supply, and providing a venue for aquatic recreational activities [2]. The construction of weirs, which obstruct natural river flow, extend water retention times, and transform lotic ecosystems into lentic ones, is an anthropogenic factor that significantly affects river ecosystems [3,4] because these artificial structures hinder pollutant dispersion, leading to the accumulation of soluble materials and a reduction in the self-purification capacity of rivers. Consequently, eutrophication and excessive phytoplankton growth ensue, severely affecting the aquatic environment and biota [5].
The Yeongsan River, the study area of this research, flows through southwestern South Korea and is among the four major rivers in the country, along with the Han, Geum, and Nakdong Rivers. With an area of 3371 km2 and an annual average water availability of 3 billion m3, which constitutes only 3.9% of the national total, the Yeongsan River basin is the smallest among South Korea’s four major rivers. This relatively small watershed size renders the river vulnerable to water quality fluctuations, even from minor pollutant inputs, highlighting its sensitivity to pollution [6]. The Yeongsan River has a higher level of water pollution than the other watersheds, with frequent occurrences of water quality issues such as eutrophication and low dissolved oxygen levels [7]. Furthermore, the watershed area and runoff are small, and the channel coefficient is high because of the topographical characteristics. Moreover, the Yeongsan River is the only major river that relies solely on agricultural reservoirs for maintaining river flow, making it vulnerable to flow rate instability [8]. In the context of the Four Major Rivers Restoration Project, which was implemented in 2010–2011 to prevent disasters, ensure adequate water supply, and improve water quality, two weirs—Seungchon and Juksan—were constructed on the main channel of the Yeongsan River [9]. Consequently, water retention times have increased and flow rates have decreased because the river’s dynamics have shifted to those of a lentic water body [10]. Additionally, the upstream region of the Yeongsan River, which includes the densely populated Gwangju Metropolitan City (with approximately 1.4 million residents) and its industrial complexes, has encountered challenges such as eutrophication, excessive phytoplankton proliferation, foul odor, and fish kills due to pollution discharge [11,12].
As primary producers, phytoplankton are crucial food web components in aquatic ecosystems [13]. Phytoplankton primary production serves as an indicator for evaluating ecosystem trophic status as it responds sensitively to changes in species composition and cell counts due to environmental shifts [14]. Moreover, phytoplankton communities are influenced by a range of complex physicochemical factors, including watershed characteristics, water temperature, and nutrient levels [15]. Long-term analysis of phytoplankton community structures and corresponding physicochemical factors provides essential data for assessing and monitoring the impacts of external influences on aquatic ecosystems [16,17]. Consequently, understanding the spatiotemporal distribution of phytoplankton and its relationships with environmental factors is crucial for evaluating the current state of aquatic ecosystems [18]. Despite this critical role of phytoplankton communities, most related research has been concentrated in the Yeongsan River estuary [19,20,21], leaving a gap in the knowledge on the relationships between the spatiotemporal dynamics of the phytoplankton community structure and environmental factors in the river’s weir sections, which are heavily influenced by urbanization. In particular, studies that correlate dominant species—key shapers of community structure—with environmental factors are notably scarce. We expected the findings of this study to contribute to identifying the key environmental factors influencing phytoplankton blooms at weirs in temperate regions, including South Korea, providing baseline data for effective aquatic ecosystem management.

2. Materials and Methods

2.1. Study Area

The Yeongsan River originates from Yongchubong Peak (elevation: 560 m) in Yong-myeon, Damyang-gun, Jeollanam-do, South Korea, and stretches 136 km to its estuary. Its basin hosts several major tributaries, including the Hwangryong River and the Jiseok, Gomakwon, Hampyeong, and Gwangju Streams [22]. Between 2010 and 2011, as part of South Korea’s Four Major Rivers Restoration Project, two weirs were constructed in the middle and lower reaches of the river (Figure 1). The Seungchon Weir (SC) is situated in Seungchon-dong, Nam-gu, Gwangju Metropolitan City; it has a drainage area of 1327 km2 and a reservoir capacity of 9,000,000 m3 [23]. Downstream, the Juksan Weir (JS) is located in Juksan-ri, Dasi-myeon, Naju-si, Jeollanam-do; it has a drainage area of 2359 km2 and a reservoir capacity of 25,700,000 m3 (Table 1) [24]. Both weirs supply agricultural water.

2.2. Physicochemical Parameter Analysis

Surface water samples were collected by boat weekly between 2019 and 2023 at SC (n = 249) and JS (n = 250) to analyze the phytoplankton community structure and assess the water quality of the part of the Yeongsan River between the two weirs. Samples were collected from the surface to a depth of 0.5 m. In situ measurements of water temperature, pH, dissolved oxygen (DO), and electrical conductivity (EC) were conducted using a calibrated multiparameter instrument (EXO2 Sonde; YSI, Yellow Springs, OH, USA). The water samples were immediately cooled and transported to the laboratory in an ice box. The following analytical tests were conducted in accordance with standard water quality assessment methods [25]: biochemical oxygen demand (BOD), chemical oxygen demand (COD), suspended solids (SS), total nitrogen (TN), nitrate nitrogen (NO3-N), ammonium nitrogen (NH3-N), total phosphorus (TP), phosphate phosphorus (PO4-P), chlorophyll-a (Chl-a), and total organic carbon (TOC). BOD, COD, and TOC were determined using the diaphragm electrode method, the potassium permanganate method, and high-temperature combustion, respectively. TN, TP, and PO4-P were analyzed using a microflow chemistry analyzer (QuAAtro 39 Autoanalyzer; BLTEC, Osaka, Japan). Ion chromatography was used for NO3-N (Integrion; Dionex, Sunnyvale, CA, USA) and NH3-N (AA3; BLTEC, Osaka, Japan) measurements. Chl-a levels were measured using a UV–Vis spectrophotometer (LAMBDA™ 25; Perkin-Elmer, Shelton, CT, USA).

2.3. Phytoplankton Analysis

For phytoplankton identification and enumeration, the collected samples were preserved by adding Lugol’s solution to achieve a final concentration of 2–3%. The samples were examined under an optical microscope at magnifications ranging from 100× to 1000×. Cell counts per milliliter were determined using a Sedgwick-Rafter chamber (Wildco, MI, USA), following standard operating procedures [26]. Based on previously established methodologies [27,28] and the classification system developed by Simonsen [29], phytoplankton were taxonomically identified and classified into four groups: Bacillariophyceae (diatoms), Chlorophyceae (green algae), Cyanophyceae (cyanobacteria), and other phytoplankton. Other phytoplankton were not classified into groups such as Dinophyceae, Chrysophyceae, and Cryptophyceae because over the 5-year study period, only 11 genera were observed at the sampling sites. Total cell counts for the remaining genera, excluding the top 5 with the highest cell counts, were less than 700 cells/mL, which was considered too low for detailed classification.

2.4. Statistical Analysis

PCA was performed using the R software (version 4.3.2) to identify the key environmental factors influencing seasonal phytoplankton dynamics. The number of principal components was determined based on eigenvalues greater than 1.0, which indicate the variance explained by each principal component [30]. Before conducting PCA, the Kaiser–Meyer–Olkin (KMO) test was used to assess the sampling adequacy of the variables. The KMO test measures the degree of interrelationships among variables, with values closer to 1 indicating greater adequacy for factor analysis; a minimum value of 0.5 is required to proceed with the analysis. The KMO values for SC and JS were 0.74 and 0.77, respectively, indicating that the sampled data were suitable for factor analysis in both cases. To determine correlations between the dominant phytoplankton groups and environmental factors, CCA was conducted using the PAST software (version 4.03). In the CCA biplot, arrows indicate the relative correlations of environmental variables as a measure of the relationship between the axes and environmental variables, and arrow length represents the relative explanatory power of the variable [31]. Additionally, five key genera were selected from each of the above four taxonomic groups and subjected to further analysis.

3. Results

3.1. Environmental Characteristics

Table 2 shows the values of the environmental factors analyzed by season at SC and JS during the study period. Among the most critical variables influencing the growth and succession of phytoplankton communities, the mean water temperature at SC was 17.7 °C (range: 5.2–30.9 °C), whereas that at JS was 17.1 °C (3.3–31.4 °C). The mean DO concentrations at SC and JS were 9.4 mg/L (range: 1.5–16.5 mg/L) and 10.0 mg/L (4.1–18.0 mg/L), respectively, suggesting that DO concentrations are inversely correlated with water temperature [32]. The pH was neutral, ranging from 7.3 to 7.7.
Nutrient fluctuations generally significantly affect phytoplankton communities [33]. Nitrogen-based nutrient (TN, NO3-N, and NH3-N) concentrations exhibited a consistent U-shaped pattern at both weirs; their values increased during winter and decreased during summer. In summer, the mean TN, NO3-N, and NH3-N concentrations in SC and JS decreased to 3.66, 1.44, and 1.42 mg/L, and 2.68, 1.36, and 0.57 mg/L, respectively. This pattern is presumably the result of nutrient dilution due to high rainfall in summer, which accounts for more than 40% of the annual rainfall [34]. The annual average rainfall in the study area is 1460.5 mm, and more than 61.6% of this total, amounting to 899.0 mm, is concentrated during the summer months (June to August). The annual mean TN, NO3-N, and NH3-N concentrations at SC and JS were 5.65, 2.12, and 2.59 mg/L, and 4.33, 2.16, and 1.28 mg/L, respectively, which were slightly higher than previously reported values [35,36].
Phosphorus-based nutrients (TP and PO4-P) did not exhibit significant seasonal patterns; the annual mean TP and PO4-P concentrations at SC and JS were 0.16 and 0.05 mg/L, and 0.12 and 0.03 mg/L, respectively.

3.2. Phytoplankton Community

The relative distribution of each taxonomic group indicates the succession patterns of phytoplankton throughout the study period (Figure 2). During winter, when the water temperature is low, Bacillariophyceae were predominant at both SC (58.8%) and JS (48.6%); however, their abundance gradually decreased from March to April, whereas that of Chlorophyceae and other phytoplankton increased (SC: Chlorophyceae 25.8%, other phytoplankton 16.6%; JS: Chlorophyceae 26.5%, other phytoplankton 37.8%). From May to August, when the water temperature rises, Chlorophyceae became dominant at both weirs (SC: 52.8%; JS: 42.7%). From August to September, Cyanophyceae reached their annual peak (SC: 11.3%; JS: 24.5%), followed by the gradual dominance of Bacillariophyceae as the water temperature gradually decreased, showing a typical succession pattern of phytoplankton in temperate aquatic systems [37,38]. Throughout the study period, Bacillariophyceae and Chlorophyceae were the most frequently occurring groups at both weirs (SC 88.6%; JS 75.9%), whereas Cyanophyceae and other phytoplankton appeared less frequently (SC: Cyanophyceae 3.3%, other phytoplankton 8.1%; JS: Cyanophyceae 9.6%, other phytoplankton 14.6%). Despite the relatively low annual mean abundance of Cyanophyceae (6.5%) at SC and JS in the Yeongsan River compared with that in other domestic water bodies [39,40], their presence is critically important in water quality management because of their potential toxicity, making the management of Cyanophyceae blooms a top environmental priority.
Cell counts for the dominant genera within each taxonomic group are presented in Figure 3 and Figure 4. To simplify the analysis, species-level data were aggregated at the genus level, and five key genera selected from each of the four phytoplankton groups (Bacillariophyceae, Chlorophyceae, Cyanophyceae, and other phytoplankton) were analyzed. These dominant genera collectively comprised a substantial portion of the phytoplankton community, accounting for an average of 93.5% of the total cell count at SC (Bacillariophyceae: 99.1%; Chlorophyceae: 81.2%; Cyanophyceae: 95.4%; other phytoplankton: 98.4%) and 98.8% at JS (Bacillariophyceae: 98.5%; Chlorophyceae: 98.7%; Cyanophyceae: 99.7%; other phytoplankton: 98.5%). Their high representation underscores the importance of these genera as reference taxa in characterizing the phytoplankton community structure in the Yeongsan River. The key genera in Bacillariophyceae included Stephanodiscus, Cyclotella, Aulacoseira, Skeletonema, and Synedra. The key genera in Chlorophyceae were Micratinium, Scenedesmus, and Actinastrum, whereas those in Cyanophyceae were Microcystis, Pseudanabaena, Merismopedia, Aphanizomenon, and Dolichospermum. Among other phytoplankton, Cryptomonas, Euglena, Mallomonas, and Peridinium were the dominant genera. The key genera in each taxonomic group were similar at both weirs.
Upon examining the dominance rate of the most frequently observed dominant genus among the top five dominant genera for each taxon, a comparison of the total cell counts between the predominant genus and the four subdominant genera in each taxonomic group yielded the following results at SC: Bacillariophyceae (Stephanodiscus sp.: 248,334 cells/mL vs. four subdominant genera: 222,860 cells/mL), Chlorophyceae (Micratinium sp.: 48,967 cells/mL vs. four subdominant genera: 84,860 cells/mL), Cyanophyceae (Microcystis sp.: 10,109 cells/mL vs. four subdominant genera: 6414 cells/mL), and other phytoplankton (Cryptomonas sp.: 24,574 cells/mL vs. four subdominant genera: 2651 cells/mL). The results for JS were as follows: Bacillariophyceae (Stephanodiscus sp.: 253,516 cells/mL vs. four subdominant genera: 160,813 cells/mL), Chlorophyceae (Micractinium sp.: 53,232 cells/mL vs. four subdominant genera: 89,108 cells/mL), Cyanophyceae (Microcystis sp.: 63,687 cells/mL vs. four subdominant genera: 20,750 cells/mL), and other phytoplankton (Cryptomonas sp.: 41,657 cells/mL vs. four subdominant genera: 2146 cells/mL). Except for Chlorophyceae, the predominant genus at both weirs consistently exhibited higher cell counts than the combined counts of the four subdominant genera. Notably, in the other phytoplankton group, Cryptomonas sp. was particularly dominant, with a prevalence of 92.5% (SC: 90%; JS: 95%). This high prevalence is attributed to the fact that Cryptomonas sp. frequently appears in spring when water temperatures are low, resulting in peak counts during this season at SC (9802 cells/mL of 27,225 cells/mL) and JS (22,948 cells/mL of 43,802 cells/mL) [41].

3.3. Multifunctional Analysis

To elucidate the relationships between the phytoplankton structure and environmental factors at SC and JS, PCA was conducted using the cell counts of each phytoplankton taxonomic group and the values of water temperature, pH, DO, BOD, COD, TOC, EC, Chl-a, and nutrients (TN, NO3-N, NH3-N, TP, and PO4-P) (Figure 5A,B). Four principal components with eigenvalues exceeding 1.0 were identified for both weirs. The first component accounted for 31.1% and 36.9% of the total variance at SC and JS, respectively, whereas the second to fourth components explained 17.9% and 18.8%, 13.7% and 11.7%, and 8.4% and 9.0% of the total variance at SC and JS, respectively. In spring at SC, the “other phytoplankton” group was positively correlated with the Chl-a concentration, suggesting that Cryptomonas sp. was predominant within this group (Figure 5A). In summer, Cyanophyceae abundance was positively correlated with the water temperature. During winter, similar environmental patterns influenced Bacillariophyceae at both weirs. Specifically, at SC, Bacillariophyceae abundance was positively correlated with DO, EC, and nitrogen-based nutrients (TN, NH3-N, and NO3-N) but negatively correlated with PO4-P. At JS, Bacillariophyceae abundance was positively correlated with nitrogen-based nutrients, pH, BOD, and Chl-a but negatively correlated with PO4-P (Figure 5B).
CCA was employed to identify the environmental factors affecting the distribution of the dominant genera within the study area, using the 14 environmental variables derived from PCA to evaluate total cell counts and relationships among the dominant genera, including five key genera from each taxonomic group, totaling twenty genera. Figure 6 presents the CCA biplot for the phytoplankton community. At SC, axes 1 and 2 explained 83.7% of the variance among the phytoplankton groups (Figure 6A). Axis 1, which accounted for 65.3% of the total variance, was positively correlated with water temperature for Merismopedia sp., Dolichospermum sp., Aphanizomenon sp., Pseudanabaena sp., Microcystis sp., Actinastrum sp., Pediastrum sp., Peridinium sp., and Aulacoseira sp. and with nitrogen-based nutrients, DO, and EC for Stephanodiscus sp., but it was negatively correlated with water temperature and PO4-P. Axis 2, which explained 18.3% of the variance, had a weak positive correlation with TP, BOD, COD, and TOC for Synedra sp. At JS, axes 1 and 2 accounted for 74.8% of the total variance (Figure 6B). Axis 1, which explained 58.8% of the total variance, revealed that Microcystis sp. and Dolichospermum sp. were positively correlated with water temperature, whereas Stephanodiscus sp. was positively correlated with nitrogen-based nutrients and EC. Axis 2, which explained 16.0% of the total variance, indicated that Cyclotella sp. was positively correlated with SS.

4. Discussion

4.1. Environmental Trends

The Seungchon and Juksan Weirs were established to secure agricultural water resources. However, the water quality of the Yeongsan River is poor because of pollutant discharge from agricultural lands and industrial complexes [42]. At SC, the water temperature in 2023 increased by 0.2 °C compared to the average water temperature over the previous four years, whereas the TN concentration rose by 0.100 mg/L, and the TP concentration decreased by 0.017 mg/L. At JS, the water temperature increased by 0.1 °C compared to the average over the previous four years, and the TN concentration increased by 0.129 mg/L, whereas the TP concentration decreased by 0.008 mg/L. Thus, at both weirs on the Yeongsan River, the water temperature and TN concentration showed an increasing trend, whereas the TP concentration showed a decreasing trend. Because water temperatures are rising because of global warming and climate issues, and directly controlling them is challenging, managing nutrient levels is becoming increasingly important [43]. To this end, it is essential to promote the establishment and expansion of wastewater treatment facilities and public livestock manure treatment centers. Additionally, continuous efforts in water quality improvement, such as ecological river restoration, non-point-source pollution reduction, and river mouth cleanups, are necessary.

4.2. Algal Bloom Dynamics

The Yeongsan River is experiencing high levels of non-point-source pollution from agricultural lands (79.4% based on BOD, 52 t/day), resulting in conditions favorable for algal growth due to eutrophication. Consequently, blue-green algae blooms occur every summer [44]. Additionally, the two weirs reduce water flow, further increasing algal blooms. Although Cyanophyceae were not dominant in the study area, they can produce toxins that affect humans and higher organisms and are therefore a key monitoring target. Toxin-producing Cyanophyceae growths are referred to as “harmful algal blooms”, and four genera (Microcystis sp., Aphanizomenon sp., Anabaena sp., and Oscillatoria sp.) have been designated as management targets. The occurrence of harmful algae has been increasing at both weirs each year, which is exacerbated by global warming, and has led to prolonged late heat, allowing blooms to appear until November, after which water temperatures drop rapidly [44]. To manage algal blooms, research on the characteristics and mechanisms of phytoplankton growth is necessary [45]. Previous studies have mainly focused on the correlations between nitrogen and phosphorus and algal blooms [46,47]; however, given the reduced diversity of phytoplankton in artificial environments, future research should prioritize identifying the primary dominant genera within these environments and assessing their succession patterns and distribution characteristics. This approach is vital for predicting algal blooms and effectively managing water resources. The focus of this study on selecting five dominant genera from each taxonomic group for environmental factor and time-series analyses represents a significant methodological contribution, particularly in understanding the impact of human-induced changes in hydrology on phytoplankton communities.

4.3. Statistical Insights

The occurrence of algal blooms involves a complex mechanism that is influenced by various environmental factors and the characteristics of phytoplankton species. Therefore, using statistical techniques to preemptively assess correlations would be beneficial. PCA was used to identify the key environmental factors affecting the composition and distribution of phytoplankton communities and to analyze the correlations between them. This analysis allows for a systematic understanding of how seasonal environmental changes impact phytoplankton and provides essential information for evaluating and managing ecosystem health. PCA revealed factors that significant influence phytoplankton. We found a positive correlation between other phytoplankton and the Chl-a concentration, suggesting a predominance of Cryptomonas sp. in this group as Cryptomonas sp. reportedly has higher per-cell Chl-a concentrations than other algal genera [48]. In summer, Cyanophyceae abundance was positively correlated with the water temperature, aligning with findings that rising temperatures contribute to the proliferation of Cyanophyceae [49,50]. Cyanobacteria can adjust their buoyancy using intracellular gas vesicles, allowing them to remain in the surface layer where they receive adequate light and optimal temperatures for growth [51]. During winter, the environmental factors affecting Bacillariophyceae exhibited similar patterns at both weirs. At SC, Bacillariophyceae abundance was positively correlated with DO, EC, and nitrogen-based nutrients (TN, NH3-N, and NO3-N) but negatively correlated with PO4-P. At JS, Bacillariophyceae abundance showed a positive correlation with nitrogen-based nutrients, pH, BOD, and Chl-a but a negative correlation with PO4-P. These findings emphasize the importance of nitrogen-based nutrients in structuring Bacillariophyceae communities. The sensitivity of phytoplankton community composition to environmental changes underscores the potential for using these organisms as indicators of ecosystem health, especially for identifying the causes of algal blooms.
CCA was used to identify the relationships between the distribution of phytoplankton and environmental variables to assess the influences of specific environmental factors on phytoplankton community structure. As we selected the dominant genera at the two weirs, this analysis allowed for a nuanced understanding of how these genera respond to environmental changes, providing essential information for water resource management and ecosystem health evaluation. Furthermore, this analysis contributes to understanding the interactions between water quality changes and the ecosystem and can aid in predicting ecosystem variability due to climate change. The results revealed significant environmental influences on the dominant genera in Bacillariophyceae and Cyanophyceae. Among Bacillariophyceae, Cyclotella sp. exhibited relatively low correlations with environmental variables, whereas Aulacoseira sp. was positively correlated with the water temperature. This result is consistent with findings by Parakkandi et al. [52] and Roshith et al. [53], who found that Aulacoseira sp., which is capable of cold adaptation, can also thrive during summer, when Cyanophyceae are prevalent. Stephanodiscus sp. was strongly positively correlated with nitrogen-based nutrients but negatively correlated with the water temperature and PO4-P concentration, consistent with previous findings indicating its dominance in cooler, nutrient-rich conditions [54,55]. Therefore, nitrogen-based nutrients and low water temperatures promote the growth of Stephanodiscus sp. Among Cyanophyceae, Microcystis sp. and Dolichospermum sp. were positively correlated with the water temperature and PO4-P concentration. These genera, along with Cylindrospermopsis, are known for contributing to harmful cyanobacterial blooms globally [56]. Several studies have linked blooms of Microcystis sp. and Dolichospermum sp. to fluctuations in water temperature and nutrient levels [57,58]. The massive proliferation of Stephanodiscus sp., Microcystis sp., and Dolichospermum sp. negatively affects water resource management; an excessive growth of Stephanodiscus sp. can lead to decreased DO concentrations and problems with filtration and sedimentation in water treatment and supply infrastructure [59]. Meanwhile, Microcystis sp. and Dolichospermum sp. produce the toxin microcystin, which can induce mitochondrial dysfunction, hepatotoxicity, and nephrotoxicity [60]. Our study focused on the relationship between phytoplankton and environmental factors in areas that have transitioned from a flowing river to a still water environment. The results of the canonical correspondence analysis (CCA) reveal a strong link between harmful cyanobacteria, water temperature, and residence time. These findings align with existing research highlighting the impact of agricultural activities on phytoplankton composition in river ecosystems. For instance, studies conducted in Lake Erie, USA, indicate that agricultural non-point-source pollution significantly affects harmful algal blooms and phytoplankton communities [61]. Similarly, research in China emphasizes that agricultural activities contribute to nutrient influx in rivers, further altering phytoplankton dynamics [62]. Collectively, these studies underscore the extensive influence of agricultural practices on riverine ecosystems and highlight the need for ongoing management and water quality improvement efforts. By continuing to monitor these harmful algal blooms, we aim to develop strategies to mitigate their occurrence and maintain the health of aquatic ecosystems.

5. Conclusions

We investigated the relationships between the phytoplankton community structure and environmental factors at two weirs (SC and JS) in the Yeongsan River basin over 5 years (2019–2023). We classified phytoplankton into four taxonomic groups (Bacillariophyceae, Chlorophyceae, Cyanophyceae, and other phytoplankton), enabling a systematic analysis of their community structure and succession patterns. PCA was conducted to identify seasonal influences of environmental factors on each taxonomic group. CCA was performed on twenty dominant genera (five from each group) to determine the environmental factors affecting the genera commonly found at both SC and JS. The results of this study are summarized as follows.
The key genera in Bacillariophyceae included Stephanodiscus, Cyclotella, Aulacoseira, Skeletonema, and Synedra. The key genera in Chlorophyceae were Micratinium, Scenedesmus, and Actinastrum, and those in Cyanophyceae were Microcystis, Pseudanabaena, Merismopedia, Aphanizomenon, and Dolichospermum. Among other phytoplankton, Cryptomonas, Euglena, Mallomonas, and Peridinium were the dominant genera. The key genera of each taxonomic group were similar at both weirs.
During the study period, similar phytoplankton succession patterns were observed at both weirs. Bacillariophyceae dominated during winter. The abundance of Chlorophyceae and other phytoplankton gradually increased from March to April. Chlorophyceae became dominant from May to August, as water temperatures increased, followed by an increase in the abundance of Cyanophyceae from August to September, exhibiting a typical succession pattern of phytoplankton in temperate aquatic systems.
PCA revealed three significant correlations. (1) In spring, “other phytoplankton” were positively correlated with the Chl-a concentration, probably because of the predominance of Cryptomonas sp., which have higher per-cell Chl-a concentrations than other phytoplankton genera. (2) In summer, Cyanophyceae were strongly positively correlated with the water temperature. This finding was attributed to the ability of Cyanophyceae to buoy to the water surface layer using gas vesicles to capitalize on sufficient sunlight and warmer temperatures conducive to their growth. (3) In winter, Bacillariophyceae were positively correlated with nitrogen-based nutrients (TN, NH3-N, and NO3-N) but negatively correlated with PO4-P, suggesting that nitrogen-based nutrients are more impactful than PO4-P on shaping the community structure of Bacillariophycea.
CCA revealed that Stephanodiscus sp., which was the dominant genus in Bacillariophyceae, was positively correlated with nitrogen-based nutrients, indicating that these nutrients serve as a nutrient source for Stephanodiscus sp. Microcystis sp. and Dolichospermum sp., which were the dominant genera in Cyanophyceae, were positively correlated with the water temperature and PO4-P concentration, indicating that increased water temperatures and PO4-P levels were the primary drivers of Microcystis sp. and Dolichospermum sp. growth during the study period and corroborating the high sensitivity of these species to these environmental factors. Given that Stephanodiscus sp. can disrupt water treatment processes by causing filtration and sedimentation problems and that Microcystis sp. and Dolichospermum sp. produce microcystin, identifying the environmental factors influencing these dominant genera is crucial for effective algal bloom control and water resource management.

Author Contributions

Conceptualization, H.C.; methodology, H.C. and M.S.; validation, H.C.; formal analysis, H.C. and M.S.; investigation, H.C., M.S., T.K., J.P. and W.-S.L.; data curation, H.C.; writing—original draft, H.C. and T.K.; writing—review and editing, H.C., T.K., J.P. and W.-S.L.; visualization, H.C. and T.K.; supervision, W.-S.L.; project administration, T.K. and W.-S.L. funding acquisition, W.-S.L. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by a grant from the National Institute of Environmental Research, funded by the Ministry of Environment (ME) of the Republic of Korea (NIER-2024-01-01-075).

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare no conflicts of interest.

References

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Water 16 02976 g001
Figure 1. Sampling sites at the Seungchon Weir (SC) and Juksan Weir (JS) in the Yeongsan River.
Figure 1. Sampling sites at the Seungchon Weir (SC) and Juksan Weir (JS) in the Yeongsan River.
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Water 16 02976 g002
Figure 2. Relative abundance and cell count for the four taxonomic groups at (A) SC and (B) JS in the Yeongsan River.
Figure 2. Relative abundance and cell count for the four taxonomic groups at (A) SC and (B) JS in the Yeongsan River.
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Water 16 02976 g003
Figure 3. Cell counts of dominant genera within (A) Bacillariophyceae, (B) Chlorophyceae, (C) Cyanophyceae, and (D) other phytoplankton at SC. SP, spring; SU, summer; AU, autumn; WI, winter.
Figure 3. Cell counts of dominant genera within (A) Bacillariophyceae, (B) Chlorophyceae, (C) Cyanophyceae, and (D) other phytoplankton at SC. SP, spring; SU, summer; AU, autumn; WI, winter.
Water 16 02976 g003
Water 16 02976 g004
Figure 4. Cell counts of dominant genera within (A) Bacillariophyceae, (B) Chlorophyceae, (C) Cyanophyceae, and (D) other phytoplankton at JS. SP, spring; SU, summer; AU, autumn; WI, winter.
Figure 4. Cell counts of dominant genera within (A) Bacillariophyceae, (B) Chlorophyceae, (C) Cyanophyceae, and (D) other phytoplankton at JS. SP, spring; SU, summer; AU, autumn; WI, winter.
Water 16 02976 g004
Water 16 02976 g005aWater 16 02976 g005b
Figure 5. Biplots of PCA results grouped by season at (A) SC and (B) JS in the Yeongsan River. PCA, principal components analysis.
Figure 5. Biplots of PCA results grouped by season at (A) SC and (B) JS in the Yeongsan River. PCA, principal components analysis.
Water 16 02976 g005aWater 16 02976 g005b
Water 16 02976 g006
Figure 6. Biplots of CCA results at (A) SC and (B) JS in the Yeongsan River. CCA, canonical correspondence analysis.
Figure 6. Biplots of CCA results at (A) SC and (B) JS in the Yeongsan River. CCA, canonical correspondence analysis.
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Table 1. Physical parameters of the study sites.
Table 1. Physical parameters of the study sites.
SiteElevation (m)Capacity
(106 m3)		Drainage
Area
(km2)		Average
Depth (m)		Geographic Coordinates (Latitude/Longitude)
SC7.59.01327.07.535°04′13.90″ N/126°46′34.09″ E
JS3.525.72359.07.134.976675° N/126.632778° E
Note(s): SC, Seungchon Weir; JS, Juksan Weir.
Table 2. Physicochemical parameters at SC and JS during different seasons.
Table 2. Physicochemical parameters at SC and JS during different seasons.
SiteSeasonWT
(°C)		pHDO
(mg/L)		TN
(mg/L)		NO3-N
(mg/L)		NH3-N
(mg/L)		TP
(mg/L)		PO4-P (mg/L)
SCSpring16.5
(10.2–26.8)		7.4
(6.6–9.0)		9.5
(1.6–16.3)		6.54
(2.74–12.95)		2.03
(0.75–3.18)		3.43
(0.74–9.23)		0.19
(0.09–0.58)		0.04
(0.01–0.27)
Summer26.4
(20.6–30.9)		7.2
(6.2–8.7)		6.7
(1.5–12.4)		3.66
(2.01–6.42)		1.44
(0.76–2.41)		1.42
(0.21–4.24)		0.17
(0.08–0.38)		0.06
(0.00–0.23)
Autumn19.7
(10.7–28.3)		7.2
(6.2–8.9)		9.1
(4.0–16.5)		4.41
(2.22–6.99)		2.11
(0.76–3.35)		1.47
(0.17–3.49)		0.13
(0.05–0.28)		0.04
(0.00–0.20)
Winter7.8
(5.2–12.7)		7.3
(6.7–8.2)		12.4
(8.9–15.6)		8.14
(3.63–11.81)		2.96
(2.04–3.96)		4.12
(0.70–7.13)		0.16
(0.06–0.31)		0.03
(0.00–0.16)
Total17.7
(5.2–30.9)		7.3
(6.2–9.0)		9.4
(1.5–16.5)		5.65
(2.01–12.95)		2.12
(0.75–3.96)		2.59
(0.17–9.23)		0.16
(0.05–0.58)		0.05
(0.00–0.27)
JSSpring15.8
(8.0–23.9)		7.7
(6.9–9.0)		9.1
(4.4–15.5)		5.08
(2.77–9.22)		2.08
(0.83–3.22)		1.99
(0.08–5.23)		0.13
(0.06–0.24)		0.03
(0.00–0.13)
Summer26.3
(20.9–31.4)		7.4
(6.6–8.9)		7.1
(4.1–11.4)		2.68
(1.51–4.03)		1.36
(0.83–2.03)		0.57
(0.04–1.73)		0.13
(0.07–0.23)		0.05
(0.00–0.13)
Autumn19.5
(9.6–28.2)		7.7
(6.5–9.2)		9.8
(6.2–14.2)		3.31
(1.64–5.57)		2.02
(1.04–2.01)		0.48
(0.02–2.01)		0.11
(0.04–0.26)		0.04
(0.00–0.14)
Winter6.3
(3.3–9.9)		8.1
(7.0–9.2)		14.2
(9.4–18.0)		6.41
(3.21–9.19)		3.26
(2.14–4.75)		2.15
(0.49–4.29)		0.12
(0.04–0.24)		0.02
(0.00–0.19)
Total17.1
(3.3–31.4)		7.7
(6.5–9.2)		10.0
(4.1–18.0)		4.33
(1.51–9.22)		2.16
(0.83–4.75)		1.28
(0.02–5.23)		0.12
(0.04–0.26)		0.03
(0.00–0.19)
Note(s): Data are means (range) over 5 years. WT, water temperature; DO, dissolved oxygen; TN, total nitrogen; NO3-N, nitrate nitrogen; NH3-N, ammonium nitrogen; TP, total phosphorus; PO4-P, phosphate phosphorus.
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Chung, H.; Son, M.; Kim, T.; Park, J.; Lee, W.-S. Correlations Between Spatiotemporal Variations in Phytoplankton Community Structure and Physicochemical Parameters in the Seungchon and Juksan Weirs. Water 2024, 16, 2976. https://doi.org/10.3390/w16202976

AMA Style

Chung H, Son M, Kim T, Park J, Lee W-S. Correlations Between Spatiotemporal Variations in Phytoplankton Community Structure and Physicochemical Parameters in the Seungchon and Juksan Weirs. Water. 2024; 16(20):2976. https://doi.org/10.3390/w16202976

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Chung, Hyeonsu, Misun Son, Taesung Kim, Jonghwan Park, and Won-Seok Lee. 2024. "Correlations Between Spatiotemporal Variations in Phytoplankton Community Structure and Physicochemical Parameters in the Seungchon and Juksan Weirs" Water 16, no. 20: 2976. https://doi.org/10.3390/w16202976

APA Style

Chung, H., Son, M., Kim, T., Park, J., & Lee, W. -S. (2024). Correlations Between Spatiotemporal Variations in Phytoplankton Community Structure and Physicochemical Parameters in the Seungchon and Juksan Weirs. Water, 16(20), 2976. https://doi.org/10.3390/w16202976

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