Phytoplankton community response to water physicochemical characteristics under seasonal variation at the Ubol Ratana Dam, Khon Kaen, Thailand

Phytoplankton community response to water physicochemical characteristics under seasonal variation at the Ubol Ratana Dam, Khon Kaen, Thailand | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Phytoplankton community response to water physicochemical characteristics under seasonal variation at the Ubol Ratana Dam, Khon Kaen, Thailand Anchana Somdee, Weeraput Butsat, Theerasak Somdee This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4488037/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Oct, 2025 Read the published version in Environmental Science and Pollution Research → Version 1 posted 6 You are reading this latest preprint version Abstract Water eutrophication is a significant environmental issue that impacts aquatic ecosystems worldwide. In this study, we aimed to investigate the relationships among the water physicochemical characteristics, phytoplankton community, and cyanotoxins in the Ubol Ratana Dam, Khon Kaen, Thailand, during the years 2022–2023 and to evaluate the relationships between changes in water parameters and the dynamics of the phytoplankton community under seasonal variations in the Ubol Ratana Dam. A total of 84 phytoplankton species belonging to 8 phyla were found in six different locations in the reservoir. Cyanobacteria (Cyanophyta) had the highest cell density (84.06%), with Microcystis aeruginosa being the dominant species. In terms of seasonality, the diversity indices, including Shannon‒Wiener and evenness values, were highest during summer and lowest during the rainy season, while species richness remained constant. Interestingly, the phytoplankton density was greater at the center of the dam and water outlet stations than that at the other stations, and these two areas also had the lowest nitrate and ammonium concentrations. Canonical correspondence analysis (CCA) explained 66.8%, 70.6%, and 63.8% of the total variation in the rainy, winter, and summer seasons, respectively. This indicates that nitrate and ammonium were factors that influenced phytoplankton growth. Correlation analysis revealed that the abundance of dominant cyanobacteria was positively correlated with temperature and orthophosphate content and negatively correlated with nitrate and ammonium content. Overall, nitrogen concentration mainly governed cyanobacterial blooms. These results suggest that relationships between physicochemical factors and the phytoplankton community significantly influence the seasonal variations in phytoplankton, providing valuable insights for future monitoring of phytoplankton blooms. Phytoplankton community Physicochemical factors Nutrients Phytoplankton blooms Cyanobacteria Seasonal dynamics Reservoir Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Water resources are vital for all living organisms, but only 0.3% of freshwater on Earth is usable (Kılıç, 2020 ). Freshwater is commonly stored in dams or reservoirs and serves various purposes in agriculture, industries, households, and flood regulation (Soomro et al. 2023). Freshwater dams show variations in physicochemical characteristics based on hydrological cycles and water flow characteristics, which impact the diversity of organisms in freshwater ecosystems, including phytoplankton (Graco-Roza et al. 2021 ). Furthermore, land-based pollution from industrial, agricultural, and urban areas is increasingly being discharged into freshwater reservoirs, resulting in decreased biodiversity in reservoirs (Wang et al. 2023 ). The proliferation of organisms is typically caused by natural events and human activities, including water contamination with nutrients from riverine inflow, leaching, rainfall, aquaculture, and household, industrial, and agricultural activities (Bassem et al. 2020; Ponnusamy et al. 2023 ; Sidabutar et al. 2020; Wang et al. 2022 ). These factors lead to changes in the main physicochemical characteristics of water bodies, resulting in plankton proliferation. Phytoplankton play a crucial role as the primary aquatic producers in freshwater ecosystems by providing essential nutrients and energy in these systems (He et al. 2022 ). Significant changes in physicochemical features have an impact on the phytoplankton community. Therefore, the local environment and water quality in freshwater ecosystems can lead to changes in phytoplankton communities and diversity (Yuan et al., 2018 ). The main physicochemical factors influencing dynamic phytoplankton communities are light, temperature, pH, and nutrient concentration (Akter et al. 2022 ; Crevecoeur et al. 2023; Sun et al. 2023 ; Yang et al. 2019 ). In addition, phytoplankton growth can increase with extended daylight periods, and this factor also interacts with temperature (Crevecoeur et al. 2023). Rising water temperatures affect the activity of phytoplankton (Sun et al. 2023 ; Zhang et al. 2021 ). Global warming causes irregular effects on the adaptation of phytoplankton, leading to harmful algal blooms (HABs) in tropical regions due to seasonal temperature fluctuations. Studies have shown that temperature can have both positive and negative effects on the proliferation of specific species. For example, high temperatures can increase the abundance of dominant species while reducing the abundance of others under the same conditions (Rasconi et al., 2017 ; Suresh Kumar and Thomas, 2019). Additionally, a more alkaline pH has been found to increase cyanobacterial photosynthesis and reproduction (Wei et al., 2022 ). Phytoplankton development depends on essential nutrients. The main nutrients that limit phytoplankton blooms are nitrogen and phosphorus, which exist in the form of ammonium nitrate, nitrite, and orthophosphate (Passos et al. 2022 ). Like other phytoplankton species, Cyanobacteria tend to dominate in environments with high nutrient contents, temperatures, and light intensities. Therefore, both short- and long-term environmental changes, including seasonal variations, strongly influence the spatial and temporal variability of phytoplankton distributions. Several studies have reported on these interactions (Crevecoeur et al. 2023; Parakkandi et al. 2021; Ponnusamy et al. 2023 ; Srichandan et al. 2015 ; SureshKumar and Thomas 2019 ; Yang et al. 2019 ). Cyanobacterial blooms, also known as HABs, release cyanotoxins into freshwater reservoirs, posing a risk to aquatic life and public health (Kong et al. 2019 ; Passos et al. 2022 ; Sun et al. 2023 ). These blooms are capable of generating cyanotoxins such as microcystin, saxitoxin, and cylindrospermopsin, which can have severe effects on organisms, including respiratory failure, hypovolemic shock, and intrahepatic hemorrhage (Barros et al. 2019; Christophoridis et al. 2018; Passos et al. 2022 ; Tito et al. 2022 ). The levels of some nutrients, such as total phosphorus, have been found to be positively correlated with the levels of cylindrospermopsin and microcystin (Barros et al. 2019). Therefore, understanding the link between physicochemical parameters and blooms is crucial for identifying key regulators that can help limit the distribution of HABs and toxins. The Ubol Ratana Dam is a large artificial freshwater reservoir in Khon Kaen Province, northeast Thailand. The dam was built for various purposes, including electricity generation, irrigation, flood relief, and fishery operation (Electricity Generating Authority of Thailand 2024a ; Sriworamas et al. 2020 ). However, prolonged droughts over several decades have led to long-term changes in the hydrological and meteorological conditions of the reservoir, exacerbated by an unpredictable climate. These changes impact water storage and may lead to sudden phytoplankton blooms (Muangsringam et al. 2019 ). There has been limited research on the physicochemical features, water management approaches, and phytoplankton communities in the Ubol Ratana Dam. In 2017–2018, Microcystis significantly proliferated, while in 2019, Cylindrospermopsis became the dominant species (Mengchouy and Meksumpun 2022 ; Muangsrigam et al. 2019). Variations in the phytoplankton community composition and diversity in the reservoir with changes in water physicochemical parameters have not been well documented. Therefore, a comprehensive survey is necessary to address this knowledge gap. The aims of this study were to i) investigate and identify the seasonal variations in the phytoplankton community at different sites, ii) study the density and diversity of phytoplankton, and iii) investigate the relationships between physicochemical variables and dominant phytoplankton assemblages under seasonal variation. We hypothesize that understanding the physicochemical characteristics of water bodies in each location and season, which are important variables, will help to limit the recruitment of phytoplankton communities and allow for the efficient surveillance and monitoring of eutrophication risks. Materials and methods Study site The Ubol Ratana Dam is located in the Ubol Ratana district of Khon Kaen Province, Thailand (see Fig. 1 a, b). It is a rock-fill dam with a clay core. The dam has a crest length of 885 m and a crest width of 6 m. The dam has a maximum volume capacity of approximately 2,431 million m 3 , a maximum depth of 32 m, and a catchment area of 12,104 km 2 (Electricity Generating Authority of Thailand 2024a ). It was constructed at the confluence of three main rivers: the Phong River, Phaniang River, and Choen River (Ingthamjitr et al. 2008 ). In this research, water samples were collected from 6 different locations within the dam reservoir: water inlet 1 (from the Choen River) (S1; coordinates 16.6376630, 102.5619740), water inlet 2 (from the Phong River and Phaniang River) near the Non-Sawang, Tha Lat, and Pong Sang Districts (S2; coordinates 16.7948950, 102.4485980), the center of the dam (S3; coordinates 16.6927110, 102.5919860), the water outlet (S4; coordinates 16.7719530, 102.6194940), a fish farming site (S5; coordinates 16.6613810, 102.5597750) near the Nong Kung Soen Community, and Non-Sang Municipality Community (S6; coordinates 16.8227750, 1 02.5981860) (Fig. 1 c). The Phong River area has high turbidity due to high loads of suspended inorganic solids. Additionally, gillnet fishing and agricultural activities are carried out in the watershed using reservoir waters (Ingthamjitr et al. 2008 ; Mengchouy and Meksumpun 2022 ). The reservoir is impacted by two monsoons: the northeast (NE) monsoon from November to February and the southwest (SW) monsoon from May to October. The average precipitation during the NE monsoon is 5–10 mm, but in August and September, it exceeds 200 mm. The monthly average air temperature during the NE monsoon is 22.5°C, while during the SW monsoon, it is 26.3–29.1°C. The surface water temperature ranges between 22 and 26°C in December and February and between 29 and 34°C from April to October (Ingthamjitr et al. 2008 ). According to a study conducted from March 2022 to January 2023, the water inflow volume increased from July to October, reaching its peak in mid-October. In terms of season, water storage varies between 1086.80 and 1495.11 million cubic meters (MCMs) during the summer (March to May), 862.86 and 3188.15 MCMs during the rainy season (June to October), and 1532.95 and 2855.43 MCMs during the winter (November to February 2023) (Supplementary information) (Electricity Generating Authority of Thailand 2024b ). Sampling methodology and water physicochemical parameters Water samples were collected at the reservoir surface (0.5-meter depth) in three replicates, and the average value was calculated for the number of phytoplankton, cyanobacteria, and physicochemical characteristics collected in three seasons at the study site in Thailand. Sampling was performed twice per season, including in summer, the rainy season, and winter, between March 2022 and January 2023, from 9:00 a.m. to 4:00 p.m. The months in which samples were collected were March and April, August and September, December and January, representing the summer, rainy, and winter seasons of Thailand, respectively. The water temperature (°C), pH, and electrical conductivity (EC) (µs.cm − 1 ) were measured using a multiparameter digital water quality meter (YSI ProDSS, USA). The water was analyzed to determine its ammonium-nitrogen and nitrate contents to infer the dissolved inorganic nitrogen content. The amount of ammonium-nitrogen was determined using the standard method for the examination of water and wastewater, APHA, 23rd ED., 2017, Part 4500- NH 3 , while the amount of nitrate-nitrogen was determined using the standard method for the examination of water and wastewater, APHA, AWWA, WEF, 23rd ED., 2017, Part 4500-NO 3 -B (APHA, 2017), with limits of detection of 0.02 and 0.02 mg L − 1 , respectively. In addition, total phosphorus and orthophosphate contents were determined using a DR 2700 Spectrophotometer (HACH, USA) with limits of detection of 0.02 mg L − 1 (Methods 4500-P B and 4500-P E) (APHA 2017). The water samples were frozen for toxin analyses. We used a microcystin-Adda ELISA kit and a cylindrospermopsin ELISA kit (from Abraxis, USA) to measure microcystin and cylindrospermopsin levels following the manufacturer's instructions. Optical density was measured at 450 nm, and toxin concentrations were determined based on a standard curve. The detection limits for microcystins and cylindrospermopsin were 0.10 and 0.040 µgL − 1 , respectively. Each analysis was performed in triplicate. Phytoplankton collection and taxonomic identification A water sample was collected from the surface water using a plankton net with a 30 µm mesh size. The sample was concentrated to a final volume of 50 ml from 100 liters of water. Three duplicate samples were taken from each location and preserved in polystyrene bottles with 2% Lugal's solution. The phytoplankton and cyanobacteria in the concentrated water were observed for classification at the genus and species levels using standard manuals, textbooks, and research articles. Observations were carried out under a light microscope (CX23 Olympus microscope), and species were counted using the Utermöhl method with an Olympus CK40 inverted phase microscope (Olympus, Tokyo, Japan) as per Edler and Elbrächter ( 2010 ). Statistical analysis The number of species for each phylum was determined and is displayed as a proportion of the total species richness. The phytoplankton species collected from each sampling location were input into the Jvenn website ( https://jvenn.toulouse.inrae.fr ) for Venn diagram analysis. Diversity indices, including the Shannon‒Wiener index, Margalef's richness index, and evenness index, were determined for each season using the Past 4.03 program. Violin and box plots were then created. A comparison of the means of the diversity values between seasons was performed using Student's t test in STATISTIX 10 software at significance levels of P < 0.01 and P < 0.05. A hierarchical clustering dendrogram was generated using the unweighted pair group method with arithmetic average (UPGMA) and a Bray‒Curtis similarity index for 25 dominant phytoplankton assemblages. Canonical correspondence analysis (CCA) was conducted to examine the relationships between the 25 dominant phytoplankton species and the physicochemical characteristics of the water bodies, including pH, temperature, electrical conductivity, and nitrate, ammonium, total phosphorus, and orthophosphate contents, throughout each season. Pearson's correlation coefficient between water physicochemical properties and the ten dominant phytoplankton species was analyzed at a significance level of p < 0.05. Clustering analysis, Pearson’s correlation, and CCA were carried out using Past 4.03 software. Results Water physicochemical parameters The physicochemical properties of the water in the Ubol Ratana Dam were monitored at six locations from March 2022 to January 2023. The summary in Table 1 shows the seasonal variations in these properties. Throughout all seasons, the pH at each location ranged between 7.2 and 8.4. The water temperatures ranged from 25.4 to 29.1°C during the rainy and winter seasons but from 32.4 to 33.5°C in the summer. The concentrations of nitrate-nitrogen and ammonium-nitrogen varied across the locations. In the rainy season, the nitrate concentration ranged from 0.2 to 0.9 mg L − 1 ; in the winter, it ranged from 0.12 to 1.2 mg L − 1 ; and in the summer, it ranged from 0.11 to 1.3 L − 1 . Similarly, the ammonium concentration ranged from 0.05 to 0.3 mg L − 1 in the rainy season, from 0.07 to 0.24 mg L − 1 in the winter, and from 0.05 to 0.21 mg L − 1 in the summer. The water samples collected from water inlet 1, water inlet 2, the fish aquaculture areas, and the water near the municipality community exhibited higher concentrations of nitrate and ammonium compared to those obtained from the center of the dam and the water outlet location. The fish aquaculture areas had greater concentrations of nitrate-nitrogen than did the other sites. In summer, the total phosphorus and orthophosphate contents ranged from 0.11 to 0.16 mg L − 1 and from 0.39 to 0.51 mg L − 1 , respectively, which were greater than those in the other seasons. Therefore, both the total phosphorus and orthophosphate contents were highest in summer, followed by those in the winter and the rainy season, respectively. In addition, cyanobacterial microcystins were detected at concentrations ranging from 0.18 to 0.72 µg/L, while cylindrospermopsin was not detected in this study. Table 1 Seasonal variations in the physicochemical properties of the water from the Ubol Ratana Dam. Site S1 S2 S3 S4 S5 S6 Rainy pH Temperature (°C) EC (µS cm -1 ) Nitrate (mg L -1 ) Ammonium (mg L -1 ) Total phosphorus (mg L -1 ) Orthophosphate (mg L -1 ) Microcystins (μg L -1 ) Cylindrospermopsin (μg L -1 ) 8.1 28.7 245 0.90 0.19 0.05 0.09 ND ND 8.2 28.4 237 0.75 0.30 0.03 0.06 ND ND 8.4 29.1 185 0.20 0.05 0.03 0.06 ND ND 8.1 28.5 192 0.30 0.08 0.08 0.04 ND ND 8.2 28.4 242 1.20 0.20 0.14 0.06 ND ND 7.9 28.2 252 0.81 0.14 0.09 0.06 ND ND Winter pH Temperature (°C) EC (µS cm -1 ) Nitrate (mg L -1 ) Ammonium (mg L -1 ) Total phosphorus (mg L -1 ) Orthophosphate (mg L -1 ) Microcystins (μg L -1 ) Cylindrospermopsin (μg L -1 ) 7.3 25.8 215 1.20 0.24 0.07 0.21 ND ND 7.2 26.1 226 0.90 0.24 0.08 0.24 ND ND 7.4 25.9 194 0.12 0.07 0.07 0.24 0.35 ND 7.3 26.2 186 0.25 0.07 0.11 0.21 0.27 ND 7.6 25.4 229 1.10 0.21 0.08 0.24 ND ND 7.4 25.8 236 0.90 0.22 0.13 0.23 ND ND Summer pH Temperature (°C) EC (µS cm -1 ) Nitrate (mg L -1 ) Ammonium (mg L -1 ) Total phosphorus (mg L -1 ) Orthophosphate (mg L -1 ) Microcystins (μg L -1 ) Cylindrospermopsin (μg L -1 ) 7.8 32.4 186 0.80 0.21 0.11 0.39 ND ND 7.4 32.7 196 0.70 0.15 0.13 0.46 0.23 ND 7.6 32.6 174 0.11 0.09 0.16 0.43 0.64 ND 7.8 33.1 164 0.30 0.05 0.15 0.51 0.72 ND 7.4 33.5 195 1.30 0.15 0.11 0.44 0.35 ND 7.5 33.1 186 1.10 0.19 0.15 0.39 0.18 ND ND: Not detected The sites for collection included S1, water inlet 1; S2, water inlet 2; S3, the Center of the Dam; S4, the water outlet; S5, fish farming; and S6, the Non-Sang Municipality Community. Phytoplankton community composition and cell density Through microscopic observation, a total of 84 phytoplankton species, representing 8 phyla, were identified from water samples collected from 6 locations in the Ubol Ratana Dam during the rainy, winter, and summer seasons of 2022–2023. The phytoplankton community had the following species richness in each phylum: Chlorophyta had the most species (34.51%), followed by Cyanophyta (23.81%), Euglenophyta (14.29%), Bacillariophyta (11.90%), Pyrrhophyta (7.14%), Chrysophyta (5.95%), Cryptophyta (1.19%), and Xanthophyta (1.19%) (Fig. 2 a). Table 2 provides a summary of all the phytoplankton species present. Cyanobacteria (Cyanophyta) had the highest cell density, accounting for approximately 84.06% of the total cell density (Fig. 2 b). The following taxa dominated: Cyanobacterium Microcystis aeruginosa Kützing was the most abundant at 55.87% (114×10 4 cells L − 1 ), followed by Planktothrix sp. at 7.79% (15.9 ×10 4 cells L − 1 ) and Cylindrospermopsis raciborskii (Woloszynska) Seenaya et Subba Raju at 6.09% (12.4 ×10 4 cells L − 1 ). The remaining taxa included Microcystis wesenbergii , Microcystis viridis , Raphiopsis raciborskii , Planktolyngbya limnetica , Pseudanabaena limnetica , Pseudanabaena mucicola , Merismopedia punctata , and Chroococcus sp. Table 2 Phytoplankton species derived from the Ubol Ratana Dam. Phylum Phytoplankton species Cyanophyta Microcystis aeruginosa (55.87), Microcystis wesenbergii (5.06), Microcystis viridis (0.56), Raphiopsis raciborskii (1.66), Cylindrospermopsis raciborskii (6.09), Cylindrospermopsis philippinensis , Planktothrix sp. 1 (7.79), Planktotrix sp. 2, Planktolyngbya limnetica (3.33), Pseudanabaena limnetica (0.65), Pseudanabaena mucicola , Aphanizomenon sp., Merismopedia sp.1, Merismopedia punctata (0.22), Chroococcus sp. (0.37), Oscillatoria sp.1 (0.26), Oscillatoria sp. 2 (0.27), Synechocystis sp., Anabaena planctonica (1.19), Anabaena sp.1 Chlorophyta Pediastrum sp.1, Pediastrum duplex , Pediastrum simplex , Monoraphidium contortum (0.52), Monoraphidium irregulare , Cosmarium sp. 1, Cosmarium sp. 2 Scenedesmus sp., Staurastrum sp., Staurastrum gracile , Coelastrum sp., Crucigeniella sp., Tetraedron sp., Coelastrum sp., Coelastrum microporum , Coelastrum polychordum , Sphaerocystis sp., Selenastrum sp., Staurodesmus curvatus , Desmidium sp., Didymocystis sp., Elakatotrix sp., Eudorina sp. (0.22), Radiococcus sp., Ankistrodesmus sp., Botryococcus braunii (0.28), Dictyosphaerium sp., Dictyosphaerium tetrachotomum , Tetrastrum heterocanthum Euglenophyta Euglena sp.1 (0.99), Euglena sp.2 (0.57), Euglena gracilis , Phacus helicoides (0.48), Phacus longicauda , Phacus sp.1, Phacus sp.2, Trachelomonas sp.1 (0.67), Trachelomonas sp.2, Trachelomonas hispida , Trachelomonas volvocinopsis , Strombomonas sp. Pyrrhophyta Peridinium sp. 1 (2.45), Peridinium sp. 2, Peridinium sp. 3, Ceratium sp.1 (2.25), Ceratium sp.2, Ceratium hirudiella Chrysophyta Dinobryon sp., Dinobryon sertularia, Centritractus sp., Mallomonas sp., Mallomonas splendens Fragilaria sp., Fragilaria ulna, Rhizosolenia sp., Navicula sp., Achnanthidium sp., Aulacoseira granulate (1.78), Nitzschia sp., Cyclotella sp., Cymbella sp. (0.41), Synedra ulna Cryptophyta Cryptomonas sp. (0.94) Xanthophyta Isthmochloron sp. The percent (%) abundance of dominant phytoplankton species is indicated in parentheses. Seasonal variation in phytoplankton diversity Venn diagrams, with colored areas representing phytoplankton abundance across the six locations in each season, are shown in Fig. 3 a-c. The overlapping areas of the diagram representing the rainy, winter, and summer seasons included a total of 10, 8, and 8 species, respectively, indicating the presence of phytoplankton communities at the 6 sampling locations. In the rainy season, M. aeruginosa , M. wesenbergii , C. raciborskii , Planktotrix sp. 1, Planktolyngbya limnetica , Anabaena planctonica , Peridinium sp. 1, Peridinium sp. 2, Peridinium sp. 3, and Ceratium sp.1 were found. M. aeruginosa , M. wesenbergii , C. raciborskii , Planktotrix sp. 1, P. limnetica , Oscillatoria sp. 2, Peridinium sp. 1, and Ceratium sp. 1 were detected in the winter season. In summer, the 8 phytoplankton species present were M. aeruginosa , M. wesenbergii , C. raciborskii , Planktotrix sp. 1, Oscillatoria sp. 1, A. planctonica , Peridinium sp. 1, and Peridinium sp. 2. The diagrams show that the five taxa shared across all six locations in all seasons included M. aeruginosa , M. wesenbergii , C. raciborskii, Planktotrix sp ., and Peridinium sp. Proportion of phytoplankton at different sites The spatial and temporal variations in the cell abundance and percentage total abundance of phytoplankton are shown in Fig. 4 a, b. The phytoplankton density ranged from 1.99 to 8.10🞨10 4 cells L − 1 during the rainy season and from 5.91 to 17.67🞨10 4 cells L − 1 during the winter (Fig. 4 a). The cell density was highest in summer, ranging from 11.77 to 28.86🞨10 4 cells L − 1 , and the community was mainly composed of cyanobacteria. Based on spatial analysis, it was found that the center of the dam (S3) and the water outlet (S4) consistently had the highest levels of total phytoplankton and cyanobacteria across all seasons. Cyanobacteria were most prevalent, making up approximately 60.42–91.82% of the total abundance in the study area (see Fig. 4 b). In particular, areas S3 and S4 had the highest relative abundance of cyanobacteria, exceeding 80%. Notably, the abundance of Euglenophyta in these areas was very low, at 0.05% or less. On the other hand, in the Non-Sang Municipality Community (S6), there was a noticeable increase in the proportion of Euglenophyta, ranging from 12.85–17.85%. Seasonal variation in phytoplankton diversity In this study, we measured the abundance of phytoplankton in three different seasons. The highest total phytoplankton cell density was recorded in summer at 114.25 🞨10 4 cells L − 1 (Fig. 5 a). The greatest proportion of cyanobacterial biomass was observed in summer at 86.23%, followed by that in winter at 81.89% and in the rainy season at 79.94%. The relative abundance of Cyanophyta increased, while the abundances of other phytoplankton phyla, such as Chlorophyta, Euglenophyta, Pyrrophyta, and Chrysophyta, decreased. In terms of diversity, the Shannon‒Wiener index revealed statistically significant differences between the rainy and winter seasons (p < 0.05), between the winter and summer seasons (p < 0.01), and between the rainy and summer seasons (p < 0.01) (Fig. 5 b). The index value was 1.92 for summer, which was higher than that for the other seasons. None of the pairwise comparisons indicated a significant difference in species richness across seasons in this study (Fig. 5 c). The evenness indices also differed significantly (p < 0.05, p < 0.01) among the different seasons (Fig. 5 d). Assemblage of 25 dominant phytoplankton species Cluster analysis revealed two main clusters derived from 25 predominant phytoplankton species (Fig. 6 ). Cluster I was further divided into two subclusters, with each minor cluster containing five species with a similarity of at least 20%. Within main cluster II, there were three related clusters: the first contained three species ( M. viridis , Oscillatoria sp. 2, and R. raciborskii – Mv, Os2, Rr); the second contained seven species ( P. limnetica , A. planctonica , M. punctata , Chroococcus sp., Oscillatoria sp. 1, M. contortum and Botryococcus braunii – PsI, Ap, Mp, Ch, Os1, Mc, Bb); and the third contained three species ( Eudorina sp., Cryptomonas sp., and P. helicoides – Eu, Cym, Ph) with a similarity of more than 30%. However, two phytoplankton species, P. limnetica and M. aeruginosa , appeared to be separate from the main group. Canonical correspondence analysis In our study, we used canonical correspondence analysis (CCA) to examine the relationships between various physicochemical variables (represented by vector lines), including pH, water temperature, EC, nitrate, ammonium, total phosphorus, and orthophosphate content, and the 25 dominant phytoplankton species (depicted as blue dots) across different seasons. During the rainy period, the eigenvalues of the first two axes, 1 and 2, were 0.654 and 0.536, respectively (Fig. 7 a). These two axes represented 66.8% of the total variation. The data indicated that C. raciborskii and A. granulate were influenced by ammonium and nitrate levels, as well as by electrical conductivity (EC), which were negatively correlated with water pH and temperature. Several phytoplankton species, including Chroococcus sp., B. braunii , M. punctata , P. limnetica , M. contortum , Planktotrix sp. 1, Eudorina sp., Oscillatoria sp. 1, R. raciborskii , and Peridinium sp. 1, were positively correlated with water pH and temperature, while M. aeruginosa was positively associated with total phosphorus content. In the winter season, the first two axes represented 70.6% of the total variation (Fig. 7 b). The CCA data also indicated that the total phosphorus content and pH were positively correlated with M. aeruginosa . A. granulate , Cryptomonas sp., and Euglena sp. 2 exhibited positive correlations with EC and ammonium and nitrate contents, which are presented on the right side of the triplot. Several phytoplankton species were also negatively affected by ammonium and nitrate contents and EC. In summer, 63.8% of the total variance was explained by axes 1 (0.675) and 2 (0.572), and the total eigenvalue was 1.247 (Fig. 7 c). In addition, C. raciborskii , M. viridis , Oscillatoria sp. 1, Euglena sp. 1, Oscillatoria sp. 2, Trachelomonas sp. 1, Cryptomonas sp., P. helicoides , A. granulate , and Euglena sp. 2 were negatively correlated with EC, temperature, and nitrate and ammonium contents in the water. The abundance of M. aeruginosa was correlated with orthophosphate and pH and was affected by temperature, nitrate-ammonium content, and EC. The nitrate and ammonium contents, temperature, and EC were also crucial factors affecting phytoplankton in all seasons. Correlation analysis The influence of water physicochemical parameters such as pH, temperature, EC, and nitrate, ammonium, total phosphorus, and orthophosphate contents on the growth of dominant phytoplankton was analyzed, as shown in Fig. 8 . Pearson’s correlation analysis indicated a significant difference (p < 0.05). The growth of C. raciborskii had a strong positive correlation with temperature (r = 0.85) and orthophosphate content (r = 0.84). The abundance of M. aeruginosa was significantly positively correlated with orthophosphate content (r = 0.75) but negatively correlated with EC (r = -0.79), nitrate content (r= -0.51), and ammonium content (r = -0.59) and showed a slight correlation with temperature. Discussion In this study, the water physicochemical parameters and phytoplankton community composition of the Ubol Ratana Dam were investigated to assess their relationships with water contaminants. The levels of nutrients, particularly nitrogen and phosphorus, were examined because they can lead to eutrophication and changes in the phytoplankton community. This study also highlights the challenges associated with assessing the diversity and community composition of phytoplankton and the consequences of temporal and seasonal variations in physical and chemical parameters. Overall, the results of this study revealed a relationship between water physicochemical properties, seasonal changes, site location, and phytoplankton and cyanobacteria biodiversity in the Ubol Ratana Dam from 2022–2023. We identified 84 species of phytoplankton belonging to 8 phyla: Bacillariophyta, Chlorophyta, Cyanophyta, Euglenophyta, Pyrrhophyta, Chrysophyta, Cryptophyta, and Xanthophyta. In a previous study in 2018 by Muangsringam et al., six of these phyla were reported in the same reservoir: Bacillariophyta, Chlorophyta, Cyanophyta, Pyrrhophyta, Cryptophyta, and Euglenophyta. We identified all the phytoplankton at the species level, and M. aeruginosa was found to be the dominant species in the reservoir, consistent with a previous study by Muangsringam et al. ( 2019 ). Cyanobacteria frequently accounted for over 80% of the total phytoplankton. Several potentially toxic genera, such as Microcystis , Cylindrospermopsis , Planktotrix , Planktolyngbya , and Anabaena , were dominant throughout our study period (Barros et al. 2019; Crevecoeur et al. 2023; Mengchouy and Meksumpun 2022 ; Muangsringam et al. 2019 ; Passos et al. 2022 ; Tito et al. 2022 ). Our findings suggest that phytoplankton respond favorably to a variety of nutrients depending on location and season. The abundance of cyanobacteria (Cyanophyta) and the density of phytoplankton varied across different geographical locations, and distinct spatial and temporal variations were observed across sampling sites despite their high relative abundance. Biological indicators are often used to assess the pollution status of aquatic ecosystems (Gogoi et al. 2019). Both the evenness distribution and Shannon‒Wiener index exhibited similar trends, showing significant increases during the winter and summer seasons and reaching their lowest points during the rainy season. The findings of this study align with those of Sun et al. ( 2023 ) and Arumugham et al. ( 2023 ). The seasonal changes in diversity indicate that the alterations occurred due to shifts in the evenness and Shannon‒Wiener indices rather than due to changes in species richness (Stirling and Wilsey, 2001; SureshKumar and Thomas, 2019 ). The Shannon‒Wiener diversity index ranged from 1 to 2, indicating moderate diversity (Parakkandi et al. 2021; Wihm, 1975). Seasonal hydrological changes may have also impacted these indices. The irrigation systems result in low riverine inflow and stable water availability in the winter and summer periods and generate changes in phytoplankton diversity, with significant increases during the winter and summer seasons, and the lowest levels occurred during the rainy season. The findings of the present study are consistent with those of Sun et al. ( 2023 ) and Arumugham et al. ( 2023 ). The seasonal changes in diversity indicate that the alterations occurred due to shifts in the evenness and Shannon‒Wiener indices rather than due to changes in species richness (Stirling and Wilsey, 2001; SureshKumar and Thomas, 2019 ). The Shannon‒Wiener diversity index ranged from 1 to 2, indicating moderate diversity (Parakkandi et al. 2021; Wihm, 1975). Seasonal hydrological changes may also affect these indices. The irrigation systems result in low riverine inflow and stable water availability in the winter and summer periods, resulting in changes in diversity. The ecology of phytoplankton in aquatic environments is affected by seasonal changes, leading to fluctuations in species diversity, evenness, the Shannon‒Wiener index, and cell density (Sun et al. 2023 ; SureshKumar et al. 2019). Temperature is a significant factor that regulates the growth of phytoplankton (Somdee et al. 2013 ; Wang et al. 2023 ). During the study, it was observed that species richness decreased in the summer. The results showed an overall increase in the total phytoplankton population in the summer months, with a specific increase in the proportion of Cyanophyta. High temperatures led to a decrease in the proportion of other phyla, with Chlorophyta, Euglenophyta, Pyrrophytophyta, and Bacillariophyta showing decreases in relative abundances. This result is consistent with the findings of Sun et al. ( 2023 ), who noted that Euglenophyta and Bacillariophyta are susceptible to increasing temperatures. These genera may be unable to grow at high temperatures due to their physiological incapacity to tolerate high temperatures, while cyanobacteria flourish and reproduce rapidly under such conditions. Increasing water temperatures induce competitive exclusion, resulting in the dominance of cyanobacteria and the disappearance of some taxa (Briddon et al. 2022 ; Sun et al. 2023 ). Cyanobacteria can impact phytoplankton communities by influencing planktonic species when the water temperature reaches a certain level. The impact of seasonal variation on cell density seemed to be greatest in the summer and lowest during the rainy season. It was challenging to determine the impact of temperature on phytoplankton abundance due to the influence of other factors, such as low water levels in summer, which led to increased nutrient content and phytoplankton biomass. However, the density of phytoplankton and cyanobacteria increased the most during the summer at all sites. This was evident from the positive correlation between water temperature and dominant phytoplankton, such as cyanobacteria. This finding is consistent with that of Kong et al. ( 2019 ). However, it remains unclear whether phytoplankton prefer high temperatures (Wang et al. 2022 ). It is possible that high temperatures do not lead to cyanobacterial blooms but rather intensify these phenomena by increasing thermal stratification and depth shifts through gas vesicles (Crevecoeur et al. 2023; Tito et al. 2022 ). Seasonal temperature significantly influences the regulation of phytoplankton reproduction, growth, and behavior. The growth of eutrophic phytoplankton is significantly affected by physicochemical factors, which cause fluctuations in phytoplankton biomass. These parameters change because of changes in weather-related processes. Phytoplankton can thrive well under a broad pH range of 6.5–10 (Ballah et al. 2019 ; SureshKumar and Thomas 2019 ). Cyanobacteria, particularly M . aeruginosa , can utilize HCO 3 − and CO 2 for photosynthesis in alkaline water, which increases their density (Wei et al. 2022 ). According to our findings, the pH during the rainy season was approximately 8, and the CCA pointed to pH as a major variable. The observed alkaline pH may be explained by water input from runoff from nearby agricultural land, which includes both organic and inorganic components that lead to the breakdown of organic waste (Gogoi et al. 2019). However, the overall quantity of phytoplankton was lower than that in the other seasons. We can speculate the causes of the low phytoplankton abundance observed. In this study, alkaline pH was linked to the growth of M . aeruginosa , but this value was not optimal (Wei et al. 2022 ). Moreover, sudden changes in hydrology, such as increased inflow and outflow of water from rainstorms, make these areas unsuitable for phytoplankton growth. Moreover, increased water storage contributes to nutrient dilution (Gogoi et al. 2019; Parakkandi et al. 2021). This causes changes in the physicochemical characteristics of water bodies. Increased levels of nutrients, particularly nitrogen and phosphorus, are essential for the growth, survival, and proliferation of phytoplankton, as they are necessary for phytoplankton metabolic processes (Arumugham et al. 2023 ; Barros et al. 2019; Briddon et al. 2022 ; Lv et al. 2011 ; SureshKumar and Thomas 2019 ). Higher levels of nitrate and ammonium were detected near the Non-Sang municipality community and fish farm locations throughout all seasons, which was caused by the use of organic and chemical fertilizers, excess feed, domestic wastewater, and anthropogenic activities, which contribute to the nutrient load in the inflowing water and in the water surrounding the reservoir (Akter et al. 2022 ; de Lima Pinheiro et al. 2023). Additionally, the changes observed with increased nitrogen content suggested that nitrates and ammonium strongly influence the growth of other phytoplankton, such as Euglenophytes. Taxa such as Euglena sp., which thrive in nutrient-rich water, frequently serve as bioindicators of hypereutrophic water pollution (Parakkandi et al. 2021). Surprisingly, although the stagnant water at the water outflow and water in the center of the dam had the highest total phytoplankton and cyanobacteria abundances, both nitrate and ammonium levels remained low in these areas. However, several studies have shown that cyanobacteria, particularly the common species M . aeruginosa , typically thrive in environments rich in nitrate and ammonium, which promote biomass growth (Sun et al. 2023 ). In contrast, a rapid utilization of nutrients may lead to decreases in nitrate and ammonium levels, generating a strong negative correlation. This finding is similar to that of Pitchaikani and Lipton ( 2016 ). Fluctuations in the levels of limiting nutrients such as ammonium and nitrate often regulate the growth of phytoplankton (Pitchaikani and Lipton 2016 ). The concentration of nitrogen sources decreases as nitrogen is rapidly consumed (SureshKumar and Thomas 2019 ). It is also possible that the availability of nitrogen from nitrogen-fixing cyanobacteria is sufficient to support the growth of nonheterocystous phytoplankton (Lv et al. 2011 ). For example, the nitrogen-fixing cyanobacterium C. raciborskii was present consistently throughout the seasons, along with M. aeruginosa (Crevecoeur et al. 2023). In nitrogen-limited environments, Microcystis is more abundant than other cyanobacterial species due to its effective assimilation of regenerated ammonium (Flanzenbaum et al. ( 2022 ). The concentration of phosphorus in the water bodies was low during the rainy season and increased during the summer. This finding was supported by a strong positive correlation between orthophosphate content and an increase in dominant phytoplankton and total biomass. Cyanobacteria rely more on phosphate as a nutrient than do other phytoplankton (Lv et al. ( 2011 ). However, less dissolved phosphate may be utilized during the rainy season due to lower plankton density, which can be attributed to various factors. The reservoir water only drains in the winter season, and water evaporates in the summer, giving rise to greater nutrient concentrations. Several factors contribute to phosphorus accumulation, particularly in summer, including fertilizer inputs, decomposition of dead plants and animals, aquaculture, and residential activities such as detergent use and the discharge of domestic effluent (Ajayan et al. 2017; Kundu et al. 2015 ). The nutrient supply for phytoplankton comes from both the water inflow and the nutrients generated within the reservoir from the degradation of organic matter by bacteria (Crevecoeur et al. 2023). Our results in this regard are inconclusive. The nutrient levels in the central dam area with high phytoplankton biomass in the summer were not significantly different from those at the other stations. However, it is possible that there was enough consumption of available phosphorus. Cyanobacteria can regulate phosphate absorption, consume it in subsequent life stages, and store in the form of polyphosphate because of their high growth rate and prolonged life span (Sanz-Luque et al. 2020 ; SureshKumar and Thomas 2019 ). They exhibit phosphorus saturation when cultivated in phosphate-rich environments (Barros et al. 2019; Dolman et al. 2012 ). However, the significant occurrence of Microcystis blooms may be linked to high phosphorus concentrations since phosphorus is a limiting nutrient under low nitrogen‒phosphorus ratios (Barros et al. 2019; Jargal and An 2023 ; Lv et al. 2011 ). Our findings indicate that phosphate is a significant factor affecting the increase in phytoplankton biomass in this reservoir. We believe that rather than phosphate content, nitrate and ammonium contents play key roles in influencing the biomass of phytoplankton and cyanobacteria. The CCA showed that the influencing factors varied with seasonal fluctuations. Potential factors are indicated by the length of the arrows in the CCA (Sharma et al., 2015 ). Nitrate and ammonium levels significantly influence the composition of the phytoplankton community throughout the year, suggesting that these factors are not impacted by seasonal changes and indicating that nitrogen levels restrict phytoplankton growth in the reservoir. We observed that total phytoplankton, particularly cyanobacteria, more efficiently utilized nitrate, ammonium, and orthophosphate when the temperature was optimal. Cyanobacteria appear to adapt effectively to environmental changes. The abundance of phytoplankton can be influenced not only by individual factors but also by interactions among multiple crucial factors. CCA revealed a broad pattern in the physicochemical variables in the rainy season, which might be related to sudden hydrological changes that lead to changes in the concentrations of nutrients in the water bodies. Based on the above discussion, the water management measures taken in reservoirs during each season have a significant impact on water physicochemical changes and nutrient dynamics. These factors ultimately control the composition of phytoplankton (Kumar et al., 2020 ; Malik and Rathi, 2022 ; Muangsringam et al., 2019 ). Seasonal variations in irrigation systems play an important role in the formation of unique phytoplankton communities. During the summer, when temperatures rise and the daily period of sunshine increases, a small amount of water flows through two gates at the confluence of the dam's outflow and center. This generates low agitation, low turbidity, stagnation, increased nutrients, and increased light availability at the epilimnion of the reservoir. These factors can positively impact and enhance the growth, photosynthesis, reproduction, and buoyancy of phytoplankton (Crevecoeur et al. 2023; Jargal and An 2023 ; Mânoca and de Lima Isaac 2023 ). A limitation of this study is that the physicochemical and biological parameters of the riverine water inflow that may influence phytoplankton population changes during seasonal variation were not investigated. Although phytoplankton blooms are currently not a serious issue and the amount of microcystin does not exceed the standard values of the World Health Organization (WHO), which recommends a maximum level of 1 µg/L of microcystin per liter of water in this reservoir, it is important to monitor the water body as the concentration of nutrients such as nitrogen and phosphorus continues to increase. This can lead to a change in the phytoplankton community composition and to cyanobacterial blooms. These significant findings support the previous prediction that cyanobacterial proliferation occurred in the central dam zone during the summer. Conclusions In this study, we investigated the physicochemical properties of the water and the phytoplankton community at six different locations at the Ubol Ratana Dam in Thailand during the years 2022–2023. Throughout spatial and temporal studies of the reservoir, a total of 84 species of phytoplankton were identified and classified into 8 phyla. Cyanobacteria made up more than 80% of the total phytoplankton population in all sites and seasons, with the most prevalent species being M . aeruginosa , which was found at high densities. The Shannon‒Wiener index indicated that the phytoplankton diversity and evenness were highest in summer, indicating that it was the most favorable season for phytoplankton growth. Nitrate and ammonium levels were related to phosphate content and temperature across all seasons, as evidenced by the CCA and strong correlations. Our findings demonstrated that cyanobacterial blooms were influenced by the physicochemical properties of the water body, particularly due to seasonal variations. This suggests that irrigation systems significantly affect water bodies in different seasons, leading to changes in phytoplankton community composition and diversity. Therefore, this study provides important baseline information for further research on the assessment, forecasting, and surveillance of the distribution of phytoplankton in aquatic ecosystems. Declarations Author contributions Anchana Somdee: Conceptualization, methodology, formal analysis and investigation, and writing-original draft preparation. Weeraput Butsat: Investigation. Theerasak Somdee: Conceptualization, methodology, proofreading, review, and editing. Funding This research was supported by the Fundamental Fund of Khon Kaen University, Thailand. Data availability All data in this research are available upon request from the corresponding author at [email protected] Ethical approval Not applicable. Consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare no competing interests. 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J Clean Prod 294:126291. https://doi.org/10.1016/j.jclepro.2021.126291 Supplementary Files 28May24SUPPLEMENTARYINFORMATION.docx Cite Share Download PDF Status: Published Journal Publication published 13 Oct, 2025 Read the published version in Environmental Science and Pollution Research → Version 1 posted Editorial decision: Major Revision 07 Sep, 2024 Reviewers agreed at journal 06 Aug, 2024 Reviewers invited by journal 25 Jun, 2024 Editor invited by journal 13 Jun, 2024 Editor assigned by journal 05 Jun, 2024 First submitted to journal 02 Jun, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4488037","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":319024570,"identity":"26114c22-7f49-489b-bba6-349d906a0d82","order_by":0,"name":"Anchana Somdee","email":"","orcid":"","institution":"Khon Kaen University","correspondingAuthor":false,"prefix":"","firstName":"Anchana","middleName":"","lastName":"Somdee","suffix":""},{"id":319024571,"identity":"50cade29-076d-487a-bcef-e3d774207ba1","order_by":1,"name":"Weeraput Butsat","email":"","orcid":"","institution":"Khon Kaen University","correspondingAuthor":false,"prefix":"","firstName":"Weeraput","middleName":"","lastName":"Butsat","suffix":""},{"id":319024572,"identity":"25157671-98de-4fc1-b8a7-176d4614559e","order_by":2,"name":"Theerasak Somdee","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtElEQVRIiWNgGAWjYDACCTBpYyAB5ScQ1MEDUZoG02JAtJbDJGixl24+/OFjznljyRkJjB9+MPzJI2yLzLE0yZnbbptJSyQwS/YwGBQT4bAcM2bebbdt5CQSGKSBDktsIKwl//Nn3m3nQFqYfxOpJYdBmnfbAZDD2Ii05UaaGdAvycaSPQ/bLHsMjAlrYZ+R/PjDx212hjOOJx++8aNCjrAWJMAIVGxAgvpRMApGwSgYBbgBAHPdNZDVdSBoAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-9793-9975","institution":"Khon Kaen University Faculty of Science","correspondingAuthor":true,"prefix":"","firstName":"Theerasak","middleName":"","lastName":"Somdee","suffix":""}],"badges":[],"createdAt":"2024-05-28 04:30:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4488037/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4488037/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11356-025-37019-6","type":"published","date":"2025-10-13T15:56:54+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":60519610,"identity":"3fc39d72-25b8-4fbb-8d15-96dd9737c1ee","added_by":"auto","created_at":"2024-07-17 16:12:36","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":463800,"visible":true,"origin":"","legend":"\u003cp\u003eMap of the geographical location of the Ubon Ratana Dam in Khon Kaen, Thailand: \u003cstrong\u003ea\u003c/strong\u003e: Thailand, \u003cstrong\u003eb\u003c/strong\u003e: Map of Khon Kaen Province, c: Ubon Ratana Dam with numbered sampling stations, as follows: water inlet 1, S2: water inlet 2, S3: center of the dam, S4: water outlet, S5: fish farming area, S6: Non-Sang Municipality community\u003c/p\u003e","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4488037/v1/b8bf653b0e096027c06166af.jpg"},{"id":60519012,"identity":"83a17b4b-fe12-4fcb-b51f-9c4143cbdadf","added_by":"auto","created_at":"2024-07-17 16:04:36","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":127111,"visible":true,"origin":"","legend":"\u003cp\u003eSpecies richness (\u003cstrong\u003ea\u003c/strong\u003e) and cell density (\u003cstrong\u003eb\u003c/strong\u003e) of all phyla of phytoplankton are shown in percentage units.\u003c/p\u003e","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4488037/v1/c578f7e6879e29fe63d78694.jpg"},{"id":60519611,"identity":"795dff13-0b2e-4314-abca-84355978b06c","added_by":"auto","created_at":"2024-07-17 16:12:36","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":109808,"visible":true,"origin":"","legend":"\u003cp\u003eVenn diagram illustrating the species of phytoplankton at each site during the rainy (\u003cstrong\u003ea\u003c/strong\u003e), winter (\u003cstrong\u003eb\u003c/strong\u003e), and summer (\u003cstrong\u003ec\u003c/strong\u003e) seasons. The sites are S1: water inlet 1, S2: water inlet 2, S3: center of the dam, S4: water outlet, S5: fish farming area, and S6: Non-Sang Municipality community.\u003c/p\u003e","description":"","filename":"Fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4488037/v1/3a6b6e303a41ba26418fd333.jpg"},{"id":60519018,"identity":"1c70fb33-2e26-407f-af02-1c2b5e242856","added_by":"auto","created_at":"2024-07-17 16:04:36","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":161317,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial and temporal variations in the concentration of phytoplankton cells (\u003cstrong\u003ea\u003c/strong\u003e) and relative abundance (%) (\u003cstrong\u003eb\u003c/strong\u003e) at each site. The sites are S1: water inlet 1, S2: water inlet 2, S3: center of the dam, S4: water outlet, S5: fish farming area, and S6: Non-Sang Municipality community.\u003c/p\u003e","description":"","filename":"FIg4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4488037/v1/5f318d4ba6ca784a4dd40efe.jpg"},{"id":60519015,"identity":"40846490-968b-46a8-b3d3-fd029d83cd9d","added_by":"auto","created_at":"2024-07-17 16:04:36","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":186719,"visible":true,"origin":"","legend":"\u003cp\u003eSeasonal variations in the relative abundance of phyla and total phytoplankton abundance (\u003cstrong\u003ea\u003c/strong\u003e), Shannon‒Wiener index (\u003cstrong\u003eb\u003c/strong\u003e), Margalef species richness (\u003cstrong\u003ec\u003c/strong\u003e), and evenness (\u003cstrong\u003ed\u003c/strong\u003e). The bar represents the relative abundance, and the line represents the total cell abundance. *: significant at p\u0026lt;0.05, **: significant at p\u0026lt;0.01, ns: nonsignificant.\u003c/p\u003e","description":"","filename":"Fig5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4488037/v1/7b652e34871ae35fc6144954.jpg"},{"id":60519014,"identity":"f911ec26-5fa4-4bff-89d2-31ebdb1e8d02","added_by":"auto","created_at":"2024-07-17 16:04:36","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":86595,"visible":true,"origin":"","legend":"\u003cp\u003eHierarchical clustering analysis derived from dominant phytoplankton abundance using Bray‒Curtis similarity. The 25 dominant species included \u003cem\u003eMicrocystis wesenbergii\u003c/em\u003e: Mw, \u003cem\u003ePeridinium\u003c/em\u003e sp. 1: Per1, \u003cem\u003ePlanktotrix\u003c/em\u003esp. 1: Pl1\u003cem\u003e, Ceratium\u003c/em\u003e sp.1: Cer1, \u003cem\u003eCylindrospermopsis raciborskii\u003c/em\u003e: Cr, \u003cem\u003eEuglena\u003c/em\u003e sp.1: Eug1, \u003cem\u003eEuglena\u003c/em\u003e sp.2: Eug2, \u003cem\u003eAulacoseira granulate\u003c/em\u003e: Aug, \u003cem\u003eCryptomonas\u003c/em\u003e sp.: Cry, \u003cem\u003eTrachelomonas \u003c/em\u003esp.1: Tr1, \u003cem\u003eMicrocystis viridis\u003c/em\u003e: Mv, \u003cem\u003eOscillatoria\u003c/em\u003e sp. 2: Os2, \u003cem\u003eRaphiopsis raciborskii\u003c/em\u003e: Rr, \u003cem\u003ePseudanabaena limnetica\u003c/em\u003e: Psl, \u003cem\u003eAnabaena planctonica\u003c/em\u003e: Ap, \u003cem\u003eMerismopedia punctata\u003c/em\u003e: Mp, \u003cem\u003eChroococcus\u003c/em\u003esp.: Ch, \u003cem\u003eOscillatoria\u003c/em\u003e sp. 1: Os1, \u003cem\u003eMonoraphidium contortum\u003c/em\u003e: Mc, \u003cem\u003eBotryococcus braunii\u003c/em\u003e: Bb, \u003cem\u003eEudorina \u003c/em\u003esp.: Eu, \u003cem\u003eCymbella \u003c/em\u003esp.: Cym, \u003cem\u003ePhacus helicoides\u003c/em\u003e: Ph, \u003cem\u003ePlanktolyngbya limnetica\u003c/em\u003e: Pl, \u003cem\u003eMicrocystis aeruginosa\u003c/em\u003e: Ma.\u003c/p\u003e","description":"","filename":"Fig6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4488037/v1/10dd54ff06da5e8afe26bcd2.jpg"},{"id":60519613,"identity":"176bf977-4fcc-4250-82b5-e86a0b500ccf","added_by":"auto","created_at":"2024-07-17 16:12:36","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":221576,"visible":true,"origin":"","legend":"\u003cp\u003eA canonical correspondence analysis (CCA) ordination plot of the physicochemical variables and dominant phytoplankton abundance in the rainy (\u003cstrong\u003ea\u003c/strong\u003e), winter (\u003cstrong\u003eb\u003c/strong\u003e), and summer (\u003cstrong\u003ec\u003c/strong\u003e) seasons. The 25 dominant phytoplankton species included \u003cem\u003eMicrocystis wesenbergii\u003c/em\u003e: Mw, \u003cem\u003ePeridinium\u003c/em\u003e sp. 1: Per1, \u003cem\u003ePlanktotrix\u003c/em\u003e sp. 1: Pl1\u003cem\u003e, Ceratium\u003c/em\u003e sp.1: Cer1, \u003cem\u003eCylindrospermopsis raciborskii\u003c/em\u003e: Cr, \u003cem\u003eEuglena\u003c/em\u003e sp.1: Eug1, \u003cem\u003eEuglena\u003c/em\u003e sp.2: Eug2, \u003cem\u003eAulacoseira granulate\u003c/em\u003e: Aug, \u003cem\u003eCryptomonas\u003c/em\u003e sp.: Cry, \u003cem\u003eTrachelomonas \u003c/em\u003esp.1: Tr1, \u003cem\u003eMicrocystis viridis\u003c/em\u003e: Mv, \u003cem\u003eOscillatoria\u003c/em\u003e sp. 2: Os2, \u003cem\u003eRaphiopsis raciborskii\u003c/em\u003e: Rr, \u003cem\u003ePseudanabaena limnetica\u003c/em\u003e: Psl, \u003cem\u003eAnabaena planctonica\u003c/em\u003e: Ap, \u003cem\u003eMerismopedia punctata\u003c/em\u003e: Mp, \u003cem\u003eChroococcus\u003c/em\u003e sp.: Ch, \u003cem\u003eOscillatoria\u003c/em\u003e sp. 1: Os1, \u003cem\u003eMonoraphidium contortum\u003c/em\u003e: Mc, \u003cem\u003eBotryococcus braunii\u003c/em\u003e: Bb, \u003cem\u003eEudorina \u003c/em\u003esp.: Eu, \u003cem\u003eCymbella \u003c/em\u003esp.: Cym, \u003cem\u003ePhacus helicoides\u003c/em\u003e: Ph, \u003cem\u003ePlanktolyngbya limnetica\u003c/em\u003e: Pl, \u003cem\u003eMicrocystis aeruginosa\u003c/em\u003e: Ma. The physicochemical parameters are represented by lines, including pH, temperature (Temp), electrical conductivity (EC), and NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e (nitrate), NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e (ammonium), total P (total phosphorus), and Ortho P (orthophosphate) contents. The sites during the rainy (R), winter (W), and summer (S) seasons are the 1: water inlet 1, 2: water inlet 2, 3: center of the dam, 4: water outlet, 5: fish farming area, and 6: Non-Sang Municipality community.\u003c/p\u003e","description":"","filename":"Fig7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4488037/v1/9246cf3c53d3d5c66fb75a49.jpg"},{"id":60519021,"identity":"3911e216-cee4-4ce1-aaca-6800dccbf945","added_by":"auto","created_at":"2024-07-17 16:04:36","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":337208,"visible":true,"origin":"","legend":"\u003cp\u003ePearson’s correlation analysis of all environmental parameters and dominant species of phytoplankton. The environmental parameters included pH, temperature (Temp), electrical conductivity (EC), NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e (nitrate), NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e (ammonium), total P (total phosphorus), and orthoP (orthophosphate). The 10 most prevalent species were \u003cem\u003eMicrocystis aeruginosa\u003c/em\u003e: Ma, \u003cem\u003eMicrocystis wesenbergii\u003c/em\u003e: Mw, \u003cem\u003eRaphiopsis raciborskii\u003c/em\u003e: Rr, \u003cem\u003eCylindrospermopsis raciborskii\u003c/em\u003e: Cr, \u003cem\u003ePlanktotrix\u003c/em\u003e sp. 1: Pl1, \u003cem\u003ePlanktolyngbya limnetica\u003c/em\u003e: Pl, \u003cem\u003eAnabaena planctonica\u003c/em\u003e: Ap, \u003cem\u003ePeridinium\u003c/em\u003e sp. 1: Per1,\u003cem\u003e Ceratium\u003c/em\u003e sp.1: Cer1, and \u003cem\u003eAulacoseira granulate\u003c/em\u003e: Aug. A blue circle indicates a positive correlation, and a red circle indicates a negative correlation. The box denotes significance at p\u0026lt;0.05.\u003c/p\u003e","description":"","filename":"Fig8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4488037/v1/c6d006a09d7db1487d6227ce.jpg"},{"id":93955818,"identity":"ff048887-2cd5-4561-b54e-b88b2ee560f8","added_by":"auto","created_at":"2025-10-20 16:03:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2774548,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4488037/v1/9dfd4bb7-d2da-4d2a-a9a4-c029eccc431c.pdf"},{"id":60519019,"identity":"e2871112-3a87-4f93-91ba-3df5357b57bb","added_by":"auto","created_at":"2024-07-17 16:04:36","extension":"docx","order_by":14,"title":"","display":"","copyAsset":false,"role":"supplement","size":271532,"visible":true,"origin":"","legend":"","description":"","filename":"28May24SUPPLEMENTARYINFORMATION.docx","url":"https://assets-eu.researchsquare.com/files/rs-4488037/v1/2e168f90e6ba7219e1187935.docx"}],"financialInterests":"","formattedTitle":"Phytoplankton community response to water physicochemical characteristics under seasonal variation at the Ubol Ratana Dam, Khon Kaen, Thailand","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWater resources are vital for all living organisms, but only 0.3% of freshwater on Earth is usable (Kılı\u0026ccedil;, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Freshwater is commonly stored in dams or reservoirs and serves various purposes in agriculture, industries, households, and flood regulation (Soomro et al. 2023). Freshwater dams show variations in physicochemical characteristics based on hydrological cycles and water flow characteristics, which impact the diversity of organisms in freshwater ecosystems, including phytoplankton (Graco-Roza et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Furthermore, land-based pollution from industrial, agricultural, and urban areas is increasingly being discharged into freshwater reservoirs, resulting in decreased biodiversity in reservoirs (Wang et al. \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The proliferation of organisms is typically caused by natural events and human activities, including water contamination with nutrients from riverine inflow, leaching, rainfall, aquaculture, and household, industrial, and agricultural activities (Bassem et al. 2020; Ponnusamy et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Sidabutar et al. 2020; Wang et al. \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These factors lead to changes in the main physicochemical characteristics of water bodies, resulting in plankton proliferation.\u003c/p\u003e \u003cp\u003ePhytoplankton play a crucial role as the primary aquatic producers in freshwater ecosystems by providing essential nutrients and energy in these systems (He et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Significant changes in physicochemical features have an impact on the phytoplankton community. Therefore, the local environment and water quality in freshwater ecosystems can lead to changes in phytoplankton communities and diversity (Yuan et al., \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The main physicochemical factors influencing dynamic phytoplankton communities are light, temperature, pH, and nutrient concentration (Akter et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Crevecoeur et al. 2023; Sun et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Yang et al. \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In addition, phytoplankton growth can increase with extended daylight periods, and this factor also interacts with temperature (Crevecoeur et al. 2023). Rising water temperatures affect the activity of phytoplankton (Sun et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Global warming causes irregular effects on the adaptation of phytoplankton, leading to harmful algal blooms (HABs) in tropical regions due to seasonal temperature fluctuations. Studies have shown that temperature can have both positive and negative effects on the proliferation of specific species. For example, high temperatures can increase the abundance of dominant species while reducing the abundance of others under the same conditions (Rasconi et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Suresh Kumar and Thomas, 2019). Additionally, a more alkaline pH has been found to increase cyanobacterial photosynthesis and reproduction (Wei et al., \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Phytoplankton development depends on essential nutrients. The main nutrients that limit phytoplankton blooms are nitrogen and phosphorus, which exist in the form of ammonium nitrate, nitrite, and orthophosphate (Passos et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Like other phytoplankton species, Cyanobacteria tend to dominate in environments with high nutrient contents, temperatures, and light intensities. Therefore, both short- and long-term environmental changes, including seasonal variations, strongly influence the spatial and temporal variability of phytoplankton distributions. Several studies have reported on these interactions (Crevecoeur et al. 2023; Parakkandi et al. 2021; Ponnusamy et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Srichandan et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; SureshKumar and Thomas \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Yang et al. \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCyanobacterial blooms, also known as HABs, release cyanotoxins into freshwater reservoirs, posing a risk to aquatic life and public health (Kong et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Passos et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Sun et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These blooms are capable of generating cyanotoxins such as microcystin, saxitoxin, and cylindrospermopsin, which can have severe effects on organisms, including respiratory failure, hypovolemic shock, and intrahepatic hemorrhage (Barros et al. 2019; Christophoridis et al. 2018; Passos et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Tito et al. \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The levels of some nutrients, such as total phosphorus, have been found to be positively correlated with the levels of cylindrospermopsin and microcystin (Barros et al. 2019). Therefore, understanding the link between physicochemical parameters and blooms is crucial for identifying key regulators that can help limit the distribution of HABs and toxins.\u003c/p\u003e \u003cp\u003eThe Ubol Ratana Dam is a large artificial freshwater reservoir in Khon Kaen Province, northeast Thailand. The dam was built for various purposes, including electricity generation, irrigation, flood relief, and fishery operation (Electricity Generating Authority of Thailand \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e; Sriworamas et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, prolonged droughts over several decades have led to long-term changes in the hydrological and meteorological conditions of the reservoir, exacerbated by an unpredictable climate. These changes impact water storage and may lead to sudden phytoplankton blooms (Muangsringam et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). There has been limited research on the physicochemical features, water management approaches, and phytoplankton communities in the Ubol Ratana Dam. In 2017\u0026ndash;2018, \u003cem\u003eMicrocystis\u003c/em\u003e significantly proliferated, while in 2019, \u003cem\u003eCylindrospermopsis\u003c/em\u003e became the dominant species (Mengchouy and Meksumpun \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Muangsrigam et al. 2019). Variations in the phytoplankton community composition and diversity in the reservoir with changes in water physicochemical parameters have not been well documented. Therefore, a comprehensive survey is necessary to address this knowledge gap. The aims of this study were to i) investigate and identify the seasonal variations in the phytoplankton community at different sites, ii) study the density and diversity of phytoplankton, and iii) investigate the relationships between physicochemical variables and dominant phytoplankton assemblages under seasonal variation. We hypothesize that understanding the physicochemical characteristics of water bodies in each location and season, which are important variables, will help to limit the recruitment of phytoplankton communities and allow for the efficient surveillance and monitoring of eutrophication risks.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy site\u003c/h2\u003e \u003cp\u003eThe Ubol Ratana Dam is located in the Ubol Ratana district of Khon Kaen Province, Thailand (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea, b). It is a rock-fill dam with a clay core. The dam has a crest length of 885 m and a crest width of 6 m. The dam has a maximum volume capacity of approximately 2,431\u0026nbsp;million m\u003csup\u003e3\u003c/sup\u003e, a maximum depth of 32 m, and a catchment area of 12,104 km\u003csup\u003e2\u003c/sup\u003e (Electricity Generating Authority of Thailand \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e). It was constructed at the confluence of three main rivers: the Phong River, Phaniang River, and Choen River (Ingthamjitr et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). In this research, water samples were collected from 6 different locations within the dam reservoir: water inlet 1 (from the Choen River) (S1; coordinates 16.6376630, 102.5619740), water inlet 2 (from the Phong River and Phaniang River) near the Non-Sawang, Tha Lat, and Pong Sang Districts (S2; coordinates 16.7948950, 102.4485980), the center of the dam (S3; coordinates 16.6927110, 102.5919860), the water outlet (S4; coordinates 16.7719530, 102.6194940), a fish farming site (S5; coordinates 16.6613810, 102.5597750) near the Nong Kung Soen Community, and Non-Sang Municipality Community (S6; coordinates 16.8227750, 1 02.5981860) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). The Phong River area has high turbidity due to high loads of suspended inorganic solids. Additionally, gillnet fishing and agricultural activities are carried out in the watershed using reservoir waters (Ingthamjitr et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Mengchouy and Meksumpun \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The reservoir is impacted by two monsoons: the northeast (NE) monsoon from November to February and the southwest (SW) monsoon from May to October. The average precipitation during the NE monsoon is 5\u0026ndash;10 mm, but in August and September, it exceeds 200 mm. The monthly average air temperature during the NE monsoon is 22.5\u0026deg;C, while during the SW monsoon, it is 26.3\u0026ndash;29.1\u0026deg;C. The surface water temperature ranges between 22 and 26\u0026deg;C in December and February and between 29 and 34\u0026deg;C from April to October (Ingthamjitr et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). According to a study conducted from March 2022 to January 2023, the water inflow volume increased from July to October, reaching its peak in mid-October. In terms of season, water storage varies between 1086.80 and 1495.11\u0026nbsp;million cubic meters (MCMs) during the summer (March to May), 862.86 and 3188.15 MCMs during the rainy season (June to October), and 1532.95 and 2855.43 MCMs during the winter (November to February 2023) (Supplementary information) (Electricity Generating Authority of Thailand \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSampling methodology and water physicochemical parameters\u003c/h2\u003e \u003cp\u003eWater samples were collected at the reservoir surface (0.5-meter depth) in three replicates, and the average value was calculated for the number of phytoplankton, cyanobacteria, and physicochemical characteristics collected in three seasons at the study site in Thailand. Sampling was performed twice per season, including in summer, the rainy season, and winter, between March 2022 and January 2023, from 9:00 a.m. to 4:00 p.m. The months in which samples were collected were March and April, August and September, December and January, representing the summer, rainy, and winter seasons of Thailand, respectively.\u003c/p\u003e \u003cp\u003eThe water temperature (\u0026deg;C), pH, and electrical conductivity (EC) (\u0026micro;s.cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) were measured using a multiparameter digital water quality meter (YSI ProDSS, USA). The water was analyzed to determine its ammonium-nitrogen and nitrate contents to infer the dissolved inorganic nitrogen content. The amount of ammonium-nitrogen was determined using the standard method for the examination of water and wastewater, APHA, 23rd ED., 2017, Part 4500- NH\u003csub\u003e3\u003c/sub\u003e, while the amount of nitrate-nitrogen was determined using the standard method for the examination of water and wastewater, APHA, AWWA, WEF, 23rd ED., 2017, Part 4500-NO\u003csub\u003e3\u003c/sub\u003e-B (APHA, 2017), with limits of detection of 0.02 and 0.02 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively. In addition, total phosphorus and orthophosphate contents were determined using a DR 2700 Spectrophotometer (HACH, USA) with limits of detection of 0.02 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (Methods 4500-P B and 4500-P E) (APHA 2017).\u003c/p\u003e \u003cp\u003eThe water samples were frozen for toxin analyses. We used a microcystin-Adda ELISA kit and a cylindrospermopsin ELISA kit (from Abraxis, USA) to measure microcystin and cylindrospermopsin levels following the manufacturer's instructions. Optical density was measured at 450 nm, and toxin concentrations were determined based on a standard curve. The detection limits for microcystins and cylindrospermopsin were 0.10 and 0.040 \u0026micro;gL\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively. Each analysis was performed in triplicate.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003ePhytoplankton collection and taxonomic identification\u003c/h2\u003e \u003cp\u003eA water sample was collected from the surface water using a plankton net with a 30 \u0026micro;m mesh size. The sample was concentrated to a final volume of 50 ml from 100 liters of water. Three duplicate samples were taken from each location and preserved in polystyrene bottles with 2% Lugal's solution. The phytoplankton and cyanobacteria in the concentrated water were observed for classification at the genus and species levels using standard manuals, textbooks, and research articles. Observations were carried out under a light microscope (CX23 Olympus microscope), and species were counted using the Uterm\u0026ouml;hl method with an Olympus CK40 inverted phase microscope (Olympus, Tokyo, Japan) as per Edler and Elbr\u0026auml;chter (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe number of species for each phylum was determined and is displayed as a proportion of the total species richness. The phytoplankton species collected from each sampling location were input into the Jvenn website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://jvenn.toulouse.inrae.fr\u003c/span\u003e\u003cspan address=\"https://jvenn.toulouse.inrae.fr\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) for Venn diagram analysis. Diversity indices, including the Shannon‒Wiener index, Margalef's richness index, and evenness index, were determined for each season using the Past 4.03 program. Violin and box plots were then created. A comparison of the means of the diversity values between seasons was performed using Student's t test in STATISTIX 10 software at significance levels of P\u0026thinsp;\u0026lt;\u0026thinsp;0.01 and P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. A hierarchical clustering dendrogram was generated using the unweighted pair group method with arithmetic average (UPGMA) and a Bray‒Curtis similarity index for 25 dominant phytoplankton assemblages. Canonical correspondence analysis (CCA) was conducted to examine the relationships between the 25 dominant phytoplankton species and the physicochemical characteristics of the water bodies, including pH, temperature, electrical conductivity, and nitrate, ammonium, total phosphorus, and orthophosphate contents, throughout each season. Pearson's correlation coefficient between water physicochemical properties and the ten dominant phytoplankton species was analyzed at a significance level of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Clustering analysis, Pearson\u0026rsquo;s correlation, and CCA were carried out using Past 4.03 software.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eWater physicochemical parameters\u003c/h2\u003e \u003cp\u003eThe physicochemical properties of the water in the Ubol Ratana Dam were monitored at six locations from March 2022 to January 2023. The summary in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the seasonal variations in these properties. Throughout all seasons, the pH at each location ranged between 7.2 and 8.4. The water temperatures ranged from 25.4 to 29.1\u0026deg;C during the rainy and winter seasons but from 32.4 to 33.5\u0026deg;C in the summer. The concentrations of nitrate-nitrogen and ammonium-nitrogen varied across the locations. In the rainy season, the nitrate concentration ranged from 0.2 to 0.9 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e; in the winter, it ranged from 0.12 to 1.2 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e; and in the summer, it ranged from 0.11 to 1.3 L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. Similarly, the ammonium concentration ranged from 0.05 to 0.3 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in the rainy season, from 0.07 to 0.24 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in the winter, and from 0.05 to 0.21 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in the summer. The water samples collected from water inlet 1, water inlet 2, the fish aquaculture areas, and the water near the municipality community exhibited higher concentrations of nitrate and ammonium compared to those obtained from the center of the dam and the water outlet location. The fish aquaculture areas had greater concentrations of nitrate-nitrogen than did the other sites. In summer, the total phosphorus and orthophosphate contents ranged from 0.11 to 0.16 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and from 0.39 to 0.51 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively, which were greater than those in the other seasons. Therefore, both the total phosphorus and orthophosphate contents were highest in summer, followed by those in the winter and the rainy season, respectively. In addition, cyanobacterial microcystins were detected at concentrations ranging from 0.18 to 0.72 \u0026micro;g/L, while cylindrospermopsin was not detected in this study.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Seasonal variations in the physicochemical properties of the water from the Ubol Ratana Dam.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.61702127659574%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSite\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.21985815602837%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.865248226950355%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eS6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.61702127659574%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRainy\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003cp\u003eTemperature (\u0026deg;C)\u003c/p\u003e\n \u003cp\u003eEC (\u0026micro;S cm\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003cp\u003eNitrate (mg L\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003cp\u003eAmmonium (mg L\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003cp\u003eTotal phosphorus (mg L\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003cp\u003eOrthophosphate (mg L\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003cp\u003eMicrocystins (\u0026mu;g L\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003cp\u003eCylindrospermopsin (\u0026mu;g L\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e8.1\u003c/p\u003e\n \u003cp\u003e28.7\u003c/p\u003e\n \u003cp\u003e245\u003c/p\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e8.2\u003c/p\u003e\n \u003cp\u003e28.4\u003c/p\u003e\n \u003cp\u003e237\u003c/p\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e8.4\u003c/p\u003e\n \u003cp\u003e29.1\u003c/p\u003e\n \u003cp\u003e185\u003c/p\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.21985815602837%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e8.1\u003c/p\u003e\n \u003cp\u003e28.5\u003c/p\u003e\n \u003cp\u003e192\u003c/p\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.865248226950355%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e8.2\u003c/p\u003e\n \u003cp\u003e28.4\u003c/p\u003e\n \u003cp\u003e242\u003c/p\u003e\n \u003cp\u003e1.20\u003c/p\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e7.9\u003c/p\u003e\n \u003cp\u003e28.2\u003c/p\u003e\n \u003cp\u003e252\u003c/p\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.61702127659574%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWinter\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003cp\u003eTemperature (\u0026deg;C)\u003c/p\u003e\n \u003cp\u003eEC (\u0026micro;S cm\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003cp\u003eNitrate (mg L\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003cp\u003eAmmonium (mg L\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003cp\u003eTotal phosphorus (mg L\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003cp\u003eOrthophosphate (mg L\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003cp\u003eMicrocystins (\u0026mu;g L\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003cp\u003eCylindrospermopsin (\u0026mu;g L\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e7.3\u003c/p\u003e\n \u003cp\u003e25.8\u003c/p\u003e\n \u003cp\u003e215\u003c/p\u003e\n \u003cp\u003e1.20\u003c/p\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e7.2\u003c/p\u003e\n \u003cp\u003e26.1\u003c/p\u003e\n \u003cp\u003e226\u003c/p\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e7.4\u003c/p\u003e\n \u003cp\u003e25.9\u003c/p\u003e\n \u003cp\u003e194\u003c/p\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.21985815602837%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e7.3\u003c/p\u003e\n \u003cp\u003e26.2\u003c/p\u003e\n \u003cp\u003e186\u003c/p\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.865248226950355%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e7.6\u003c/p\u003e\n \u003cp\u003e25.4\u003c/p\u003e\n \u003cp\u003e229\u003c/p\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e7.4\u003c/p\u003e\n \u003cp\u003e25.8\u003c/p\u003e\n \u003cp\u003e236\u003c/p\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.61702127659574%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSummer\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003cp\u003eTemperature (\u0026deg;C)\u003c/p\u003e\n \u003cp\u003eEC (\u0026micro;S cm\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003cp\u003eNitrate (mg L\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003cp\u003eAmmonium (mg L\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003cp\u003eTotal phosphorus (mg L\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003cp\u003eOrthophosphate (mg L\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003cp\u003eMicrocystins (\u0026mu;g L\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003cp\u003eCylindrospermopsin (\u0026mu;g L\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e7.8\u003c/p\u003e\n \u003cp\u003e32.4\u003c/p\u003e\n \u003cp\u003e186\u003c/p\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e7.4\u003c/p\u003e\n \u003cp\u003e32.7\u003c/p\u003e\n \u003cp\u003e196\u003c/p\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e7.6\u003c/p\u003e\n \u003cp\u003e32.6\u003c/p\u003e\n \u003cp\u003e174\u003c/p\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.21985815602837%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e7.8\u003c/p\u003e\n \u003cp\u003e33.1\u003c/p\u003e\n \u003cp\u003e164\u003c/p\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.865248226950355%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e7.4\u003c/p\u003e\n \u003cp\u003e33.5\u003c/p\u003e\n \u003cp\u003e195\u003c/p\u003e\n \u003cp\u003e1.30\u003c/p\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e7.5\u003c/p\u003e\n \u003cp\u003e33.1\u003c/p\u003e\n \u003cp\u003e186\u003c/p\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003cp\u003eND\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eND: Not detected\u003c/p\u003e\n\u003cp\u003eThe sites for collection included S1, water inlet 1; S2, water inlet 2; S3, the Center of the Dam; S4, the water outlet; S5, fish farming; and S6, the Non-Sang Municipality Community.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003ePhytoplankton community composition and cell density\u003c/h2\u003e \u003cp\u003eThrough microscopic observation, a total of 84 phytoplankton species, representing 8 phyla, were identified from water samples collected from 6 locations in the Ubol Ratana Dam during the rainy, winter, and summer seasons of 2022\u0026ndash;2023. The phytoplankton community had the following species richness in each phylum: Chlorophyta had the most species (34.51%), followed by Cyanophyta (23.81%), Euglenophyta (14.29%), Bacillariophyta (11.90%), Pyrrhophyta (7.14%), Chrysophyta (5.95%), Cryptophyta (1.19%), and Xanthophyta (1.19%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e provides a summary of all the phytoplankton species present. Cyanobacteria (Cyanophyta) had the highest cell density, accounting for approximately 84.06% of the total cell density (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). The following taxa dominated: Cyanobacterium \u003cem\u003eMicrocystis aeruginosa\u003c/em\u003e K\u0026uuml;tzing was the most abundant at 55.87% (114\u0026times;10\u003csup\u003e4\u003c/sup\u003e cells L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), followed by \u003cem\u003ePlanktothrix\u003c/em\u003e sp. at 7.79% (15.9 \u0026times;10\u003csup\u003e4\u003c/sup\u003e cells L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and \u003cem\u003eCylindrospermopsis raciborskii\u003c/em\u003e (Woloszynska) Seenaya et Subba Raju at 6.09% (12.4 \u0026times;10\u003csup\u003e4\u003c/sup\u003e cells L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). The remaining taxa included \u003cem\u003eMicrocystis wesenbergii\u003c/em\u003e, \u003cem\u003eMicrocystis viridis\u003c/em\u003e, \u003cem\u003eRaphiopsis raciborskii\u003c/em\u003e, \u003cem\u003ePlanktolyngbya limnetica\u003c/em\u003e, \u003cem\u003ePseudanabaena limnetica\u003c/em\u003e, \u003cem\u003ePseudanabaena mucicola\u003c/em\u003e, \u003cem\u003eMerismopedia punctata\u003c/em\u003e, and \u003cem\u003eChroococcus\u003c/em\u003e sp.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePhytoplankton species derived from the Ubol Ratana Dam.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhylum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhytoplankton species\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCyanophyta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMicrocystis aeruginosa\u003c/em\u003e (55.87), \u003cem\u003eMicrocystis wesenbergii\u003c/em\u003e (5.06), \u003cem\u003eMicrocystis viridis\u003c/em\u003e (0.56), \u003cem\u003eRaphiopsis raciborskii\u003c/em\u003e (1.66), \u003cem\u003eCylindrospermopsis raciborskii\u003c/em\u003e (6.09), \u003cem\u003eCylindrospermopsis philippinensis\u003c/em\u003e, \u003cem\u003ePlanktothrix\u003c/em\u003e sp. 1 (7.79), \u003cem\u003ePlanktotrix\u003c/em\u003e sp. 2, \u003cem\u003ePlanktolyngbya limnetica\u003c/em\u003e (3.33), \u003cem\u003ePseudanabaena limnetica\u003c/em\u003e (0.65), \u003cem\u003ePseudanabaena mucicola\u003c/em\u003e, \u003cem\u003eAphanizomenon\u003c/em\u003e sp., \u003cem\u003eMerismopedia\u003c/em\u003e sp.1, \u003cem\u003eMerismopedia punctata\u003c/em\u003e (0.22), \u003cem\u003eChroococcus\u003c/em\u003e sp. (0.37), \u003cem\u003eOscillatoria\u003c/em\u003e sp.1 (0.26), \u003cem\u003eOscillatoria\u003c/em\u003e sp. 2 (0.27), \u003cem\u003eSynechocystis\u003c/em\u003e sp., \u003cem\u003eAnabaena planctonica\u003c/em\u003e (1.19), \u003cem\u003eAnabaena\u003c/em\u003e sp.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChlorophyta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePediastrum\u003c/em\u003e sp.1, \u003cem\u003ePediastrum duplex\u003c/em\u003e, \u003cem\u003ePediastrum simplex\u003c/em\u003e, \u003cem\u003eMonoraphidium contortum\u003c/em\u003e (0.52), \u003cem\u003eMonoraphidium irregulare\u003c/em\u003e, \u003cem\u003eCosmarium\u003c/em\u003e sp. 1, \u003cem\u003eCosmarium\u003c/em\u003e sp. 2\u003c/p\u003e \u003cp\u003e\u003cem\u003eScenedesmus\u003c/em\u003e sp., \u003cem\u003eStaurastrum\u003c/em\u003e sp., \u003cem\u003eStaurastrum gracile\u003c/em\u003e, \u003cem\u003eCoelastrum\u003c/em\u003e sp., \u003cem\u003eCrucigeniella\u003c/em\u003e sp., \u003cem\u003eTetraedron\u003c/em\u003e sp., \u003cem\u003eCoelastrum\u003c/em\u003e sp., \u003cem\u003eCoelastrum microporum\u003c/em\u003e, \u003cem\u003eCoelastrum polychordum\u003c/em\u003e, \u003cem\u003eSphaerocystis\u003c/em\u003e sp., \u003cem\u003eSelenastrum\u003c/em\u003e sp., \u003cem\u003eStaurodesmus curvatus\u003c/em\u003e, \u003cem\u003eDesmidium\u003c/em\u003e sp., \u003cem\u003eDidymocystis\u003c/em\u003e sp., \u003cem\u003eElakatotrix\u003c/em\u003e sp., \u003cem\u003eEudorina\u003c/em\u003e sp. (0.22), \u003cem\u003eRadiococcus\u003c/em\u003e sp., \u003cem\u003eAnkistrodesmus\u003c/em\u003e sp., \u003cem\u003eBotryococcus braunii\u003c/em\u003e (0.28), \u003cem\u003eDictyosphaerium\u003c/em\u003e sp., \u003cem\u003eDictyosphaerium tetrachotomum\u003c/em\u003e, \u003cem\u003eTetrastrum heterocanthum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEuglenophyta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eEuglena\u003c/em\u003e sp.1 (0.99), \u003cem\u003eEuglena\u003c/em\u003e sp.2 (0.57), \u003cem\u003eEuglena gracilis\u003c/em\u003e, \u003cem\u003ePhacus helicoides\u003c/em\u003e (0.48), \u003cem\u003ePhacus longicauda\u003c/em\u003e, \u003cem\u003ePhacus\u003c/em\u003e sp.1, \u003cem\u003ePhacus\u003c/em\u003e sp.2, \u003cem\u003eTrachelomonas\u003c/em\u003e sp.1 (0.67), \u003cem\u003eTrachelomonas\u003c/em\u003e sp.2, \u003cem\u003eTrachelomonas hispida\u003c/em\u003e, \u003cem\u003eTrachelomonas volvocinopsis\u003c/em\u003e, \u003cem\u003eStrombomonas\u003c/em\u003e sp.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePyrrhophyta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePeridinium\u003c/em\u003e sp. 1 (2.45), \u003cem\u003ePeridinium\u003c/em\u003e sp. 2, \u003cem\u003ePeridinium\u003c/em\u003e sp. 3, \u003cem\u003eCeratium\u003c/em\u003e sp.1 (2.25), \u003cem\u003eCeratium\u003c/em\u003e sp.2, \u003cem\u003eCeratium hirudiella\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChrysophyta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eDinobryon\u003c/em\u003e sp., \u003cem\u003eDinobryon sertularia, Centritractus\u003c/em\u003e sp., \u003cem\u003eMallomonas\u003c/em\u003e sp., \u003cem\u003eMallomonas splendens\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eFragilaria\u003c/em\u003e sp., \u003cem\u003eFragilaria ulna, Rhizosolenia\u003c/em\u003e sp., \u003cem\u003eNavicula\u003c/em\u003e sp., \u003cem\u003eAchnanthidium\u003c/em\u003e sp., \u003cem\u003eAulacoseira granulate\u003c/em\u003e (1.78), \u003cem\u003eNitzschia\u003c/em\u003e sp., \u003cem\u003eCyclotella\u003c/em\u003e sp., \u003cem\u003eCymbella\u003c/em\u003e sp. (0.41), \u003cem\u003eSynedra ulna\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCryptophyta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCryptomonas\u003c/em\u003e sp. (0.94)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXanthophyta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eIsthmochloron\u003c/em\u003e sp.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eThe percent (%) abundance of dominant phytoplankton species is indicated in parentheses.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eSeasonal variation in phytoplankton diversity\u003c/h2\u003e \u003cp\u003eVenn diagrams, with colored areas representing phytoplankton abundance across the six locations in each season, are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea-c. The overlapping areas of the diagram representing the rainy, winter, and summer seasons included a total of 10, 8, and 8 species, respectively, indicating the presence of phytoplankton communities at the 6 sampling locations. In the rainy season, \u003cem\u003eM. aeruginosa\u003c/em\u003e, \u003cem\u003eM. wesenbergii\u003c/em\u003e, \u003cem\u003eC. raciborskii\u003c/em\u003e, \u003cem\u003ePlanktotrix\u003c/em\u003e sp. 1, \u003cem\u003ePlanktolyngbya limnetica\u003c/em\u003e, \u003cem\u003eAnabaena planctonica\u003c/em\u003e, \u003cem\u003ePeridinium\u003c/em\u003e sp. 1, \u003cem\u003ePeridinium\u003c/em\u003e sp. 2, \u003cem\u003ePeridinium\u003c/em\u003e sp. 3, and \u003cem\u003eCeratium\u003c/em\u003e sp.1 were found. \u003cem\u003eM. aeruginosa\u003c/em\u003e, \u003cem\u003eM. wesenbergii\u003c/em\u003e, \u003cem\u003eC. raciborskii\u003c/em\u003e, \u003cem\u003ePlanktotrix\u003c/em\u003e sp. 1, \u003cem\u003eP. limnetica\u003c/em\u003e, \u003cem\u003eOscillatoria\u003c/em\u003e sp. 2, \u003cem\u003ePeridinium\u003c/em\u003e sp. 1, and \u003cem\u003eCeratium\u003c/em\u003e sp. 1 were detected in the winter season. In summer, the 8 phytoplankton species present were \u003cem\u003eM. aeruginosa\u003c/em\u003e, \u003cem\u003eM. wesenbergii\u003c/em\u003e, \u003cem\u003eC. raciborskii\u003c/em\u003e, \u003cem\u003ePlanktotrix\u003c/em\u003e sp. 1, \u003cem\u003eOscillatoria\u003c/em\u003e sp. 1, \u003cem\u003eA. planctonica\u003c/em\u003e, \u003cem\u003ePeridinium\u003c/em\u003e sp. 1, and \u003cem\u003ePeridinium\u003c/em\u003e sp. 2. The diagrams show that the five taxa shared across all six locations in all seasons included \u003cem\u003eM. aeruginosa\u003c/em\u003e, \u003cem\u003eM. wesenbergii\u003c/em\u003e, \u003cem\u003eC. raciborskii, Planktotrix sp\u003c/em\u003e., and \u003cem\u003ePeridinium\u003c/em\u003e sp.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eProportion of phytoplankton at different sites\u003c/h2\u003e \u003cp\u003eThe spatial and temporal variations in the cell abundance and percentage total abundance of phytoplankton are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea, b. The phytoplankton density ranged from 1.99 to 8.10\u0026#128936;10\u003csup\u003e4\u003c/sup\u003e cells L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e during the rainy season and from 5.91 to 17.67\u0026#128936;10\u003csup\u003e4\u003c/sup\u003e cells L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e during the winter (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). The cell density was highest in summer, ranging from 11.77 to 28.86\u0026#128936;10\u003csup\u003e4\u003c/sup\u003e cells L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, and the community was mainly composed of cyanobacteria. Based on spatial analysis, it was found that the center of the dam (S3) and the water outlet (S4) consistently had the highest levels of total phytoplankton and cyanobacteria across all seasons. Cyanobacteria were most prevalent, making up approximately 60.42\u0026ndash;91.82% of the total abundance in the study area (see Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). In particular, areas S3 and S4 had the highest relative abundance of cyanobacteria, exceeding 80%. Notably, the abundance of Euglenophyta in these areas was very low, at 0.05% or less. On the other hand, in the Non-Sang Municipality Community (S6), there was a noticeable increase in the proportion of Euglenophyta, ranging from 12.85\u0026ndash;17.85%.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSeasonal variation in phytoplankton diversity\u003c/h2\u003e \u003cp\u003eIn this study, we measured the abundance of phytoplankton in three different seasons. The highest total phytoplankton cell density was recorded in summer at 114.25 \u0026#128936;10\u003csup\u003e4\u003c/sup\u003e cells L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). The greatest proportion of cyanobacterial biomass was observed in summer at 86.23%, followed by that in winter at 81.89% and in the rainy season at 79.94%. The relative abundance of Cyanophyta increased, while the abundances of other phytoplankton phyla, such as Chlorophyta, Euglenophyta, Pyrrophyta, and Chrysophyta, decreased. In terms of diversity, the Shannon‒Wiener index revealed statistically significant differences between the rainy and winter seasons (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), between the winter and summer seasons (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and between the rainy and summer seasons (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). The index value was 1.92 for summer, which was higher than that for the other seasons. None of the pairwise comparisons indicated a significant difference in species richness across seasons in this study (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec). The evenness indices also differed significantly (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) among the different seasons (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAssemblage of 25 dominant phytoplankton species\u003c/h2\u003e \u003cp\u003eCluster analysis revealed two main clusters derived from 25 predominant phytoplankton species (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Cluster I was further divided into two subclusters, with each minor cluster containing five species with a similarity of at least 20%. Within main cluster II, there were three related clusters: the first contained three species (\u003cem\u003eM. viridis\u003c/em\u003e, \u003cem\u003eOscillatoria\u003c/em\u003e sp. 2, and \u003cem\u003eR. raciborskii\u003c/em\u003e \u0026ndash; Mv, Os2, Rr); the second contained seven species (\u003cem\u003eP. limnetica\u003c/em\u003e, \u003cem\u003eA. planctonica\u003c/em\u003e, \u003cem\u003eM. punctata\u003c/em\u003e, \u003cem\u003eChroococcus\u003c/em\u003e sp., \u003cem\u003eOscillatoria\u003c/em\u003e sp. 1, \u003cem\u003eM. contortum\u003c/em\u003e and \u003cem\u003eBotryococcus braunii\u003c/em\u003e \u0026ndash; PsI, Ap, Mp, Ch, Os1, Mc, Bb); and the third contained three species (\u003cem\u003eEudorina\u003c/em\u003e sp., \u003cem\u003eCryptomonas\u003c/em\u003e sp., and \u003cem\u003eP. helicoides\u003c/em\u003e \u0026ndash; Eu, Cym, Ph) with a similarity of more than 30%. However, two phytoplankton species, \u003cem\u003eP. limnetica\u003c/em\u003e and \u003cem\u003eM. aeruginosa\u003c/em\u003e, appeared to be separate from the main group.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eCanonical correspondence analysis\u003c/h2\u003e \u003cp\u003eIn our study, we used canonical correspondence analysis (CCA) to examine the relationships between various physicochemical variables (represented by vector lines), including pH, water temperature, EC, nitrate, ammonium, total phosphorus, and orthophosphate content, and the 25 dominant phytoplankton species (depicted as blue dots) across different seasons. During the rainy period, the eigenvalues of the first two axes, 1 and 2, were 0.654 and 0.536, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea). These two axes represented 66.8% of the total variation. The data indicated that \u003cem\u003eC. raciborskii\u003c/em\u003e and \u003cem\u003eA. granulate\u003c/em\u003e were influenced by ammonium and nitrate levels, as well as by electrical conductivity (EC), which were negatively correlated with water pH and temperature. Several phytoplankton species, including \u003cem\u003eChroococcus\u003c/em\u003e sp., \u003cem\u003eB. braunii\u003c/em\u003e, \u003cem\u003eM. punctata\u003c/em\u003e, \u003cem\u003eP. limnetica\u003c/em\u003e, \u003cem\u003eM. contortum\u003c/em\u003e, \u003cem\u003ePlanktotrix\u003c/em\u003e sp. 1, \u003cem\u003eEudorina\u003c/em\u003e sp., \u003cem\u003eOscillatoria\u003c/em\u003e sp. 1, \u003cem\u003eR. raciborskii\u003c/em\u003e, and \u003cem\u003ePeridinium\u003c/em\u003e sp. 1, were positively correlated with water pH and temperature, while \u003cem\u003eM. aeruginosa\u003c/em\u003e was positively associated with total phosphorus content. In the winter season, the first two axes represented 70.6% of the total variation (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe CCA data also indicated that the total phosphorus content and pH were positively correlated with \u003cem\u003eM. aeruginosa\u003c/em\u003e. \u003cem\u003eA. granulate\u003c/em\u003e, \u003cem\u003eCryptomonas\u003c/em\u003e sp., and \u003cem\u003eEuglena\u003c/em\u003e sp. 2 exhibited positive correlations with EC and ammonium and nitrate contents, which are presented on the right side of the triplot. Several phytoplankton species were also negatively affected by ammonium and nitrate contents and EC. In summer, 63.8% of the total variance was explained by axes 1 (0.675) and 2 (0.572), and the total eigenvalue was 1.247 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ec). In addition, \u003cem\u003eC. raciborskii\u003c/em\u003e, \u003cem\u003eM. viridis\u003c/em\u003e, \u003cem\u003eOscillatoria\u003c/em\u003e sp. 1, \u003cem\u003eEuglena\u003c/em\u003e sp. 1, \u003cem\u003eOscillatoria\u003c/em\u003e sp. 2, \u003cem\u003eTrachelomonas\u003c/em\u003e sp. 1, \u003cem\u003eCryptomonas\u003c/em\u003e sp., \u003cem\u003eP. helicoides\u003c/em\u003e, \u003cem\u003eA. granulate\u003c/em\u003e, and \u003cem\u003eEuglena\u003c/em\u003e sp. 2 were negatively correlated with EC, temperature, and nitrate and ammonium contents in the water. The abundance of \u003cem\u003eM. aeruginosa\u003c/em\u003e was correlated with orthophosphate and pH and was affected by temperature, nitrate-ammonium content, and EC. The nitrate and ammonium contents, temperature, and EC were also crucial factors affecting phytoplankton in all seasons.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation analysis\u003c/h2\u003e \u003cp\u003eThe influence of water physicochemical parameters such as pH, temperature, EC, and nitrate, ammonium, total phosphorus, and orthophosphate contents on the growth of dominant phytoplankton was analyzed, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. Pearson\u0026rsquo;s correlation analysis indicated a significant difference (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The growth of \u003cem\u003eC. raciborskii\u003c/em\u003e had a strong positive correlation with temperature (r\u0026thinsp;=\u0026thinsp;0.85) and orthophosphate content (r\u0026thinsp;=\u0026thinsp;0.84). The abundance of \u003cem\u003eM. aeruginosa\u003c/em\u003e was significantly positively correlated with orthophosphate content (r\u0026thinsp;=\u0026thinsp;0.75) but negatively correlated with EC (r = -0.79), nitrate content (r= -0.51), and ammonium content (r = -0.59) and showed a slight correlation with temperature.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, the water physicochemical parameters and phytoplankton community composition of the Ubol Ratana Dam were investigated to assess their relationships with water contaminants. The levels of nutrients, particularly nitrogen and phosphorus, were examined because they can lead to eutrophication and changes in the phytoplankton community. This study also highlights the challenges associated with assessing the diversity and community composition of phytoplankton and the consequences of temporal and seasonal variations in physical and chemical parameters. Overall, the results of this study revealed a relationship between water physicochemical properties, seasonal changes, site location, and phytoplankton and cyanobacteria biodiversity in the Ubol Ratana Dam from 2022\u0026ndash;2023.\u003c/p\u003e \u003cp\u003eWe identified 84 species of phytoplankton belonging to 8 phyla: Bacillariophyta, Chlorophyta, Cyanophyta, Euglenophyta, Pyrrhophyta, Chrysophyta, Cryptophyta, and Xanthophyta. In a previous study in 2018 by Muangsringam et al., six of these phyla were reported in the same reservoir: Bacillariophyta, Chlorophyta, Cyanophyta, Pyrrhophyta, Cryptophyta, and Euglenophyta. We identified all the phytoplankton at the species level, and \u003cem\u003eM. aeruginosa\u003c/em\u003e was found to be the dominant species in the reservoir, consistent with a previous study by Muangsringam et al. (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Cyanobacteria frequently accounted for over 80% of the total phytoplankton. Several potentially toxic genera, such as \u003cem\u003eMicrocystis\u003c/em\u003e, \u003cem\u003eCylindrospermopsis\u003c/em\u003e, \u003cem\u003ePlanktotrix\u003c/em\u003e, \u003cem\u003ePlanktolyngbya\u003c/em\u003e, and \u003cem\u003eAnabaena\u003c/em\u003e, were dominant throughout our study period (Barros et al. 2019; Crevecoeur et al. 2023; Mengchouy and Meksumpun \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Muangsringam et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Passos et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Tito et al. \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Our findings suggest that phytoplankton respond favorably to a variety of nutrients depending on location and season. The abundance of cyanobacteria (Cyanophyta) and the density of phytoplankton varied across different geographical locations, and distinct spatial and temporal variations were observed across sampling sites despite their high relative abundance.\u003c/p\u003e \u003cp\u003eBiological indicators are often used to assess the pollution status of aquatic ecosystems (Gogoi et al. 2019). Both the evenness distribution and Shannon‒Wiener index exhibited similar trends, showing significant increases during the winter and summer seasons and reaching their lowest points during the rainy season. The findings of this study align with those of Sun et al. (\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and Arumugham et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The seasonal changes in diversity indicate that the alterations occurred due to shifts in the evenness and Shannon‒Wiener indices rather than due to changes in species richness (Stirling and Wilsey, 2001; SureshKumar and Thomas, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The Shannon‒Wiener diversity index ranged from 1 to 2, indicating moderate diversity (Parakkandi et al. 2021; Wihm, 1975). Seasonal hydrological changes may have also impacted these indices. The irrigation systems result in low riverine inflow and stable water availability in the winter and summer periods and generate changes in phytoplankton diversity, with significant increases during the winter and summer seasons, and the lowest levels occurred during the rainy season. The findings of the present study are consistent with those of Sun et al. (\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and Arumugham et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The seasonal changes in diversity indicate that the alterations occurred due to shifts in the evenness and Shannon‒Wiener indices rather than due to changes in species richness (Stirling and Wilsey, 2001; SureshKumar and Thomas, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The Shannon‒Wiener diversity index ranged from 1 to 2, indicating moderate diversity (Parakkandi et al. 2021; Wihm, 1975). Seasonal hydrological changes may also affect these indices. The irrigation systems result in low riverine inflow and stable water availability in the winter and summer periods, resulting in changes in diversity.\u003c/p\u003e \u003cp\u003eThe ecology of phytoplankton in aquatic environments is affected by seasonal changes, leading to fluctuations in species diversity, evenness, the Shannon‒Wiener index, and cell density (Sun et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; SureshKumar et al. 2019). Temperature is a significant factor that regulates the growth of phytoplankton (Somdee et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). During the study, it was observed that species richness decreased in the summer. The results showed an overall increase in the total phytoplankton population in the summer months, with a specific increase in the proportion of Cyanophyta. High temperatures led to a decrease in the proportion of other phyla, with Chlorophyta, Euglenophyta, Pyrrophytophyta, and Bacillariophyta showing decreases in relative abundances. This result is consistent with the findings of Sun et al. (\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), who noted that Euglenophyta and Bacillariophyta are susceptible to increasing temperatures. These genera may be unable to grow at high temperatures due to their physiological incapacity to tolerate high temperatures, while cyanobacteria flourish and reproduce rapidly under such conditions. Increasing water temperatures induce competitive exclusion, resulting in the dominance of cyanobacteria and the disappearance of some taxa (Briddon et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Sun et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Cyanobacteria can impact phytoplankton communities by influencing planktonic species when the water temperature reaches a certain level. The impact of seasonal variation on cell density seemed to be greatest in the summer and lowest during the rainy season.\u003c/p\u003e \u003cp\u003eIt was challenging to determine the impact of temperature on phytoplankton abundance due to the influence of other factors, such as low water levels in summer, which led to increased nutrient content and phytoplankton biomass. However, the density of phytoplankton and cyanobacteria increased the most during the summer at all sites. This was evident from the positive correlation between water temperature and dominant phytoplankton, such as cyanobacteria. This finding is consistent with that of Kong et al. (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, it remains unclear whether phytoplankton prefer high temperatures (Wang et al. \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). It is possible that high temperatures do not lead to cyanobacterial blooms but rather intensify these phenomena by increasing thermal stratification and depth shifts through gas vesicles (Crevecoeur et al. 2023; Tito et al. \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Seasonal temperature significantly influences the regulation of phytoplankton reproduction, growth, and behavior. The growth of eutrophic phytoplankton is significantly affected by physicochemical factors, which cause fluctuations in phytoplankton biomass. These parameters change because of changes in weather-related processes. Phytoplankton can thrive well under a broad pH range of 6.5\u0026ndash;10 (Ballah et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; SureshKumar and Thomas \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Cyanobacteria, particularly \u003cem\u003eM\u003c/em\u003e. \u003cem\u003eaeruginosa\u003c/em\u003e, can utilize HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e and CO\u003csub\u003e2\u003c/sub\u003e for photosynthesis in alkaline water, which increases their density (Wei et al. \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). According to our findings, the pH during the rainy season was approximately 8, and the CCA pointed to pH as a major variable. The observed alkaline pH may be explained by water input from runoff from nearby agricultural land, which includes both organic and inorganic components that lead to the breakdown of organic waste (Gogoi et al. 2019). However, the overall quantity of phytoplankton was lower than that in the other seasons. We can speculate the causes of the low phytoplankton abundance observed. In this study, alkaline pH was linked to the growth of \u003cem\u003eM\u003c/em\u003e. \u003cem\u003eaeruginosa\u003c/em\u003e, but this value was not optimal (Wei et al. \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Moreover, sudden changes in hydrology, such as increased inflow and outflow of water from rainstorms, make these areas unsuitable for phytoplankton growth. Moreover, increased water storage contributes to nutrient dilution (Gogoi et al. 2019; Parakkandi et al. 2021). This causes changes in the physicochemical characteristics of water bodies.\u003c/p\u003e \u003cp\u003eIncreased levels of nutrients, particularly nitrogen and phosphorus, are essential for the growth, survival, and proliferation of phytoplankton, as they are necessary for phytoplankton metabolic processes (Arumugham et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Barros et al. 2019; Briddon et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Lv et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; SureshKumar and Thomas \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Higher levels of nitrate and ammonium were detected near the Non-Sang municipality community and fish farm locations throughout all seasons, which was caused by the use of organic and chemical fertilizers, excess feed, domestic wastewater, and anthropogenic activities, which contribute to the nutrient load in the inflowing water and in the water surrounding the reservoir (Akter et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; de Lima Pinheiro et al. 2023). Additionally, the changes observed with increased nitrogen content suggested that nitrates and ammonium strongly influence the growth of other phytoplankton, such as Euglenophytes. Taxa such as \u003cem\u003eEuglena\u003c/em\u003e sp., which thrive in nutrient-rich water, frequently serve as bioindicators of hypereutrophic water pollution (Parakkandi et al. 2021). Surprisingly, although the stagnant water at the water outflow and water in the center of the dam had the highest total phytoplankton and cyanobacteria abundances, both nitrate and ammonium levels remained low in these areas. However, several studies have shown that cyanobacteria, particularly the common species \u003cem\u003eM\u003c/em\u003e. \u003cem\u003eaeruginosa\u003c/em\u003e, typically thrive in environments rich in nitrate and ammonium, which promote biomass growth (Sun et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In contrast, a rapid utilization of nutrients may lead to decreases in nitrate and ammonium levels, generating a strong negative correlation. This finding is similar to that of Pitchaikani and Lipton (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Fluctuations in the levels of limiting nutrients such as ammonium and nitrate often regulate the growth of phytoplankton (Pitchaikani and Lipton \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The concentration of nitrogen sources decreases as nitrogen is rapidly consumed (SureshKumar and Thomas \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). It is also possible that the availability of nitrogen from nitrogen-fixing cyanobacteria is sufficient to support the growth of nonheterocystous phytoplankton (Lv et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). For example, the nitrogen-fixing cyanobacterium \u003cem\u003eC. raciborskii\u003c/em\u003e was present consistently throughout the seasons, along with \u003cem\u003eM. aeruginosa\u003c/em\u003e (Crevecoeur et al. 2023). In nitrogen-limited environments, \u003cem\u003eMicrocystis\u003c/em\u003e is more abundant than other cyanobacterial species due to its effective assimilation of regenerated ammonium (Flanzenbaum et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe concentration of phosphorus in the water bodies was low during the rainy season and increased during the summer. This finding was supported by a strong positive correlation between orthophosphate content and an increase in dominant phytoplankton and total biomass. Cyanobacteria rely more on phosphate as a nutrient than do other phytoplankton (Lv et al. (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). However, less dissolved phosphate may be utilized during the rainy season due to lower plankton density, which can be attributed to various factors. The reservoir water only drains in the winter season, and water evaporates in the summer, giving rise to greater nutrient concentrations. Several factors contribute to phosphorus accumulation, particularly in summer, including fertilizer inputs, decomposition of dead plants and animals, aquaculture, and residential activities such as detergent use and the discharge of domestic effluent (Ajayan et al. 2017; Kundu et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The nutrient supply for phytoplankton comes from both the water inflow and the nutrients generated within the reservoir from the degradation of organic matter by bacteria (Crevecoeur et al. 2023). Our results in this regard are inconclusive. The nutrient levels in the central dam area with high phytoplankton biomass in the summer were not significantly different from those at the other stations. However, it is possible that there was enough consumption of available phosphorus. Cyanobacteria can regulate phosphate absorption, consume it in subsequent life stages, and store in the form of polyphosphate because of their high growth rate and prolonged life span (Sanz-Luque et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; SureshKumar and Thomas \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). They exhibit phosphorus saturation when cultivated in phosphate-rich environments (Barros et al. 2019; Dolman et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). However, the significant occurrence of \u003cem\u003eMicrocystis\u003c/em\u003e blooms may be linked to high phosphorus concentrations since phosphorus is a limiting nutrient under low nitrogen‒phosphorus ratios (Barros et al. 2019; Jargal and An \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Lv et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Our findings indicate that phosphate is a significant factor affecting the increase in phytoplankton biomass in this reservoir. We believe that rather than phosphate content, nitrate and ammonium contents play key roles in influencing the biomass of phytoplankton and cyanobacteria.\u003c/p\u003e \u003cp\u003eThe CCA showed that the influencing factors varied with seasonal fluctuations. Potential factors are indicated by the length of the arrows in the CCA (Sharma et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Nitrate and ammonium levels significantly influence the composition of the phytoplankton community throughout the year, suggesting that these factors are not impacted by seasonal changes and indicating that nitrogen levels restrict phytoplankton growth in the reservoir. We observed that total phytoplankton, particularly cyanobacteria, more efficiently utilized nitrate, ammonium, and orthophosphate when the temperature was optimal. Cyanobacteria appear to adapt effectively to environmental changes. The abundance of phytoplankton can be influenced not only by individual factors but also by interactions among multiple crucial factors. CCA revealed a broad pattern in the physicochemical variables in the rainy season, which might be related to sudden hydrological changes that lead to changes in the concentrations of nutrients in the water bodies. Based on the above discussion, the water management measures taken in reservoirs during each season have a significant impact on water physicochemical changes and nutrient dynamics. These factors ultimately control the composition of phytoplankton (Kumar et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Malik and Rathi, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Muangsringam et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Seasonal variations in irrigation systems play an important role in the formation of unique phytoplankton communities. During the summer, when temperatures rise and the daily period of sunshine increases, a small amount of water flows through two gates at the confluence of the dam's outflow and center. This generates low agitation, low turbidity, stagnation, increased nutrients, and increased light availability at the epilimnion of the reservoir. These factors can positively impact and enhance the growth, photosynthesis, reproduction, and buoyancy of phytoplankton (Crevecoeur et al. 2023; Jargal and An \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; M\u0026acirc;noca and de Lima Isaac \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). A limitation of this study is that the physicochemical and biological parameters of the riverine water inflow that may influence phytoplankton population changes during seasonal variation were not investigated.\u003c/p\u003e \u003cp\u003eAlthough phytoplankton blooms are currently not a serious issue and the amount of microcystin does not exceed the standard values of the World Health Organization (WHO), which recommends a maximum level of 1 \u0026micro;g/L of microcystin per liter of water in this reservoir, it is important to monitor the water body as the concentration of nutrients such as nitrogen and phosphorus continues to increase. This can lead to a change in the phytoplankton community composition and to cyanobacterial blooms. These significant findings support the previous prediction that cyanobacterial proliferation occurred in the central dam zone during the summer.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this study, we investigated the physicochemical properties of the water and the phytoplankton community at six different locations at the Ubol Ratana Dam in Thailand during the years 2022\u0026ndash;2023. Throughout spatial and temporal studies of the reservoir, a total of 84 species of phytoplankton were identified and classified into 8 phyla. Cyanobacteria made up more than 80% of the total phytoplankton population in all sites and seasons, with the most prevalent species being \u003cem\u003eM\u003c/em\u003e. \u003cem\u003eaeruginosa\u003c/em\u003e, which was found at high densities. The Shannon‒Wiener index indicated that the phytoplankton diversity and evenness were highest in summer, indicating that it was the most favorable season for phytoplankton growth. Nitrate and ammonium levels were related to phosphate content and temperature across all seasons, as evidenced by the CCA and strong correlations. Our findings demonstrated that cyanobacterial blooms were influenced by the physicochemical properties of the water body, particularly due to seasonal variations. This suggests that irrigation systems significantly affect water bodies in different seasons, leading to changes in phytoplankton community composition and diversity. Therefore, this study provides important baseline information for further research on the assessment, forecasting, and surveillance of the distribution of phytoplankton in aquatic ecosystems.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003econtributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnchana Somdee:\u0026nbsp;Conceptualization, methodology,\u0026nbsp;formal analysis and investigation, and writing-original draft preparation.\u0026nbsp;Weeraput Butsat: Investigation.\u0026nbsp;Theerasak Somdee:\u0026nbsp;Conceptualization, methodology, proofreading, review, and\u0026nbsp;editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the Fundamental Fund of\u0026nbsp;Khon Kaen University, Thailand.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data in this research are available upon request from the corresponding author at [email protected]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAjayan AP, Ajit Kumar KG (2017) Phytoplankton as biomonitors: A study of Museum Lake in Government Botanical Garden and Museum, Thiruvananthapuram, Kerala India. 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J Clean Prod 294:126291. https://doi.org/10.1016/j.jclepro.2021.126291\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Phytoplankton community, Physicochemical factors, Nutrients, Phytoplankton blooms, Cyanobacteria, Seasonal dynamics, Reservoir","lastPublishedDoi":"10.21203/rs.3.rs-4488037/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4488037/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWater eutrophication is a significant environmental issue that impacts aquatic ecosystems worldwide. In this study, we aimed to investigate the relationships among the water physicochemical characteristics, phytoplankton community, and cyanotoxins in the Ubol Ratana Dam, Khon Kaen, Thailand, during the years 2022\u0026ndash;2023 and to evaluate the relationships between changes in water parameters and the dynamics of the phytoplankton community under seasonal variations in the Ubol Ratana Dam. A total of 84 phytoplankton species belonging to 8 phyla were found in six different locations in the reservoir. Cyanobacteria (Cyanophyta) had the highest cell density (84.06%), with \u003cem\u003eMicrocystis aeruginosa\u003c/em\u003e being the dominant species. In terms of seasonality, the diversity indices, including Shannon‒Wiener and evenness values, were highest during summer and lowest during the rainy season, while species richness remained constant. Interestingly, the phytoplankton density was greater at the center of the dam and water outlet stations than that at the other stations, and these two areas also had the lowest nitrate and ammonium concentrations. Canonical correspondence analysis (CCA) explained 66.8%, 70.6%, and 63.8% of the total variation in the rainy, winter, and summer seasons, respectively. This indicates that nitrate and ammonium were factors that influenced phytoplankton growth. Correlation analysis revealed that the abundance of dominant cyanobacteria was positively correlated with temperature and orthophosphate content and negatively correlated with nitrate and ammonium content. Overall, nitrogen concentration mainly governed cyanobacterial blooms. These results suggest that relationships between physicochemical factors and the phytoplankton community significantly influence the seasonal variations in phytoplankton, providing valuable insights for future monitoring of phytoplankton blooms.\u003c/p\u003e","manuscriptTitle":"Phytoplankton community response to water physicochemical characteristics under seasonal variation at the Ubol Ratana Dam, Khon Kaen, Thailand","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-17 16:04:31","doi":"10.21203/rs.3.rs-4488037/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major Revision","date":"2024-09-07T07:17:46+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2024-08-06T09:26:10+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-06-25T21:54:09+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Environmental Science and Pollution Research","date":"2024-06-13T16:27:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-05T04:14:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Science and Pollution Research","date":"2024-06-02T23:08:05+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"a02ea934-f2cf-42ac-a162-b80edbb486e6","owner":[],"postedDate":"July 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-20T15:59:11+00:00","versionOfRecord":{"articleIdentity":"rs-4488037","link":"https://doi.org/10.1007/s11356-025-37019-6","journal":{"identity":"environmental-science-and-pollution-research","isVorOnly":false,"title":"Environmental Science and Pollution Research"},"publishedOn":"2025-10-13 15:56:54","publishedOnDateReadable":"October 13th, 2025"},"versionCreatedAt":"2024-07-17 16:04:31","video":"","vorDoi":"10.1007/s11356-025-37019-6","vorDoiUrl":"https://doi.org/10.1007/s11356-025-37019-6","workflowStages":[]},"version":"v1","identity":"rs-4488037","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4488037","identity":"rs-4488037","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}