Relationship between water temperature and phytoplankton communities in Ba Lai river, Viet Nam

Science & Technology Development Journal, 23(2):536-547 Open Access Full Text Article Research Article 1Institute of Tropical Biology (ITB), Vietnam Academy of Science and Technology (VAST), 85 Tran Quoc Toan Street, District 3, Ho Chi Minh City, Vietnam 2Graduate University of Science and Technology (GUST), Vietnam Academy of Science and Technology (VAST), 18 Hoang Quoc Viet, Hanoi, Vietnam Correspondence Pham Thanh Luu, Institute of Tropical Biology (ITB), Vietnam Academy of Science and Technology (VAST), 85 Tran Quoc Toan Street, District 3, Ho Chi Minh City, Vietnam Graduate University of Science and Technology (GUST), Vietnam Academy of Science and Technology (VAST), 18 Hoang Quoc Viet, Hanoi, Vietnam Email: thanhluupham@gmail.com History  Received: 2020-02-11  Accepted: 2020-06-15  Published: 2020-06-30 DOI : 10.32508/stdj.v23i2.1755 Relationship between water temperature and phytoplankton communities in Ba Lai river, Viet Nam Tran Thi Hoang Yen1, Dinh LeMai Phuong2, Tran Thanh Thai1, Nguyen Thi My Yen1, Ngo Xuan Quang1,2, Pham Thanh Luu1,2,* Use your smartphone to scan this QR code and download this article ABSTRACT Introduction: Phytoplankton which can affect higher trophic levels play a pivotal role as primary producers. Phytoplankton structure and diversity may, besides other factors, be controlled by the changing of water temperatures. Hence, the present study aimed to determine some relationship between phytoplankton assemblage and water temperature in Ba Lai River, Vietnam. Methods: In this research, sample data along the Ba Lai Estuary during two surveys were carried out in rainy season (September, 2017) and in dry season (March, 2018), and analyzed by Spearman's correlation and Linear regression analysis to find the correlations. Results: The results showed that the temper- ature of surface water in Ba Lai River was quite stable spatially. A total of 128 species belonging to 5 groups- namely cyanobacteria, diatoms, green algae, euglenids and dinoflagellates- were recorded in which diatoms were clearly dominant. Additionally, multiple stepwise linear regression revealed that phytoplankton assemblage correlated significantly with the temperature of surface water. The water temperature had a significant positive correlation with chlorophyll-a concentration but was negatively correlated with Margalef's diversity index in the rainy season. Moreover, the significant negative association of water temperaturewith biomass of phytoplankton and biomass of diatoms, whichwas principal in species number, were determined in the dry season. Conclusion: This study investigated the phytoplankton communities and found their correlation with environment in the area, and demonstrated advantages of phytoplankton which warrant their further research. Keywords: Linear regression analysis, Phytoplankton assemblage, Spearman's correlation analysis, the water temperature INTRODUCTION Phytoplankton contribute to nearly half of the annual global primary production and are major drivers of biogeochemical cycling, thereby sustaining the food webs of most ecosystems; thus, they have garnered increased attention in research1. Phytoplankton are useful indicators of water quality because their spa- tial and temporal patterns reflect both short-and long- term environmental changes2. Therefore, changes in phytoplankton community structure and diversity can be evaluated to understand impact of environ- mental changes on ecosystems3. The favorable environmental parameters (e.g. pH, temperature, salinity, dissolved oxygen, and nutri- ents) in estuarine and coastal waters have been re- ported to affect and link with the diversity, distri- bution, abundance, and composition of phytoplank- ton4,5. Temperature is among the major factor to influence phytoplankton growth rates, nutrient stoi- chiometry, and spatial and temporal distribution in freshwater systems. The alteration of seasonal tem- perature and increasing water temperatures, in addi- tion to other parameters, may control phytoplankton growth and diversity. Additionally, biodiversity is de- creasing at a rapid rate and this decrease is further ac- celerated by artificial climate change and associated rising temperatures on a global scale 6. An increas- ing number of studies have focused on how phyto- plankton primary productivity and species composi- tion are influenced by water temperature, triggered by an interest to understand how global warming affects ecosystem processes and properties 7. The combined effects of temperature and diversity on phytoplank- ton growth have been emphasized in recent study of Schabhüttl et al. (2013)6. In this research, high tem- perature had a negative effect on phytoplankton di- versity, in that a temperature-dependent decrease in diversity was most obvious in communities adapted to cooler base temperatures. Moreover, green algae and diatoms have shown a trend to perform better at lower temperatures, while cyanobacteria have shown a stronger relationship with increasing temperatures in mixed communities6. Besides, the main objective of the study in Tagus Estuary (Portugal) was to iden- tify the key environmental factors affecting phyto- plankton structure. BIOENV analysis revealed that in Cite this article : Yen T T H, Phuong D L M, Thai T T, Yen N T M, Quang N X, Luu P T. Relationship be- tween water temperature and phytoplankton communities in Ba Lai river, Viet Nam. Sci. Tech. Dev. J.; 23(2):536-547. 536 Copyright © VNU-HCM Press. This is an open- access article distributed under the terms of the Creative Commons Attribution 4.0 International license. Science & Technology Development Journal, 23(2):536-547 study period, the water temperature and other factors were the variables that had the strongest correlations with the phytoplankton community along the Tagus Estuary and best explained the phytoplankton spa- tial pattern 8. The study in the Yangtze River (China) observed that water temperature, nutrient concentra- tions and light availabilitywere the driving factors that determine phytoplankton dynamics9. Ba Lai River is a river in the Mekong Delta region which flows through Ben Tre Province. It is a direct tributary of Tien River and the natural boundary be- tween Phu Duc and Tan Phu Communes. At the be- ginning of the 20th century, the river became alluvial, narrow and shallow. Nowadays, the water source of Ba Lai River comesmainly from theMyThoRiver. Ba Lai River has also begun to diminish while Ba Lai Es- tuary is filling with sediment and becoming blocked. In 2002, in the Ba Lai Estuary, the construction of the Ba Lai dam which functions to prevent salinity, create a fresh source of water, and supply water for the districts of Binh Dai, Ba Tri, Giong Trom, Chau Thanh in Ben Tre Town, has been considered as an environmental failure for the originally connected es- tuarine and river ecosystems10. Several studies have warned that the Ba Lai dam could result in a high dis- turbance and deposition of this river11. According to a recent study of phytoplankton community structure in Ba Lai River, it was revealed that the water qual- ity had shown signs of serious deterioration through biological indicators and there were other evidence which indicated that the Ba Lai dam negatively af- fected the phytoplankton communities. The phyto- plankton distribution was influenced by the environ- mental parameters12. Themain objectives of this paper were to characterize the influence of water temperature, one of the main environmental factors, on controlling the distribution pattern of phytoplankton communities and diversity in the Ba Lai River, therefore contributing to the as- sessment and monitoring of aquatic environments. MATERIALS ANDMETHODS Study area and field sampling Two surveys were conducted at 10 sites (represented by nomenclature BL1 to BL10) in the Ba Lai River in March of 2018 (dry season) and September of 2017 (rainy season) (Table 1, Figure 1). The water tem- perature was collected from the surface andmeasured in situ by using a multi-parameter system (Hach 156, Company, USA). Planktonic diatom samples were collected from the surface waters by towing a conical net made of bolt- ing silk with 25 mmmesh size. Subsequently, samples Table 1: Locations of sample sites Sites Sampling coordinates Latitude Longitude BL1 1017’29.8”N 10612’40.2”E BL2 1018’43.7”N 10617’38.5”E BL3 1017’38.8”N 10621’22.0”E BL4 1016’10.8”N 10626’24.5”E BL5 1012’30.6”N 10632’6.48”E BL6 1011’05.8”N 10634’35.8”E BL7 1010’17.7”N 10636’48.6”E BL8 1008’44.9”N 10638’01.1”E BL9 1008’17.1”N 10638’35.6”E BL10 1002’38.5”N 10641’03.7”E were kept in 150 ml plastic bottles, preserved in 4% neutralized formalin and used for qualitative analy- sis; chlorophyll-a analysis samples were collected by used plastic cans (each with a capacity of 2 L of sur- face water), and preserved in 4% neutralized formalin for qualitative analysis13. Planktonic diatom identification and chlorophyll-a analysis Samples were examined with an inverted mi- croscope (CK40, Olympus, Japan) at 200 or 400magnification. Identification was based on morphology per examples from the literature14–16. The classification of phytoplankton into taxonomic groups and verification of currently accepted tax- onomic names followed AlgaeBase 17. At least 500 cells were counted under Sedgewick counting technique, a method by Sournia (1978) to determine abundance18. The biomass of cells was calculated based on geometrical formulas according to Sun and Liu (2003) 19. Biovolume was calculated based on geometrical cells or colony volumes and subsequently converted to biomass (wet weight) by assuming a specific gravity of 1 mg/mm3 (Wetzel and Likens, 2013)20. In order to analyze chlorophyll-a concentration, about 100-300 mL samples were filtered through GF/C filter paper. The filter was subsequently frozen until sample processing. Chlorophyll-a was disso- ciated with 90% acetone solution overnight at room temperature and in the dark. The samples were cen- trifuged at 400 rpm for 20 minutes to discard scum. Chlorophyll-a in the extract solution was analyzed by an UV-DR-500 spectrophotometer (Hach, USA). 537 Science & Technology Development Journal, 23(2):536-547 Figure 1: Sampling stations in Ba Lai river, Ben Tre province. Data analysis The phytoplankton community structure of species richness was assessed by Margalef ’s index (d) and Pielou’s evenness index (J’) which were used to char- acterize the phytoplankton community at each site. These metrics were calculated by using the PRIMER VI analytical package developed by PlymouthMarine Laboratory (U.K.). One-way analysis of variance (ANOVA) was used to test the significance of the differences among the sites based on the water temperature and the phytoplank- ton species structure metrics. The analysis was com- pleted, using Tukey’s HSD test significant difference. The correlations between the water temperature and the phytoplankton community structure and diversity were determined by Spearman’s correlation analysis method, and then linear regression analysis was per- formed. All variables were log-transformed (log + 1) to normalize their distributions before analysis. Sta- tistical calculations were performed using Statgraphic centurion XV. RESULTS The water temperature and chlorophyll-a concentration Temperature is important because it has a massive effect on determining what organisms can survive in a body of water and also directly affects the rate of photosynthesis and sensitivity of organisms’ toxic wastes21. The results showed that temperature of sur- face water in the Ba Lai River was quite stable spa- tially. It varied between aminimum rate of 27.60C in the dry season and a maximum rate of 33.95C in the rainy season (Figure 2). One-way ANOVA was per- formed and showed that no significant difference in the surface water temperature was detected between the two seasons (p>0.05). The temperature of the sur- vey in Ba Lai River was relatively stable and there were not so large fluctuations between the sample sites and between the two surveys. Ba Lai River is located in the tropical region which has a similar water temperature as some other bodies of water in Southern Viet Nam (range of 28–32C)22. The average water temperature of approximately 30C reflects positive conditions for development of phytoplankton23. Chlorophyll-a concentration varied between 0–58.05 mg/L with the minimum and maximum concentra- tions occurring in the dry season (Figure 3); these 538 Science & Technology Development Journal, 23(2):536-547 Figure 2: The water temperature from sampling sites in dry and rainy seasons. concentrations are higher in some seaward areas than in the inner zone of the Ba Lai River estuary. There was a significant increase from the upstream sections to the downstream river sections in both surveyed seasons. Generally, chlorophyll-a concentration was high at stations BL7, BL8, and BL9; it was highest at BL9 station (whichwas below the Ba Lai dam). Mean- while, BL3 station had the lowest chlorophyll-a con- centration. Phytoplankton need light and nutrients to develop. Since the water environment of Ba Lai River had high alluvial content, turbidity and strong disturbance, these factors prevented light from enter- ing the water environment, leading to poor growth and development of phytoplankton, and resulting in low chlorophyll-a content in that area. BL3 station had high turbidity and total suspended solids (TSS) values that led to low concentration of chlorophyll-a. Phytoplankton composition and abun- dance Species composition of phytoplankton From the two surveys, a total of 128 phytoplank- ton species, belonging to 5 phyla and 76 genera were identified from the Ba Lai River (Figure 4). The phytoplankton species were predominantly Bacillar- iophyceae (69 species, gaining 53.91% of total species number), followed by Chlorophyceae (25 species, 19.53%), Cyanophyceae (19 species, 14.84%), Dino- phyceae (8 species, 6.25%), and Euglenophyceae (7 species, 5.47%) (Figure 4C). The number of Bacil- lariophyceae was most dominant in both seasons which could be related to the fact that diatoms could thrive well in varying environmental changes, while the number of Euglenophyceae and Dinophyceae were lower than the other phylas. The species number of phytoplankton in the rainy season was higher than that in the dry season. Particularly, the number of Dinophyceae was only recorded in the lower part of the Ba Lai dam in the dry season, while the number of Chlorophyceae and Cyanophyceae often appeared at sites near the upper part of the dam in the rainy sea- son. This corresponded to the study conducted on the Ba Lai River by Pham et al. (2017)12. Phytoplankton abundance and biomass Phytoplankton densities ranged from 301–1185  103cells/L in the rainy season and from 303–683  103cells/L in the dry one. The maximum abundance (up to 1185  103 cells/L) occurred at BL9 station in the rainy season, whereas the minimum density (301  103 cells/L) was recorded at BL5 station in the rainy season (Figure 5). The BL9 station had highest den- sity because of the increasing abundance of several cyanobacterial species, including Anabaena smithii, Planktolyngbya limnetica and Oscillatoria limosa, ac- companied by the decreasing abundance of marine diatoms. The phytoplankton usually distributed ac- cording to the rule that the freshwater species (such as 539 Science & Technology Development Journal, 23(2):536-547 Figure 3: Chlorophyll-a concentration in two seasons. Figure 4: The seasonal distributions of phytoplankton composition in Ba Lai River in dry season (A), rainy season (B), and both seasons (C). 540 Science & Technology Development Journal, 23(2):536-547 cyanobacteria and green algae) weremore observed at upper stations and decreased at lower stations, while the phytoplankton communities were more charac- terized by marine diatoms in downstream stations. However, at the sites near the Ba Lai dam, the dis- tribution of phytoplankton communities was disturb- ing. At stations BL8 and BL9 (which are considered downstream stations), the abundance of cyanobacte- ria and green algae were higher than marine diatoms. Besides, cyanobacteria has an advantageous charac- teristic which is optimal growth at higher tempera- tures, whereas diatoms grow better at relatively lower temperatures22,24. The phytoplankton species which were predominant in quantitative samples at some sites of the Ba Lai River were as follows: Anabaena smithii, Dolichospermum affine, Planktolyngbya lim- netica, Coscinodiscus rothii, Coscinodiscus subtilis, Cy- clotella meneghiniana, Scenedesmus acuminatus var. briseriatus, Euglena acus. At BL6 station in the rainy season, the species of Euglenophyceae densities that were dominant were Euglena acus and Phacus acumi- natus, which revealed the mesotrophic to eutrophic conditions of the body of water25. The biomass varied from 5.27–17.09 mg/L in the dry season and from 3.83–27.31 mg/L in the rainy season (Figure 6). The maximum and minimum biomasses occurred at BL3 and BL9 stations, respectively, in the rainy season. Biomass of Bacillariophyceae was dom- inant at most sites, which represented the greatest contribution to phytoplankton biomass in the Ba Lai River. The restriction of light entering the environ- ment led to poor growth and development ofmicroal- gae because of rich alluvial deposits, which was one of the reasons for the low density and primary biomass of phytoplankton in the shallow and turbid estuary systems. Corresponding to the abundance of phyto- plankton, the biomass of cyanobacteria and green al- gae significant increased at the sites near the Ba Lai dam (where there was actually a marked succession of diatom species) along with the high salinity gra- dient. The biomass of Bacillariophyceae was high at the other sites. Moving to the upstream sites, the high abundance and biomass of diatoms that adapted to the saline environment weremore distributed. This could explain the marine species being carried by the saline flows from the tributaries of the Ba Lai River12. This could be a disturbance which greatly explains why the phytoplankton communities were probably influ- enced by the dam. Biological indices of phytoplankton commu- nity Temporal and spatial variations of phytoplankton metrics are shown in Figure 7.Margalef ’s diversity in- dex (d) measured density of species in an ecosystem. The higher the index value, the greater the species richness over the study area, and vice versa 26. Mar- galef ’s diversity index (d) ranged from 2.31–6.56 and 2.60–5.36 in the dry and rainy seasons, respectively. The maximum and minimum biomasses occurred in the BL2 and BL8 stations, respectively, in the rainy season. One-way ANOVAwas performed and no sig- nificant difference of the d index was detected be- tween the two seasons (p>0.05). The diversity at the sites was relatively high and was evenly increased from the dry season to the rainy season, except for stations BL8 and BL10, of which the dry season is higher than the rainy season. The number of species in both sites was higher in the dry season than in the rainy season, whereas the rest was opposite. Evenness index (J’) showed the stability of the phytoplankton communities in the ecosystem, throughout this indi- cator showed the balance of the community 26. Even- ness index (J’) was also high, and 80% of values sur- passed 0.7. The index ranged from 0.62 to 0.89 and from 0.46 to 0.81 in rainy and dry seasons, respec- tively, and no significant difference of J’ index was de- tected between the two seasons (p>0.05). TheBL8 sta- tionwas lower than the other stations which indicated that the phytoplankton community was dominated by a few species coinciding with the observation of the blooms of Scenedesmus acuminatus var. briseriatus in the study area during both seasons. Relation of the water temperature to the phytoplankton communities’ structure and biodiversity The correlation between the water temperature and the phytoplankton communities’ structure and bio- diversity were evaluated by Spearman’s correlation analysis and linear regression analysis. The results of the Spearman’s correlation analysis between the water temperature and phytoplankton communities’ structure and biodiversity in the rainy season are shown in Table 2. The statistical data was analyzed by Spearman’s correlation analysis, which showed that the water temperature positively correlated with chlorophyll-a concentration (r=0.793) but negatively correlated with Margalef ’s diversity index (d) (r =- 0.711) in water. Meanwhile, the other variables did not correlate with the water temperature (p>0.05); these variables were abundance and biomass of phyto- plankton, abundance and biomass of Bacillariophyta, and J’ index. Additionally, the linear regression analysis indicated the relationship between the water temperature and 541 Science & Technology Development Journal, 23(2):536-547 Figure 5: The abundance of phytoplankton in the Ba Lai River. Figure 6: The biomass of phytoplankton in the Ba Lai River. 542 Science & Technology Development Journal, 23(2):536-547 Figure 7: Phytoplankton metrics (d, J’) in the Ba Lai River Table 2: The correlation coefficient between the water temperature and phytoplankton communities structure, biodiversity in both seasons Variables The water temperature Rainy season Dry season Abundance of phytoplankton r -0.024 -0.711 p-value 0.942 0.033* Abundance of Bacillariophyta r 0.037 -0.462 p-value 0.913 0.166 Biomass of phytoplankton r 0.11 -0.742 p-value 0.743 0.026* Biomass of Bacillariophyta r -0.529 -0.790 p-value 0.112 0.018* Chlorophyll-a concentration r 0.793 0.376 p-value 0.017* 0.259 d index r -0.711 -0.363 p-value 0.033* 0.276 J’ index r 0.103 0.007 p-value 0.757 0.985 Note:  Correlation is significant at the 0.05 level chlorophyll-a concentration, and d index, as illus- trated in Figure 8. The water temperature had a positive correlation with chlorophyll-a concentration, using a relatively low adjusted coefficient of sim- ple linear regression model (adjusted R2=43.787%, p=0.022). Meanwhile, the water temperature was sig- nificantly and negatively correlated with d Index and a substantially predictive capability (R2=49.599%, p=0.014). Furthermore, the results of stepwise multiple linear regression analysis showed that when combining the concentration of chlorophyll-a and the d Index, the relationship between water temperature and the two factors could be interpreted in a highly significant model. The observed versus predicted water temper- atures in the Ba Lai River are shown in Figure 9. That is, the water temperature positively correlated with concentration of chlorophyll-a but had a negative re- lationship with d Index. Water temperature = 1.5602 + 0.0232414  Chlorophyll-a - 0.103903 d Index (with: R2 = 71.74%, p=0.005) 543 Science & Technology Development Journal, 23(2):536-547 Figure 8: Model of linear regression analysis in the rainy season. A. The correlation between water tempera- ture and chlorophyll-a concentration, B. The correlation between water temperature and d Index. Figure 9: The observed versus predicted water temperatures in the Ba Lai River. The results of the Spearman’s correlation analysis on water temperature and phytoplankton communities’ structure, and biodiversity in the dry season indicated that the water temperature negatively correlated with abundance of phytoplankton, biomass of phytoplank- ton and biomass of Bacillariophyta (Table 2) (r=- 0.711, r=-0.742, r=-0.790, respectively). Meanwhile correlations of the water temperature with the other variables were non-significant. Furthermore, the sim- ple linear regression between the water tempera- ture and biomass of phytoplankton, and biomass of Bacillariophyta (excepted abundance of phytoplank- ton) resulted, actually, in a model with a substan- tially higher predictive capability (Figure 10). In this model, the water temperature had a significant neg- ative correlation with biomass; the biomass of Bacil- lariophyta were able to yield two models with sub- stantially and relatively low predictive capability (ad- justed R2=46.37%, p=0.018; R2=47.21%, p=0.017, re- spectively). DISCUSSION Phytoplankton reflect and affect water quality which changes in its community structure, patterns of dis- tribution and the proportion of sensitive species. In our study, the phytoplankton assemblages in the Ba Lai River included freshwater, estuarine and fresh- water, and estuarine and marine species (which were dominated by diatoms). The dominance of this group (diatoms) has been already reported by other authors either along the river or estuary; however, studying in running water conditions are more appropriate for evaluating growth of diatoms8,27,28. Generally, dom- inance of diatoms indicates the physical inconsistency of the shallow coastal environments which are very common to study; most diatoms are robust and re- main in the estuarine environments in spite of the er- ratic salinities29,30. Biodiversity is an important component of ecosystem functioning and stability and it has been widely used to characterize community structure. The diversity of 544 Science & Technology Development Journal, 23(2):536-547 Figure 10: Model of Linear regression analysis in dry season. A. The correlation between water temperature and biomass, B. The correlation between water temperature and biomass of Bacillariophyta. a community depends on the Species Richness and Species Evenness31,32. The Margalef ’s Diversity In- dex and Evenness Index were relatively high, which explains that the evenness between the survey sites re- flects the uniformity and stability of ecological charac- teristics in the area. However, there were many clear evidences that the Ba Lai dam caused many negative effects on phytoplankton community and biodiversity in the river. The environmental parameters influence the phy- toplankton distribution; of these parameters, water temperature plays a fundamental role and induces marked changes in a community structure. In the rainy season, the water temperature had a positive re- lationship with chlorophyll-a concentration but neg- ative correlation with d Index. That means if the wa- ter temperature increased, chlorophyll-a content in- creased. However, while there was an increase in wa- ter temperature, the d Index decreased, and vice versa. Chlorophyll-a concentration is a convenient index of phytoplankton biomass, and water temperature is an important factor which influences the control of the growth and distribution of phytoplankton 32–34. In the aquatic ecosystem, chlorophyll-a concentration usually is recognized as a surrogate for biomass of phytoplankton which is dependent primarily on sev- eral environmental parameters and has been studied in recent studies35. According to the study in Meil- iang Bay (China), the concentration of chlorophyll-a was significantly correlated with water temperature; a multiple stepwise linear regression explained 99.2% of the variation of chlorophyll-a. However, in Taihu Lake (China), the water temperature explained 98.7% of the variation of chlorophyll-a. Thus, water temperature is an important factor influ- encing the annual change of chlorophyll-a concentra- tion and phytoplankton biomass36. In general, biodi- versity is an important factor which determines phy- toplankton community performance under varying temperature conditions6. Biodiversity is a useful al- ternative to reflect the ecological quality of aquatic ecosystems because biological communities are influ- enced and integrated by the environmental effects of water37. The warmth of the water temperature and environmental instability caused changes in phyto- plankton diversity and community structure, which are indispensable characteristics underlying ecosys- tem functioning and trophic transfer38. From the re- search inVamCoRiver (Vietnam), the phytoplankton biodiversity positively correlated with water tempera- ture and species number 39. Moreover, when combin- ing the concentrations of chlorophyll-a and d Index, the highly significant model of water temperature was predicted (Figure 9). Increasing the concentration of chlorophyll-a will lead to rise of water temperature but it is limited by the diversity index. Likewise, in the dry season, the water temperature had a significant negative correlation with biomass and the biomass of Bacillariophyta found in this study. That means, if the water temperature increased, the biomass of phytoplankton and biomass of Bacillariophyta de- creased, and vice versa. The impact of temperature on phytoplankton entails nutrient uptake, death rate, and nutrient release from particulate nutrients, abun- dance, and biomass; all of these parameters were in- vestigated by recent studies. The effects of seasonal temperature and daily temperature on phytoplank- ton biomass were simulated numerically and showed that the phytoplankton biomass was strong to the ef- fects (and variation) of water temperatures, accord- ing to the dynamics of the model and model predic- tions in Lake Tai (China)38. Changes in phytoplank- ton biomass and photosynthesis in relation to temper- ature were showed in theWestern English Channel40. Dinoflagellates and biomass showed a positive corre- lation with respect to temperature, reaching the high- est biomass (between 15C and 17C). On the other hand, diatoms indicated a negative correlation with temperature, with highest biomass at 10C41. 545 Science & Technology Development Journal, 23(2):536-547 Besides the other abiotic variables, the variability of water temperature can be considered a major fac- tor contributing to the changes of the phytoplank- ton community structure, which was favorable for the development of chlorophyll-a concentration, diver- sity index and biomass. According to the studies in the Ba Lai River, there were more factors influencing phytoplankton growth. For instance, the phytoplank- ton assemblage was affected by total dissolved solids, salinity and nutrients. This indicates that in addition to nutrient concentrations, the total dissolved solids significantly influence the phytoplankton community structure as a result of high turbidity affected by tides and silt accretion in the estuary 12. Thus, predictive models for relationships between water temperature and phytoplankton community structure are prelimi- nary data which can provide baseline information and be used as critical ecological tools for water quality management. CONCLUSION Results of this study contributed the advantageous in- formation on phytoplankton structure and diversity, as well as their correlation with water temperature in running water systems which are becoming more and more popular in Vietnam. The phytoplankton assem- blage in the Ba Lai River was typical in the shallow and turbid estuarine systems where diatoms were clearly dominant in both the dry and rainy seasons. Addi- tionally, the results illustrated that the temperatures of surface water from the Ba Lai River did not vary among the sampling sites and was similar to the wa- ter temperature of some other rivers in SouthernViet- nam, which were beneficial for the development of phytoplankton. The relationships showed that rises in water temperature could significantly elevate the chlorophyll-a concentration but decrease the diver- sity index, biomass of phytoplankton, and biomass of diatoms (which were the primary species in terms of species number). Water temperature is one of the key factors that changes phytoplankton distribution and associates with phytoplankton bloom, in turn influ- encing the whole food chain and which can seriously impact future global warming. Therefore, it is essen- tial to observe how the changes of environmental pa- rameters impact phytoplankton assemblages in river ecosystems so that the water supply issues in the re- gion can be effectively managed. COMPETING INTERESTS The authors declare that there is no conflict of interest regarding the publication of this article. AUTHORS’ CONTRIBUTIONS The contributions of all authors are equal in selecting data, calculating descriptors, analyzing results, and writing a manuscript. ACKNOWLEDGMENTS This research was funded by Vietnam National Foun- dation for Science and Technology Development (NAFOSTED) under grant number 106.06-2019.51. REFERENCES 1. Marañón E, Lorenzo MP, Cermeño P, Mouriño-Carballido B. Nutrient limitation suppresses the temperature depen- dence of phytoplankton metabolic rates. The ISME journal. 2018;12(7):1836. PMID: 29695860. Available from: https: //doi.org/10.1038/s41396-018-0105-1. 2. Padisak J, Borics G, Grigorszky I, Soróczki-Pintér E. Use of phytoplankton assemblages for monitoring ecological status of lakes within the Water Framework Directive: the assem- blage index. Hydrobiologia. 2006;553(1):1–14. Available from: https://doi.org/10.1007/s10750-005-1393-9. 3. Ye YY, Luo Y, Wang Y, Lin M, Xiang P, Ashraf MA. Relation between diversity of phytoplankton and environmental fac- tors in waters around nanri island. Applied ecology and en- vironmental research. 2017;15(3):241–252. Available from: https://doi.org/10.15666/aeer/1503_241252. 4. Torremorell A, Llames ME, Perez GL, Escaray R, Bustingorry J, Zagarese H. Annual patterns of phytoplankton density and primary production in a large, shallow lake: the central role of light. Freshwater Biology. 2009;54(3):437–449. Available from: https://doi.org/10.1111/j.1365-2427.2008.02119.x. 5. Saifullah ASM, Kamal AHM, Idris MH, Rajaee AH. Community composition and diversity of phytoplankton in relation to en- vironmental variables and seasonality in a tropical mangrove estuary. Regional Studies in Marine Science. 2019;32:100826. Available from: https://doi.org/10.1016/j.rsma.2019.100826. 6. Schabhüttl S, Hingsamer P, Weigelhofer G, Hein T, Weigert A, Striebel M. Temperature and species richness effects in phytoplankton communities. Oecologia. 2013;171(2):527– 536. PMID: 22847329. Available from: https://doi.org/10.1007/ s00442-012-2419-4. 7. Striebel M, Schabhüttl S, Hodapp D, Hingsamer P, Hillebrand H. Phytoplankton responses to temperature increases are constrained by abiotic conditions and community composi- tion. Oecologia. 2016;182(3):815–827. PMID: 27488200. Avail- able from: https://doi.org/10.1007/s00442-016-3693-3. 8. Brogueira MJ, Rosário-Oliveira M, Cabeçadas G. Phytoplank- ton community structure defined by key environmental vari- ables in Tagus estuary, Portugal. Marine environmental re- search. 2007;64(5):616–628. PMID: 17884159. Available from: https://doi.org/10.1016/j.marenvres.2007.06.007. 9. Yang C, Nan J, Li J. Driving Factors and Dynamics of Phy- toplankton Community and Functional Groups in an Estuary Reservoir in the Yangtze River, China. Water. 2019;11(6):1184. Available from: https://doi.org/10.3390/w11061184. 10. Ngo QX, Sayenko EM, Lutaenko KA. Bivalves composition and ecological aspects of the Ba Lai River, an estuary of the Mekong Delta, Vietnam. The Korean Journal of Malacology. 2018;34(3):169–178. Available from: https://doi.org/10.9710/ kjm.2018.34.3.169. 11. NguyenTS,NguyenMH. Studyondepositionof Ba Lai estuary, Ben Tre province. Journal of Science. 2011;27(1):211–217. 12. Pham TL, Tran TND, Tran TT, Nguyen TMY, Ngo XQ. Seasonal variations of phytoplankton community structure in relation to physico-chemical factors in Ba Lai river, Ben Tre Province. Vietnam Journal of Agricultural Sciences. 2017a;15(5):631– 641. 546 Science & Technology Development Journal, 23(2):536-547 13. Bartram J, Chorus I. Toxic cyanobacteria in water: A guide to their public health consequences, monitoring and man- agement. Published on behalf of WHO, Spon Press Lon- don. 1999;416. Available from: https://doi.org/10.1201/ 9781482295061. 14. Shirota A. The plankton of South Vietnam-Fresh water and marine plankton. Overseas Technical Cooperation Agency Japan. 1966;. 15. Wehr JD. Freshwater algae: identification and use as bioindi- cators. Journal of Phycology. 2011;47(2):436–438. Available from: https://doi.org/10.1111/j.1529-8817.2011.00973.x. 16. Edward GB, David CS. Freshwater algae: identification, enumeration and use as bioindicators. 2nd Edition. Wiley- Blackwell. 2015;. 17. Guiry DM, Guiry MG. AlgaeBase. World-wide electronic publi- cation, National University of Ireland, Galway. 2014;Available from: 18. Sournia A. Phytoplankton manual, published by UNESCO. 1978;. 19. Sun J, Liu D. Geometric models for calculating cell biovol- ume and surface area for phytoplankton. Journal of plank- ton research. 2003;25(11):1331–1346. Available from: https: //doi.org/10.1093/plankt/fbg096. 20. Wetzel RG, LikensGE. Limnological analyses. Springer Science & Business Media. 2013;. 21. Uttah EC, Uttah C, Akpa PA, Ikpeme EM, Ogbeche J, Usip L, et al. Bio-survey of plankton as indicators of water qual- ity for recreational activities in Calabar River, Nigeria. Jour- nal of Applied Sciences and Environmental Management. 2008;12(2):35–42. Available from: https://doi.org/10.4314/ jasem.v12i2.55525. 22. Dao TS. Relationship between Phytoplankton and Envi- ronmental Variables from Bien Ho and Lak Lakes in Central Highland of Vietnam. Journal of Environment and Ecology. 2016;7(2). Available from: https://doi.org/10.5296/jee.v7i2. 9704. 23. Wetzel RG. Limnology: lake and river ecosystems (3rdedition). Academic Press San Diego. 2001;p. 205–288–331–393. 24. Miao X, Wang S, Liu M, Ma J, Hu J, Li T, et al. Changes in the phytoplankton community structure of the Backshore Wet- land of Expo Garden, Shanghai from 2009 to 2010. Aquacul- ture and Fisheries. 2019;4(5):198–204. Available from: https: //doi.org/10.1016/j.aaf.2019.02.004. 25. Palmer CM. A composite rating of algae tolerating organic pollution 2. Journal of Phycology. 1969;5(1):78–82. PMID: 27097257. Available from: https://doi.org/10.1111/j.1529- 8817.1969.tb02581.x. 26. Magurran AE. Ecological diversity and its measurement. Princeton university press. 1988;p. 175. Available from: https: //doi.org/10.1007/978-94-015-7358-0. 27. Pham TL, Tran THY, Tran TT, Ngo XQ. Relationship Between Phytoplankton Community And Environmental Variables In The Ham Luong River, Ben Tre Province, Vietnam. Journal of Marine Science and Technology. 2017b;17(4A):235–245. 28. Duong TT, et al. Seasonal variation of cyanobacteria and mi- crocystins in the Nui Coc Reservoir, Northern Vietnam. Jour- nal of Applied Phycology. 2013;25:1065–1075. Available from: https://doi.org/10.1007/s10811-012-9919-9. 29. Fernandez LF, Brandini FP. Diatom association in variation in shelf waters off Parana State, Southern Brazil: Annual varia- tion in relation to environmental factors. Brazilian J Oceanogr. 2004;52:19–34. Available from: https://doi.org/10.1590/S1679- 87592004000100003. 30. PouladiM,Qadermarzi A, Baharvand F, VazirizadehA, Hedayat A. Effects of physicochemical factors on seasonal variations of phytoplankton in theMondRiver Estuary of Bushehr Province, Persian Gulf, Iran. Biodiversitas Journal of Biological Diversity. 2017;18(1). Available from: https://doi.org/10.13057/biodiv/ d180130. 31. Cardinale BJ, et al. Biodiversity loss and its impact on human- ity. Nature. 2012;486(7401):59. PMID: 22678280. Available from: https://doi.org/10.1038/nature11148. 32. Ekhator O, Alika F. Phytoplankton diversity indices of Osse river, Edo state, Nigeria. Ife Journal of Science. 2016;18(1):63– 84. 33. Lerdburoos Y, Tarnchalanukit W, Tabthipwon P, Yoonphand R. Relationship among Nutrients, Chlorophyll-a, Physical and Chemical Properties of Water in Srinadharin Reservoir, Kan- chanaburi Province. NaresuanUniversity Journal: Science and Technology (NUJST). 2013;18(2):9–16. 34. Nurdin S, Mustapha MA, Lihan T. The relationship between sea surface temperature and chlorophyll-a concentration in fisheries aggregation area in the archipelagic waters of Sper- monde using satellite images. In AIP Conference Proceed- ings. 2013;1571(1):466–472. Available from: https://doi.org/ 10.1063/1.4858699. 35. Baliarsingh SK, Lotliker AA, Sahu KC, Kumar TS. Spatio- temporal distribution of chlorophyll-a in relation to physico- chemical parameters in coastal waters of the northwestern Bay of Bengal. Environmental monitoring and assessment. 2015;187(7):481. PMID: 26130242. Available from: https: //doi.org/10.1007/s10661-015-4660-x. 36. Cai LL, Zhu GW, Zhu MY, Xu H, Qin BQ. Effects of tem- perature and nutrients on phytoplankton biomass during bloom seasons in Taihu Lake. Water science and engineering. 2012;5(4):361–374. 37. Adelakun KM, Mu’azu MM, Amali RP, Omotayo OL. Diver- sity of Phytoplankton Communities in a Tropical River Basin, Nigeria. Hydro Nepal: Journal of Water, Energy & Environ- ment. 2016;19:52–56. Available from: https://doi.org/10.3126/ hn.v19i0.15353. 38. Rasconi S, Winter K, Kainz MJ. Temperature increase and fluctuation induce phytoplankton biodiversity loss-Evidence from a multi-seasonal mesocosm experiment. Ecology and Evolution. 2017;7(9):2936–2946. PMID: 28479993. Available from: https://doi.org/10.1002/ece3.2889. 39. Dao TS, Bui TNP. Phytoplankton from Vam Co River in South- ern Vietnam. Environmental Management and Sustainable Development. 2016;5(1):113–125. Available from: https://doi. org/10.5296/emsd.v5i1.8775. 40. Chen M, Fan M, Yuan X, Zhu H. Effect of seasonal chang- ing temperature on the growth of phytoplankton. Mathe- matical Biosciences & Engineering. 2017;14(5&6):1091–1117. PMID: 29161852. Available from: https://doi.org/10.3934/mbe. 2017057. 41. Xie Y, Tilstone GH, Widdicombe C, Woodward EMS, Harris C, Barnes MK. Effect of increases in temperature and nutrients on phytoplankton community structure and photosynthesis in the western English Channel. Marine Ecology Progress Se- ries. 2015;519:61–73. Available from: https://doi.org/10.3354/ meps11101. 547