Joint effects of five environmental factors on the growth of cyanobacterium Microcystis aeruginosa

In many lakes and reservoirs, Microcystis aeruginosa is one of the dominant bloom species. Five environmental factors, including nutrients and physical factors, were selected to evaluate their effects and interactions on the growth of M. aeruginosa (FACHB-905) by joint analysis in a laboratory batch culture. The results indicated that all five factors affected the growth rate alone or in combination, and that their interactions were complex. This cyanobacterium strain preferred higher water temperature and alkaline conditions, while not requiring high illumination or high concentrations of nitrogen and phosphorus. Owing to these features the bloom of this cyanobacterium appears easily in nature. The form of nitrogen (nitrate or ammonium) also affected the assessment of M. aeruginosa bloom. The possibility of M. aeruginosa bloom would still exist even if the phosphorus concentration in the water column was very low. The result provided a good basis for the analysis and prediction ofM. aeruginosa blooms in terms of environmental assessment, because the joint analysis of multiple factors would offer more valuable information than a univariate analysis. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/). doi: 10.2166/wcc.2018.255 ://iwaponline.com/wqrj/article-pdf/54/2/79/682644/wqrjc0540079.pdf Guikun Hu Qingtian Zhang (corresponding author) Tianjin Marine Environmental Protection and Restoration Technology Engineering Center, Tianjin 300457, China and Tianjin Key Laboratory of Marine Resources and Chemistry, Tianjin University of Science and Technology, Tianjin 300457, China E-mail: qtzhang@163.com Qingtian Zhang School of Life Sciences, Nankai University, Tianjin 300071, China This article has been made Open Access thanks to the kind support of CAWQ/ACQE (https://www. cawq.ca).


INTRODUCTION
Harmful algal blooms (HABs) in freshwater have become a hot topic across the world. They result in a deterioration in the quality of water resources, and also cause bad conditions that affect the growth and development of aquatic organisms in lakes or reservoirs (Kameyama et al. ; Backer et al. ). The formation mechanisms of HABs, as well as how to forecast or control the blooms, have therefore become a worldwide concern and a subject of serious debate (Tayaban et al. ). It is well known that an excess of green-blue algae (also usually Their results gave us more information than some univariate experiments. However, the limitations of orthogonal experiments, which could not include many factors or their levels, meant that few factors were included in the analysis. Studies using many experimental treatments would incur too much expenditure and workload, and those using fewer factors or levels only offer a limited explanation of blooms. Quiblier et al. () also conducted multi-factor studies in batch culture. They used natural water and added some nutrients; their main subject was the phytoplankton community. It has thus been shown that more studies including more factors are essential to bloom analysis.
The Uniform Design method (UD) was devised by Professors Fang and Wang in 1978, and is an important method for application in virtual experimental and solidity designs; UD has become a standard tool in experimental design over the last two decades (Fang & Ma ; Winker & Lin ).
Compared with other statistical methods, UD reduces the number of experiments in a multiple-dimension optimization and allows the largest possible number of levels for each factor (Wu et al. ). It has been used successfully in many experiments of condition optimization (Peng et al. ). Compared with the common orthogonal design method, this method has some particular advantages. First, only one experiment is needed for each level of each factor, and the experimental counts are equal to the level counts; the lower number of experimental treatments will reduce cost and workload significantly. Secondly, it is both convenient for analyzing interactions among experimental factors and helpful in developing a mathematical model. Owing to these advantages, UD has hitherto been successfully applied in many research areas (Liang et al. ; Mehri & Ghazaghi ).
Five environmental factors showing effects on the growth of M. aeruginosa were chosen to do a basic integrative study. The five factors were nitrogen, phosphorus, temperature, pH and illumination. The joint effect of these factors on the growth of M. aeruginosa was the main aim of this study, but we also try to test the different impacts between ammonium and nitrate. We hope that the optimal conditions obtained from this study will be helpful in understanding the complexity of cyanobacterial blooms.

Cyanobacterium strain and culture conditions
The cyanobacterium strain Microcystis aeruginosa (FACHB-905) was bought from the Freshwater Algae Culture Collection of the Institute of Hydrobiology, CAS (http://algae.ihb.ac.cn). The cyanobacterium seed was cultured in BG-11 medium in the culture box prior to the experiments. Culture conditions for the seed were as follows: water temperature 25 ± 0.5°C; pH 7.5-8.5; illumination 28.5-34.2 μmol photons m −2 s −1 ; and photoperiod 14 h:10 h (light:dark).
Experimental materials, equipment and environments were sterilized or disinfected before the experiments.
M. aeruginosa was cultured under axenic conditions. All operations and counting processes were performed under aseptic laboratory conditions.

Experimental designs
The five environmental factors selected for the study were nitrogen (nitrate and ammonium in different experiments), K 2 HPO 4 , illumination, water temperature, and pH. The optimal table of the Uniform Design (U* 12 6 2 × 3 2 × 4),  Table 1. Twelve treatments were set for each experiment, as well as three repetitions for each treatment. The batch culture method was used for both experiments, i.e. no nutrients were added during the experiments.
(1) An appropriate amount of M. aeruginosa in its exponential growth stage was transferred into a 5 L triangular flask and all the components were added to the BG-11 culture medium except nitrogen and phosphorus. After being shaken gently, 100 mL of mixed liquid with well distributed M. aeruginosa was pipetted into a 250 mL triangular flask, which was marked as one repetition of an experimental treatment.
(2) Nitrate and K 2 HPO 4 were added to each treatment, according to the concentrations presented in Table 1.
Three repetitions were specified for each treatment.
(3) After controlling the pH of each treatment, the flasks were placed under their temperature and lighting conditions, as listed in Table 1. For the second experiment, the above steps were repeated, but using ammonium instead of nitrate.

Data collection and analysis
The densities of M. aeruginosa were measured microscopically in the blood-cell-counting chamber every two days.
These flasks were shaken twice a day during the experiments. The growth rates (μ) of this cyanobacterium were calculated using the formula: where t indicated the days of exponential growth period, N was the density of the t day, and N 0 was the initial density at the beginning of the exponential growth period. DPS software (Tang & Feng ) was used for the subsequent statistical analysis and data fitting. The software OriginPro was used to plot the figures.

Brief introduction to the growth of M. aeruginosa
In both experiments minor increases and lower final densities were detected for most treatments. M. aeruginosa increased greatly and got the highest final densities under the conditions of treatment C04 in both experiments.
In the ammonium experiment the treatment C04 represented the best growth of M. aeruginosa, outshining other treatments, in which the growth curve went up rapidly.
Among the other treatments, C02 showed a better growth condition, but its cell density was close to that in treatments C08, C11 and C12. In the nitrate experiment the cell densities increased sharply in both treatment C04 and treatment C09, although C04 showed a little higher density.
Some treatments showed minor increases, and others had nearly no increase. The final densities with the two nitrogen forms were quite different; the difference between the maximum and minimum was huge ( Figure 1).
Comparing the final densities in terms of the treatment,  (Table 1). These differences clearly indicated the favorite growth condition of M. aeruginosa. This cyanobacterium prefered higher water temperatures and an alkaline environment, but did not require higher illumination or higher concentrations of nutrients (nitrogen and phosphorus). Phosphorus was not added in the treatments C08 and C11 in this study; however, the cyanobacteria in them kept growing to some extent ( Figure 2). The growth rates of C09 showed great difference in terms of nitrogen forms, which was consistent with the analysis of final density.
The effect of temperature was also obvious in this study. The growth rates in the high-temperature group (e.g. C02, C04, C05 and C12) were higher than those of the low-temperature group (e.g. C01, C06, C07 and C10). Low temperature was an important limitation for the bloom (Figure 2).

Regression equations
The stepwise regression of a quadratic multinomial to the growth rate was performed for both experiments, in order to derive optimal growth conditions and understand the relationships among these factors.

Optimal growth conditions of M. aeruginosa
The optimal values indicated that this cyanobacterium prefered higher water temperatures and an alkaline environment, but did not need higher illumination or higher concentrations of nitrogen and phosphorus. That is to say, this cyanobacterium strain had highly adaptive abilities in the environment. This result was consistent with some reports about cyanobacteria (Dokulil & Teubner ).
Cyanobacteria proliferations have been reported in oligotrophic and mesotrophic freshwater bodies ( Jacquet et al.

).
A strong M. aeruginosa proliferation was observed in the Djoudj pond with higher nitrogen concentrations, whereas soluble reactive phosphorus (SRP) concentrations were always low (Berger et al. ; Quiblier et al. ).
The results of this study were quite similar to the field observations. In addition, the designed N/P (nitrogen/ phosphorus) ratio in treatment C04 was 15:1, which was also the same as the Redfield ratio and similar to other reports (Yi et al. ; Zhang & Hu ). This ratio was also a good condition for the increase of microalgae.
This regression equations showed that all the five environ- Generally speaking, the difference between the univariate and multivariate results was not big (Zhang et al. a, b, c), but the multivariate result would be more suitable for bloom assessment. Also, the fitting model was useful to forecast cyanobacterium growth under various conditions. In addition, the bloom may be limited by some factors that were not tested in our study, so further studies including more key factors should be conducted in laboratory and in field study. It is a good basis for bloom analysis.

Influence of nitrogen forms
The results of this study suggested not only the influence of environmental factors on the growth of M. aeruginosa, but also reflected the different effects caused by the nitrogen forms. Because, as mentioned before, the difference between the two experiments was just the nitrogen forms, while the optimal conditions and their interactions were not the same (Figure 2  The nitrogen forms in the aquatic ecosystem were changeable; the transformation between nitrogen forms occurred frequently owing to biological processes. A combined analysis including various nitrogen forms in the water column would be useful for phytoplankton study.

About phosphorus storage
Obviously, the zero phosphorus concentration in this study was a calculated result, which did not mean this cyanobacterium did not need phosphorus nutrient (Wang et al.  in a water column, a bloom was still possible. It may also affect the analysis of the N/P ratio.

Enlightenments gained from the physical factors
The current results suggest that water temperature had a strong influence on the growth of M. aeruginosa. This was consistent with monitoring results in situ, with many cyanobacteria blooms occurring when water temperature increased during the summer months (Zheng et al. ).  This primary study is a good basis for bloom analysis; experiments including more environmental factors will give us a better understanding of bloom occurence.