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Seasonal variations of phytoplankton community structure in relation to physico-chemical factors in Lake Baiyangdian, China
Available online at www.sciencedirect.com
Procedia Environmental Sciences 2 (2010) 1622–1631
International Society for Environmental Information Sciences 2010 Annual Conference (ISEIS)
Seasonal variations of phytoplankton community structure in relation to physico-chemical factors in Lake Baiyangdian, China Cunqi Liua, Lusan Liub, Huitao ShenC,* a b c
College of life sciences,Hebei university,Baoding 071002,China
Chinese research academy of enviromrntal sciences, Beijing 100012,China
College of resource and enviroment, East China normal unviversity, Shanghai 200062,China
Abstract In order to study the seasonal variations in the structure and dynamics of phytoplankton community of the Lake Baiyangdian in China, quantitative samples were collected in each season during 2005. A total of 152 taxa were identified by microscope, phytoplankton had an abundance ranging from 131.25×10 4 ind. L-1 to 745.88×104 ind. L-1. The highest phytoplankton abundance were observed in spring and the lowest in autumn. Cyanophyta, Euglenophyta and Cryptophyta were the most important groups in terms of abundance and biomass. The preponderant and sub-preponderant species changed in accordance with the seasons. Canonical Correspondence Analysis (CCA) was used to investigate the relationship between environmental factors and phytoplankton community. The results indicated that nutrient gradient had a positive correlation with axis 1 in spring, summer and autumn. But in winter, the nutrient gradient had a positive correlation with axis 2. The varieties of environmental factors affected the structure of phytoplankton community in each season. CCA shows a slight, but significant method to understand the spatial distribution of phytoplankton community in lake systems.
© 2010 Published by Elsevier Ltd. Keywords
Baiyangdian; Phytoplankton; Environmental factors; Seasonal variations; Canonical Correspondence Analysis (CCA)
1 Introduction Phytoplankton plays an important role in lake ecosystems as they produce oxygen and food, which sustains all other forms of life [1,2]. Knowledge on their abundance and community composition can aid fishery management and provide further understanding of ecological balance. In different types of lakes, changes in the phytoplankton community have long been recognized as providing a good indicator of the trophic status and environmental quality [3,4]. Further more, various physico-chemical parameters are responsible for controlling phytoplankton growth and reproduction [5,6]. Actually, environmental instability and temporal and spatial changes determine the community present in a lake [7], how to recognize the composition and dynamics of phytoplankton is such a difficult problem. So many different multivariate analysis methods were widely used to appraise phytoplankton communities, such as Cluster Analysis [8], Canonical Correspondence Analysis [9,10]. Canonical Correspondence Analysis (CCA) was used to reduce data dimensions, determine the main environmental variables responsible for the fluctuations in the abundance and dynamics of phytoplankton community[4,11]. Lake Baiyangdian, which lies in the middle of Hebei Province, is the largest natural freshwater body in the North China Plain. It is a lake group of more than 140 lakes with a total area of approximately 366 km2 at water level of 10.5 m, and latitude and longitude coordinates of 38°44′ -49′ and 115°45′- 116°07′, respectively[12,13]. Baiyangdian is known as “the kidney of North China” which is important to support water circulation, to keep biological diversity, and to control pollution. It is closely linked with the North China people’s survival and development. Simultaneity, Lake Baiyangdian has historically been an important recreational site attracting a large number of visitors and an increasing number of permanent residents. Recently, due to low local precipitation, lack of inflows and serious pollution all combine to make Lake Baiyangdian especially sensitive and vulnerable system. The water quantity of Lake Baiyangdian has decreased heavily. The serious pollution of Baiyangdian influenced the community of phytoplankton. The lake has been studied since the 1990s by biologists, ecologists and hydrologists[15-19]. * Cunqi Liu. Tel.: +0-086-0312-5079364; fax: +0-086-0312-5079364. E-mail address: [email protected].
1878-0296 © 2010 Published by Elsevier doi:10.1016/j.proenv.2010.10.173
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Meanwhile, a few articles about the temporal and spatial distribution of phytoplankton in this area were published [20-22]. However, information about the physico-chemical conditions directly related to the phytoplankton community is still lacking. In the present study, we examined the phytoplankton succession during each season in 2005 in Lake Baiyangdian. The aim of the study was to identify the relationships between phytoplankton succession and their control factors and to increase our understanding of phytoplankton succession patterns in this lake. 2 Sites, materials and methods 2.1 Sites descriptions Eight sampling sites were relatively isolated from one another in Lake Baiyangdian to give good spatial representation of the whole characters and required navigating between sampling areas through narrow channels bordered by areas of emergent reeds (Fig. 1). Samples were collected in each season from April to November in 2005. Caipu Tai was farthest from the sewage sources, and considered to be the cleanest site. Duan Cun, Guang Dian, Quan Tou were fisheries, the aquiculture were so flourishing. Zaolin Zhuang was the exit of Lake Baiyangdian, and grew so much aquatic grass. Wangjia Zhai closed with the village and disturbed by people. Shaoche Dian was a lentic zone, where bred fish and crab. Nanliu Zhuang was closed to the Fuhe River and industrial inputs from Baoding City resulting in the highest nutrient level. 2.2 Sampling Subsurface water samples (1 L) for phytoplankton analysis were collected seasonally during 2005 from each site. Sample locations were consistent in each season. The water samples were fixed with Lugol’s solution for quantitative analysis. Each sample was then allowed to settle for at least 36 h, where upon the supernatant was carefully removed and the remaining volume was adjusted to 30 mL. This sample was kept at ćuntil analysis [23].
Fig.1 The sampling sites of Lake Baiyangdian in Hebei Province, China 1, Duan Cun; 2, Caipu Tai; 3, Quan Tou; 4, Guang Dian; 5, Zaolin Zhuang; 6, Wangjia Zhai; 7, Shaoche Dian; 8, Nanliu Zhuang The algal taxa were identified and counted according to these references, including Dalian Hydrobiological Institute [24], Jin[25], Han[26], Li[27], Snit’ko[28], Holopainen[29]. Biomass was estimated by biovolumes assuming 1μm3 equals to 1pg [30,31]. The cell volume of each algal species was calculated based on cell shape and size [32,33]. Water quality parameters were measured at all sampling stations by Agency of Environmental Protection of Baoding, Hebei Province. These parameters included pH, dissolved oxygen (DO), chemical oxygen demand (COD), biological oxygen demand (BOD), total phosphates (TP), total nitrogen (TN), ammonium nitrogen (NH4+-N), transparency (Secchi disk clarity) (Trans). Water transparency was estimated on each sampling occasion with a white Secchi disc [34](φ25 cm). Water samples were analysed for pH and the other nutrients following standard analytical techniques [35]. 2.3 Statistical analysis Canonical Correspondence Analysis (CCA) method was used to ascertain the relationships between phytoplankton and environmental variables along the study period. CCA extracts synthetic gradients from the biotic and environmental matrices and
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the explanatory variables are quantitatively represented by arrows in graphical biplots[36]. The arrow direction indicates positive or negative correlations and their length is relative to the importance of the explanatory variable in the ordination[37]. For statistical analysis of data, each species with more than 1% of total phytoplankton density, which also apperaed at least in three seasons and 4 sites during the study period, was considered (Table 2). Species data were log (X+1) transformed before analysis to obtain normal distribution[9]. Environmental variables except pH were also log (X+1) transformed. 3 Results 3.1 Physico-chemical characteristics Table 1 shows the mean physico-chemical values as well as the standard error (S.E.) of the investigated sites of Lake Baiyangdian in 2005. The pH was always alkaline in each season and fluctuated between 8.028 (November) and 8.436 (April). The mean dissolved oxygen (DO) concentrations were 8.2 mg/L, respectively, over the 4 seasons with the highest value 8.925 mg/L appearing in spring and lowest value 6.39 mg/L in autumn. The seasonal distribution of SD was 71.875 cm in April, 59.375 cm in July, 78.75 cm in September and 67.5 cm in November respectively. Biochemical oxygen demand (BOD) content fluctuated from 5.195 mg/L in spring to 6.581 mg/L in summer. The mean TN and NH4+-N concentrations were 4.038 mg/L and 2.205 mg/L, respectively. The concentrations of TN and NH4+-N showed similar seasonal variance with the highest concentrations in autumn, followed in a descending order by winter, spring and summer. Total phosphorus (TP) attained a maximum concentration in November (0.183 mg/L) and decreased to a minimum in July (0.085 mg/L). Average chemical oxygen demand (COD) content reached a maximum in September (31.138 mg/L) and a minimum value (28.063 mg/L) was recorded in July. The causal link was demonstrated in the correlation analysis (Table 2). There was no clear link between pH and the other environmental variables. Dissolved oxygen was positively correlated with transparency, but negatively with other nutrients (i.e. COD, BOD, TP et al.). Of all the nutrients variables were significantly correlated by twos in the data set. It was likely that transparency at these sites was limited by a combination of variables, involving TP, TN and NH4+-N, and TN concentrations seemed to be most important. Table 1 Mean and range of nutrient concentrations in Lake Baiyangdian, 2005 Date/Parameter Apr.2005 Jul.2005 Sep.2005 Nov.2005
Mean Range Mean Range Mean Range Mean Range
Date/Parameter Apr.2005 Jul.2005 Sep.2005 Nov.2005
pH
DO(mg/L)
SD(cm)
BOD(mg/L)
8.436±0.110* 8.03~9.04 8.289±0.101 7.76~8.68 8.369±0.055 8.15~8.54 8.028±0.119 7.3~8.4
8.925±1.373 2.14~15.4 8.9±1.109 5~12.6 6.39±0.680 2.06~8.3 8.619±1.158 1.04~11.53
71.875±13.227 30~150 59.375±10.541 30~120 78.75±12.598 40~150 67.5±14.064 15~134
5.195±1.074 3.8~12.5 6.581±2.000 2.05~19.3 5.288±2.053 2~19.3 5.748±0.907 3.93~11.9
TP(mg/L)
COD(mg/L)
0.143±0.091 0.019~0.779 0.085±0.027 0.027~0.262 0.087±0.027 0.023~0.268 0.183±0.116 0.007~0.973
29.45±6.631 13.8~73.7 28.0625±3.474 19.4~50.6 31.138±4.782 23.8~64.2 30.213±6.287 18.1~72.9
TN(mg/L) Mean Range Mean Range Mean Range Mean Range
NH4+-N(mg/L)
2.651±1.177 0.855~10.8 1.974±0.455 1.1~5.11 7.508±3.047 0.74~27.2 4.020±2.593 0.619~22
1.493±0.954 0.513~8.17 0.984±0.328 0.482~3.25 3.280±2.775 0.289~22.7 3.062±2.507 0.318~20.6
* Mean ± Standard Error (S.E.) Table 2 Correlation coefficients of environmental variables
pH DO
DO 0.449
COD -0.393 -0.944**
BOD -0.512 -0.932**
TP -0.439 -0.943**
TN -0.332 -0.866**
NH4+-N -0.413 -0.926**
SD 0.329 0.682
Cunqi Liu et al. / Procedia Environmental Sciences 2 (2010) 1622–1631
COD 0.973** 0.972** 0.945** BOD 0.942** 0.893** TP 0.929** TN NH4*-N SD Date except pH were log transformed before analysis. (* for P˘0.05, ** for P˘0.01)
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0.973** 0.948** 0.993** 0.909**
-0.689 -0.580 -0.790* -0.795* -0.730*
3.2 Phytoplankton composition, abundance and biomass During the study, 152 species of eight algal classes, Chlorophyta (52.6%), Cyanophyta (18.4%), Bacillariophyta (13.2%), Eugleno- phyta (8.6%), Pyrrophyta (2.6%), Xanthophyta (2%), Cryptophyta (2%) and Chrysophyta (0.6%), were identified. Cyanophyta, Euglenophyta and Cryptophyta were the most important groups in terms of abundance and biomass than the other taxonomic groups. Raphidiopsis sinensia, Merismopedia tenuissima of Cyanophyta, Cryptomonas ovata, Cryptomonas marssonii of Cryptophyta, Euglena caudata from Euglenophyta were the most common species throughout the year. There were marked seasonal and spatial differences in the quantitative composition and biomass of phytoplankton communities (Table 3). Throughout the sampling period, April 2005 was the month in which mean phytoplankton reached its maximal values (745.9h104 ind. L-1 in abundance and 18.43 mg L-1 in biomass). C. ovata and C. marssonii from Cryptophyta were the most abundant species of all the sampling sites. The both species contributed to the high biovolume records due to their big cell-sizes. Also, Chlamydomonas sp. from Chlorophyta and Synedra sp. from Bacillariophyta were present in large amounts, but no apparent increases were recorded in biomass. July 2005 was another significant month in terms of mean phytoplankton abundance and biomass. R. sinensia, Merismopedia glauca and Lyngbya limnetica from Cyanophyta, Chlamydomonas sp. and Chlorella vulgaris from Chlorophyta were common at all stations. In the sampling of September 2005, a significant decrease was recorded in abundance and biomass of phytoplankton. Mean phytoplankton density (131.3h104 ind. L-1) reached minimal values in the four seasons. C. vulgaris and Synedra sp. were noted in phytoplankton density, but were not important in biomass. In winter (November 2005), an increase was recorded in terms of phytoplankton abundance than September. Whereas, its mean biomass values reached lowest (3.08 mg L-1). C. ovata, C. marssonii, L. limnetica and C. vulgaris were important species in terms of abundance and frequency (100% among stations). L. limnetica and C. vulgaris were present in large amounts at all stations in terms of density. C. ovata and C. marssonii were important in total biomass despite small density numbers. During the study period, the nutrient concen- trations in Nanliu Zhuang were obviously higher than the other sites due to the inputs of sewage discharge and industrial contaminants from Baoding city, this made the phytoplankton abundance and biomass the most. It has been well documented that nutrient enrichment resulted in high algal densities[38]. Duan Cun and Quan Tou were fisheries, both of them closed to the village as Wangjia Zhai did. All of these stations received non-point polluted sources (agricultural runoff and human waste) and had the similar abundance and biomass. The sites of Guang Dian, Zaolin Zhuang and Shaoche Dian grew so much aquatic plants, such as reeds, lotus and cattail et al. Phytoplankton abundance and biomass in these three sites were less than the anterior four sampling sites. Caipu Tai ranked the last in abundance and biomass. This site was far away from the village and sewage river, the nutrient concentrations was the lowest of all the sites. Table 3 Phytoplankton abundance and biomass in Lake Baiyangdian in 2005 (Abundance: ×104 ind. L-1; biomass: ×mg L-1) Data/Sites Apr. Jul. Sept. Nov. Mean
1
2
3
4
5
6
7
8
Mean
814.2
39.6
159.3
207.5
117.6
165.8
90.6
4372.5
745.9
(23.36)*
(0.70)
(1.95)
(2.47)
(1.58)
(1.93)
(1.04)
(114.42)
(18.43)
319.2
76.5
1185.5
185.7
183.9
867
201.8
1643.9
582.9
(6.54)
(1.86)
(25.47)
(6.07)
(4.67)
(38.81)
(6.62)
(4.03)
(11.76)
90.5
34.5
152.9
167.6
54.2
267.6
169.7
113.3
131.3
(1.66)
(0.93)
(2.73)
(4.62)
(1.97)
(5.50)
(10.61)
(0.75)
(3.60)
156.2
25.5
604.8
198.2
154.5
198.9
179
17.7
191.8
(2.65)
(0.46)
(8.73)
(2.63)
(2.66)
(3.76)
(3.26)
(0.48)
(3.08)
345
44
525.6
189.7
127.5
374.8
160.2
1536.8
413
(8.55)
(0.99)
(9.72)
(3.95)
(2.72)
(12.50)
(5.39)
(29.92)
(9.22)
* For phytoplankton biomass. 3.3 Correlations and CCA analysis
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Main environmental variables responsible for the greatest phytoplankton community variability were identified with CCA that simultaneously represented environmental variables in a bidimensional space [11]. Total phytoplankton abundance was positively with COD, TP, TN and NH4+-N, but negatively with DO and SD (Table 4). Forward selection indicated that six of the eight physico-chemical variables (except for pH and BOD) made independent and significant contributions to the variance in phytoplankton data (P˘0.05). Consequently, 27 biotic and 6 abiotic variables were used in present CCA. The codes of phytoplankton were showed in Table 5. The statistical information of CCA analysis was shown in Table 6. Results strictly represented the existing relationships between environmental variables and phytoplankton species. The eigenvalues and the percentage for variance of each season in axis1 were much higher than axis 2. Species-environmental correlation for axis 1 and 2 was high, indicating a strong correlation between phytoplankton species distribution and environment variables used for ordination. Table 4 Correlation coefficients between phytoplankton abundance and environmental variables pH
DO
COD
Abundance -0.704* 0.709* * for P˘0.05, ** for P˘0.01, - not significant.
BOD
TP
TN
NH4+-N
SD
-
0.771*
0.765*
0.710*
-0.952**
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Table 5 Codes of phytoplankton species for CCA Codes s1 s2 s3 s4 s5 s6 s7 s8 s9 s10 s11 s12 s13 s14 s15 s16 s17 s18 s19 s20 s21 s22 s23 s24 s25 s26 s27
Phytoplankton Scenedesmus quadricauda Scenedesmus bijuga Chlamdam onas sp. Westella botryoides Kirchneriella sp. Cosmarium quadrum Chlorella vulgaris Dictyosphaerium pulchellum Coelastrum sphaericum Crucigenia rectangularis Crucigenia tetrapedia Lyngbya limnetica Gloeocapsa minima Dactylococcopsis irregularis Phormidium tenue Merismopedia tenuissima Gloeocapsa turgida Microcystis pulverea Oscillatoria tenuis Synedra sp. Navicula sp. Cyclotella sp. Euglena caudata Euglena acus Trachelomonas ensifera Cryptomonas ovata Cryptomonas Marssonii
Spring + + + + + + + + +
+ + + +
+ + + +
+ +
Summer + + + + + + + + + + + + + + + + + + + + + + + + + +
Autumn + + + + + + + + + + + + + + + + + + + + +
Winter + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + +
Table 6 Statistics for the first two axes of CCA performed on phytoplankton at Lake Baiyangdian Axis Season Eigenvalues Percentage
Spring 0.165 58.0
Axis1 Summer Autumn 0.157 0.122 52.5 34.6
Winter 0.157 39.1
Spring 0.059 20.7
Summer 0.057 19.1
Axis2 Autumn 0.103 29.4
Winter 0.095 23.8
Cumulative Percentage for variance
58.0
52.5
34.6
39.1
78.7
71.6
64.0
62.9
Species -environment correlations
0ˊ99 8
0.994
1.000
1.000
0.993
0.881
0.997
1.000
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Fig. 2 Spatial ordination resulting from CCA of most abundant phytoplankton species with respect to physical-chemical parameters. See Table 5 for phytoplankton genus name codes. Fig. 2 showed the CCA sample biplots in relation to environmental gradients potentially influencing the phytoplankton. The length of the environmental variable arrows represented the relative explanatory of each variable in relation to individual sample positions within the ordination. TP, TN, NH4+-N and COD had positive correlations with axis1 in spring, summer and autumn. Nevertheless, SD had a negative correlation with axis 1. In spring, several phytoplankton species such as Westella botryoides, Kirchneriella sp., Dictyosphaerium pulchellum, Coelastrum sphaericum, Gloeocapsa minima, Dactylococcopsis irregularis, Phormidium tenue et al., which distributed at the right side of Axis1, were positively correlated with the content of nutriments such as TP and TN. Nevertheless, some phytoplankton species such as Crucigenia tetrapedia, G. minima, D. irregularis, P. tenue, M. tenuissima, Gloeocapsa turgida and Microcystis pulverea, distributed at the left side of axis1 in summer and autumn. These species were negatively correlated with SD. In winter, axis 2 was positively correlated with TN and TP. The strong influence of nutriments was seen on the species plot which showed Euglenophyta, like C. ovata and C. marssonii, associated with high content of TP and TN. From the CCA biplots, we considered that the number of phytoplankton species and abundances were well adapted to changing environmental parameters. The rapidly changing nutrient status had resulted in major shifts in community composition and magnitude of phytoplankton. 4 Discussion Investigation of the structure and function of phytoplankton communities are of utmost importance for studies of the lacustrine ecosystems’ dynamics[11]. Long-term phytoplankton succession has been well investigated in aquatic ecology in China[41]. Strong annual variation in the physical, chemical and biological characteristic of lakes seems play a regulatory role on phytoplankton dynamics, and annual variation in nutrient supply is an important determinant of phytoplankton variability. Previous studies of the Lake Baiyangdian concerning the phytoplankton composition [40,21] identified 129 taxa of phytoplankton in 1958 and 408 in 1995 respectively. Among the most relevant aspects of such studies were those related to population abundance and diversity, spatial and temporal distribution. Present study reported the spatial and temporal variation
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of phytoplankton in this area, as well as up to what extent the main environmental factors were responsible for the system’s phytoplankton variability. During the study period, a total of 152 taxa were identified and less than the year of 1995. The phytoplankton was dominated by Cryptophyta in early spring. The summer period showed Cyanophyta and Chlorophyta dominance (when the oxygen depletion and a large fish kills occured). The dominance of Cyanophyta, Cryptophyta and Chlorophyta was found in the autumn-winter period. Abundances and composition of phytoplankton varied significantly through the seasons in Lake Baiyangdian. Generally, differences in phytoplankton biomass and composition have been found between lakes of different trophic lakes [39, 23]. Nutrient-rich lakes of temperate areas are usually dominated by diatoms and blue-green algae whereas the commonest phytoplankton organisms in poor environments are nano- planktonic species and flagellates[42]. It is well known that changes in physico-chemical characteristics of any lake can lead to concomitant qualitative and quantitative changes in phytoplankton communities[2]. The physico-chemical factors of Lake Baiyangdian varied seasonally during the study period. The CCA ordination reflected the corresponding correlations between phytoplankton communities and major environmental variables. The ordination biplots showed that the environmental variables in turn influenced the dynamics of phytoplankton. From winter to early spring, the nutrients content such as phosphorus and nitrogen increased, which promoted the growth of phytoplankton. As a consequence, we observed an increase of phytoplankton in early spring. At the same time, the spring growth of algae is firstly dominated by Cyanophyta. Nutrient limitation was a common characteristic during summer. The CCA analysis presented a limitation of nutrients in summer and autumn. We could see that massive of phytoplankton were far away from the arrow of nutrients in these two seasons. In winter, the reduction of light energy resulted in phytoplankton abundances decreased, which is associated to cool winter temperatures. In addition, the nutrients had positively correlation with the second CCA axis, and Cryptophyt had a greater pertinence with nutrients content. The CCA performed on the data revealed that the phytoplankton abundances had obvious dynamics and were mainly regulated by DO, TP and NH4+-N. But different lakes had diverse influencing factors. Habib et al. [3]showed that Silicate was more important than nitrate and phosphate as an environmental determination influencing phytoplankton assemble in Lake Loch Lomond. Zhang et al. [43] studied driving forces shaping phytoplankton assemblages in two subtropical plateau lakes in China. The results suggested that the major driving forces in Lake Fuxian were physical variables, and particularly the underwater light climate, whereas, nutrients were the important driving force in Lake Xingyun. The nutrient supply and its ratios have a decisive effect on the species composition of the phytoplankton since different algal species have different nutrient requirements[44]. On the other hand, the role of nutrients versus grazing pressure in controlling phytoplankton in eutrophic lakes continues to be a controversial question among limnologists[37,45]. Planktivorous fish can depress nanophytoplankton predators, such as small cladocerans and rotifers, and thus allow nanophytoplankton to increase[46]. Arhonditsis et al.[47] found that phytoplankton dynamics in Lake Washington were controlled by available solar radiation, total phosphorus levels and grazing pressure. Zhang et al. [48] indicated that small-sized algae were principally regulated by bottom-up forces (nutriments), while large-sized phytoplankton was regulated both by top-down forces and competition with small-sized algae. However, Ortega-Mayagoitia et al.[49] indicated that planktivorous fish and zooplankton had minor importance in phytoplankton under hyper- eutrophic condition. Knowledge of phytoplankton community structure and dynamics, which is necessary to understand eutrophication effects, remains a research challenge[50]. Although general hypotheses and patterns of plankton succession are well described in temperate lakes, there are still few studies assessing the phytoplankton succession in shallow eutrophical lakes in China. The present study demonstrated that varying environmental variables in seasonality could strongly affect phytoplankton variability in Lake Baiyangdian. Also, it showed that phytoplankton community could generally be explained by an array of abiotic variables, despite the complexity of the environmental factors involved. The success of lake management and restoration projects depends on the detection of spatial and temporal changes in lake status that reflect changes in the surrounding environment. Sustained integrated monitoring of the water body for the purpose of modeling future changes is strongly recommended as an aid to formulating wise management policy for Lake Baiyangdian. Acknowledgments We would like to thank the Agency of Environmental Protection of Baoding for supplying the data of environmental variables. The investigations were funded by the project of research and development in Hebei Province, China (06276905); and by national 11th Fie-year magor science and technology project on control and prevention of water pollution, China (2008ZX07209-009-02). References [1]Khan TA. 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Evaluation of self-purification efficiency of Fuhe Stream-Baiyangdian Lake through zooplankton. Acta Hydrobiologica Sinica 1996; 20: 212-220. (in Chinese). [17] Xu MQ, Zhu J, Huang YY, Gao YR. The ecological degradation and restoration of Baiyandian Lake, China. J Freshwater Ecol 1998; 13: 433-446. [18] Ye CM, Gong AJ, Wang XJ, Zheng HH, Lei ZF. Distribution of atrazine in a crop-soil- groundwater system at Baiyangdian Lake area in China. J Environ Sci-China 2001;13: 148-152. (in Chinese) [19] Ma ZP, Jing AQ. A study on the water flow in Baiyangdian lake. Acta Mechanica Sinica 2004; 36: 589-95 .(in Chinese) [20] Liu SF, Li WY, Wen LQ. Research of Phytoplankton and Appraisement of Nutrition of the Lake Baiyangdian. Chinese Journal of Environmental Science 1995; S1: 11-13. (in Chinese) [21]Zhang YK, Tian YM, Zhang XS. Studies on phytoplankton in Lake Baiyangdian. Acta Hydrobiologica Sinica 1995; 19: 317-326. (in Chinese) [22] Feng JS. Appraisement of phytoplan kton and water quality of the Lake Baiyangdian. JiangSu Environmental Science and Technology 1999; 2: 27-29 (in Chinese). [23] Fathi AA, Flower RJ. Water quality and phytoplankton communities in Lake Qarun (Egypt). Aquat Sci 2005; 67: 350-362. [24] Dalian Hydrobiological Institute . Freshwater Biology. Beijing: Agricultural Press; 1982. (in Chinese) [25] Jin XC, Tu QY. The Standard Methods for Observation and Analysis in Lake Eutrophication, 2nd edn.. Beijing: Chinese Environmental & Scientific Press;1990. (in Chinese) [26] Han MS, Shu YF. Atlas of Freshwater Biology. Beijing: Oceanic Press; 1995. (in Chinese) [27] Li SH, Bi LJ. Flora Algarum Sinicarum Aquae Dulcis. Beijing: Chinese Scientific Press; 1998. (in Chinese) [28]Snit’ko L V, Rogozin A G. On the Assessment of the Structural Organization of Phytoplankton (Lake Bol’shoe Miassovo, the Southern Urals). Ekologiya 2002; 6: 426-431. [29] Holopainen A, Niinioja R, Rämö A. Seasonal succession, vertical distribution and long term variation of phytoplankton communities in two shallow forest lakes in eastern Finland. Hydrobiologia 2003, 506/509: 237-245. [30] Eker-Develi E, Kideys A E. Distribution of phytoplankton in the southern Black Sea in summer 1996, spring and autumn 1998. J Marine Syst 2003; 39: 203–211. [31] Aktan Y, Tüfekçi V, H. Tüfekçi H, Aykulu G. Distribution patterns, biomass estimates and diversity of phytoplankton in İzmit Bay (Turkey). Estuar, Coast and Shelf Science 2005: 64: 372- 384. [32] Hillebrand H, Durselen C D, Kirschtel D, Pollingher U, Zohary T. Biovolume calculation for pelagic and benthic microalgae. J Phycol 1999; 35: 403- 424 [33] Sun J, Liu D. Geometric Models for Calculating Cell Biovolume and Surface Area for Phytoplankton. J Plankton Res 2003; 25: 1331-1346. [34] Dokulil M T, Teubner K. Do phytoplankton communities correctly track trophic changes? An assessment using directly measured and palaeolimnological data. 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Procedia Environmental Sciences 2 (2010) 1622–1631
International Society for Environmental Information Sciences 2010 Annual Conference (ISEIS)
Seasonal variations of phytoplankton community structure in relation to physico-chemical factors in Lake Baiyangdian, China Cunqi Liua, Lusan Liub, Huitao ShenC,* a b c
College of life sciences,Hebei university,Baoding 071002,China
Chinese research academy of enviromrntal sciences, Beijing 100012,China
College of resource and enviroment, East China normal unviversity, Shanghai 200062,China
Abstract In order to study the seasonal variations in the structure and dynamics of phytoplankton community of the Lake Baiyangdian in China, quantitative samples were collected in each season during 2005. A total of 152 taxa were identified by microscope, phytoplankton had an abundance ranging from 131.25×10 4 ind. L-1 to 745.88×104 ind. L-1. The highest phytoplankton abundance were observed in spring and the lowest in autumn. Cyanophyta, Euglenophyta and Cryptophyta were the most important groups in terms of abundance and biomass. The preponderant and sub-preponderant species changed in accordance with the seasons. Canonical Correspondence Analysis (CCA) was used to investigate the relationship between environmental factors and phytoplankton community. The results indicated that nutrient gradient had a positive correlation with axis 1 in spring, summer and autumn. But in winter, the nutrient gradient had a positive correlation with axis 2. The varieties of environmental factors affected the structure of phytoplankton community in each season. CCA shows a slight, but significant method to understand the spatial distribution of phytoplankton community in lake systems.
© 2010 Published by Elsevier Ltd. Keywords
Baiyangdian; Phytoplankton; Environmental factors; Seasonal variations; Canonical Correspondence Analysis (CCA)
1 Introduction Phytoplankton plays an important role in lake ecosystems as they produce oxygen and food, which sustains all other forms of life [1,2]. Knowledge on their abundance and community composition can aid fishery management and provide further understanding of ecological balance. In different types of lakes, changes in the phytoplankton community have long been recognized as providing a good indicator of the trophic status and environmental quality [3,4]. Further more, various physico-chemical parameters are responsible for controlling phytoplankton growth and reproduction [5,6]. Actually, environmental instability and temporal and spatial changes determine the community present in a lake [7], how to recognize the composition and dynamics of phytoplankton is such a difficult problem. So many different multivariate analysis methods were widely used to appraise phytoplankton communities, such as Cluster Analysis [8], Canonical Correspondence Analysis [9,10]. Canonical Correspondence Analysis (CCA) was used to reduce data dimensions, determine the main environmental variables responsible for the fluctuations in the abundance and dynamics of phytoplankton community[4,11]. Lake Baiyangdian, which lies in the middle of Hebei Province, is the largest natural freshwater body in the North China Plain. It is a lake group of more than 140 lakes with a total area of approximately 366 km2 at water level of 10.5 m, and latitude and longitude coordinates of 38°44′ -49′ and 115°45′- 116°07′, respectively[12,13]. Baiyangdian is known as “the kidney of North China” which is important to support water circulation, to keep biological diversity, and to control pollution. It is closely linked with the North China people’s survival and development. Simultaneity, Lake Baiyangdian has historically been an important recreational site attracting a large number of visitors and an increasing number of permanent residents. Recently, due to low local precipitation, lack of inflows and serious pollution all combine to make Lake Baiyangdian especially sensitive and vulnerable system. The water quantity of Lake Baiyangdian has decreased heavily. The serious pollution of Baiyangdian influenced the community of phytoplankton. The lake has been studied since the 1990s by biologists, ecologists and hydrologists[15-19]. * Cunqi Liu. Tel.: +0-086-0312-5079364; fax: +0-086-0312-5079364. E-mail address: [email protected].
1878-0296 © 2010 Published by Elsevier doi:10.1016/j.proenv.2010.10.173
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Meanwhile, a few articles about the temporal and spatial distribution of phytoplankton in this area were published [20-22]. However, information about the physico-chemical conditions directly related to the phytoplankton community is still lacking. In the present study, we examined the phytoplankton succession during each season in 2005 in Lake Baiyangdian. The aim of the study was to identify the relationships between phytoplankton succession and their control factors and to increase our understanding of phytoplankton succession patterns in this lake. 2 Sites, materials and methods 2.1 Sites descriptions Eight sampling sites were relatively isolated from one another in Lake Baiyangdian to give good spatial representation of the whole characters and required navigating between sampling areas through narrow channels bordered by areas of emergent reeds (Fig. 1). Samples were collected in each season from April to November in 2005. Caipu Tai was farthest from the sewage sources, and considered to be the cleanest site. Duan Cun, Guang Dian, Quan Tou were fisheries, the aquiculture were so flourishing. Zaolin Zhuang was the exit of Lake Baiyangdian, and grew so much aquatic grass. Wangjia Zhai closed with the village and disturbed by people. Shaoche Dian was a lentic zone, where bred fish and crab. Nanliu Zhuang was closed to the Fuhe River and industrial inputs from Baoding City resulting in the highest nutrient level. 2.2 Sampling Subsurface water samples (1 L) for phytoplankton analysis were collected seasonally during 2005 from each site. Sample locations were consistent in each season. The water samples were fixed with Lugol’s solution for quantitative analysis. Each sample was then allowed to settle for at least 36 h, where upon the supernatant was carefully removed and the remaining volume was adjusted to 30 mL. This sample was kept at ćuntil analysis [23].
Fig.1 The sampling sites of Lake Baiyangdian in Hebei Province, China 1, Duan Cun; 2, Caipu Tai; 3, Quan Tou; 4, Guang Dian; 5, Zaolin Zhuang; 6, Wangjia Zhai; 7, Shaoche Dian; 8, Nanliu Zhuang The algal taxa were identified and counted according to these references, including Dalian Hydrobiological Institute [24], Jin[25], Han[26], Li[27], Snit’ko[28], Holopainen[29]. Biomass was estimated by biovolumes assuming 1μm3 equals to 1pg [30,31]. The cell volume of each algal species was calculated based on cell shape and size [32,33]. Water quality parameters were measured at all sampling stations by Agency of Environmental Protection of Baoding, Hebei Province. These parameters included pH, dissolved oxygen (DO), chemical oxygen demand (COD), biological oxygen demand (BOD), total phosphates (TP), total nitrogen (TN), ammonium nitrogen (NH4+-N), transparency (Secchi disk clarity) (Trans). Water transparency was estimated on each sampling occasion with a white Secchi disc [34](φ25 cm). Water samples were analysed for pH and the other nutrients following standard analytical techniques [35]. 2.3 Statistical analysis Canonical Correspondence Analysis (CCA) method was used to ascertain the relationships between phytoplankton and environmental variables along the study period. CCA extracts synthetic gradients from the biotic and environmental matrices and
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the explanatory variables are quantitatively represented by arrows in graphical biplots[36]. The arrow direction indicates positive or negative correlations and their length is relative to the importance of the explanatory variable in the ordination[37]. For statistical analysis of data, each species with more than 1% of total phytoplankton density, which also apperaed at least in three seasons and 4 sites during the study period, was considered (Table 2). Species data were log (X+1) transformed before analysis to obtain normal distribution[9]. Environmental variables except pH were also log (X+1) transformed. 3 Results 3.1 Physico-chemical characteristics Table 1 shows the mean physico-chemical values as well as the standard error (S.E.) of the investigated sites of Lake Baiyangdian in 2005. The pH was always alkaline in each season and fluctuated between 8.028 (November) and 8.436 (April). The mean dissolved oxygen (DO) concentrations were 8.2 mg/L, respectively, over the 4 seasons with the highest value 8.925 mg/L appearing in spring and lowest value 6.39 mg/L in autumn. The seasonal distribution of SD was 71.875 cm in April, 59.375 cm in July, 78.75 cm in September and 67.5 cm in November respectively. Biochemical oxygen demand (BOD) content fluctuated from 5.195 mg/L in spring to 6.581 mg/L in summer. The mean TN and NH4+-N concentrations were 4.038 mg/L and 2.205 mg/L, respectively. The concentrations of TN and NH4+-N showed similar seasonal variance with the highest concentrations in autumn, followed in a descending order by winter, spring and summer. Total phosphorus (TP) attained a maximum concentration in November (0.183 mg/L) and decreased to a minimum in July (0.085 mg/L). Average chemical oxygen demand (COD) content reached a maximum in September (31.138 mg/L) and a minimum value (28.063 mg/L) was recorded in July. The causal link was demonstrated in the correlation analysis (Table 2). There was no clear link between pH and the other environmental variables. Dissolved oxygen was positively correlated with transparency, but negatively with other nutrients (i.e. COD, BOD, TP et al.). Of all the nutrients variables were significantly correlated by twos in the data set. It was likely that transparency at these sites was limited by a combination of variables, involving TP, TN and NH4+-N, and TN concentrations seemed to be most important. Table 1 Mean and range of nutrient concentrations in Lake Baiyangdian, 2005 Date/Parameter Apr.2005 Jul.2005 Sep.2005 Nov.2005
Mean Range Mean Range Mean Range Mean Range
Date/Parameter Apr.2005 Jul.2005 Sep.2005 Nov.2005
pH
DO(mg/L)
SD(cm)
BOD(mg/L)
8.436±0.110* 8.03~9.04 8.289±0.101 7.76~8.68 8.369±0.055 8.15~8.54 8.028±0.119 7.3~8.4
8.925±1.373 2.14~15.4 8.9±1.109 5~12.6 6.39±0.680 2.06~8.3 8.619±1.158 1.04~11.53
71.875±13.227 30~150 59.375±10.541 30~120 78.75±12.598 40~150 67.5±14.064 15~134
5.195±1.074 3.8~12.5 6.581±2.000 2.05~19.3 5.288±2.053 2~19.3 5.748±0.907 3.93~11.9
TP(mg/L)
COD(mg/L)
0.143±0.091 0.019~0.779 0.085±0.027 0.027~0.262 0.087±0.027 0.023~0.268 0.183±0.116 0.007~0.973
29.45±6.631 13.8~73.7 28.0625±3.474 19.4~50.6 31.138±4.782 23.8~64.2 30.213±6.287 18.1~72.9
TN(mg/L) Mean Range Mean Range Mean Range Mean Range
NH4+-N(mg/L)
2.651±1.177 0.855~10.8 1.974±0.455 1.1~5.11 7.508±3.047 0.74~27.2 4.020±2.593 0.619~22
1.493±0.954 0.513~8.17 0.984±0.328 0.482~3.25 3.280±2.775 0.289~22.7 3.062±2.507 0.318~20.6
* Mean ± Standard Error (S.E.) Table 2 Correlation coefficients of environmental variables
pH DO
DO 0.449
COD -0.393 -0.944**
BOD -0.512 -0.932**
TP -0.439 -0.943**
TN -0.332 -0.866**
NH4+-N -0.413 -0.926**
SD 0.329 0.682
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COD 0.973** 0.972** 0.945** BOD 0.942** 0.893** TP 0.929** TN NH4*-N SD Date except pH were log transformed before analysis. (* for P˘0.05, ** for P˘0.01)
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0.973** 0.948** 0.993** 0.909**
-0.689 -0.580 -0.790* -0.795* -0.730*
3.2 Phytoplankton composition, abundance and biomass During the study, 152 species of eight algal classes, Chlorophyta (52.6%), Cyanophyta (18.4%), Bacillariophyta (13.2%), Eugleno- phyta (8.6%), Pyrrophyta (2.6%), Xanthophyta (2%), Cryptophyta (2%) and Chrysophyta (0.6%), were identified. Cyanophyta, Euglenophyta and Cryptophyta were the most important groups in terms of abundance and biomass than the other taxonomic groups. Raphidiopsis sinensia, Merismopedia tenuissima of Cyanophyta, Cryptomonas ovata, Cryptomonas marssonii of Cryptophyta, Euglena caudata from Euglenophyta were the most common species throughout the year. There were marked seasonal and spatial differences in the quantitative composition and biomass of phytoplankton communities (Table 3). Throughout the sampling period, April 2005 was the month in which mean phytoplankton reached its maximal values (745.9h104 ind. L-1 in abundance and 18.43 mg L-1 in biomass). C. ovata and C. marssonii from Cryptophyta were the most abundant species of all the sampling sites. The both species contributed to the high biovolume records due to their big cell-sizes. Also, Chlamydomonas sp. from Chlorophyta and Synedra sp. from Bacillariophyta were present in large amounts, but no apparent increases were recorded in biomass. July 2005 was another significant month in terms of mean phytoplankton abundance and biomass. R. sinensia, Merismopedia glauca and Lyngbya limnetica from Cyanophyta, Chlamydomonas sp. and Chlorella vulgaris from Chlorophyta were common at all stations. In the sampling of September 2005, a significant decrease was recorded in abundance and biomass of phytoplankton. Mean phytoplankton density (131.3h104 ind. L-1) reached minimal values in the four seasons. C. vulgaris and Synedra sp. were noted in phytoplankton density, but were not important in biomass. In winter (November 2005), an increase was recorded in terms of phytoplankton abundance than September. Whereas, its mean biomass values reached lowest (3.08 mg L-1). C. ovata, C. marssonii, L. limnetica and C. vulgaris were important species in terms of abundance and frequency (100% among stations). L. limnetica and C. vulgaris were present in large amounts at all stations in terms of density. C. ovata and C. marssonii were important in total biomass despite small density numbers. During the study period, the nutrient concen- trations in Nanliu Zhuang were obviously higher than the other sites due to the inputs of sewage discharge and industrial contaminants from Baoding city, this made the phytoplankton abundance and biomass the most. It has been well documented that nutrient enrichment resulted in high algal densities[38]. Duan Cun and Quan Tou were fisheries, both of them closed to the village as Wangjia Zhai did. All of these stations received non-point polluted sources (agricultural runoff and human waste) and had the similar abundance and biomass. The sites of Guang Dian, Zaolin Zhuang and Shaoche Dian grew so much aquatic plants, such as reeds, lotus and cattail et al. Phytoplankton abundance and biomass in these three sites were less than the anterior four sampling sites. Caipu Tai ranked the last in abundance and biomass. This site was far away from the village and sewage river, the nutrient concentrations was the lowest of all the sites. Table 3 Phytoplankton abundance and biomass in Lake Baiyangdian in 2005 (Abundance: ×104 ind. L-1; biomass: ×mg L-1) Data/Sites Apr. Jul. Sept. Nov. Mean
1
2
3
4
5
6
7
8
Mean
814.2
39.6
159.3
207.5
117.6
165.8
90.6
4372.5
745.9
(23.36)*
(0.70)
(1.95)
(2.47)
(1.58)
(1.93)
(1.04)
(114.42)
(18.43)
319.2
76.5
1185.5
185.7
183.9
867
201.8
1643.9
582.9
(6.54)
(1.86)
(25.47)
(6.07)
(4.67)
(38.81)
(6.62)
(4.03)
(11.76)
90.5
34.5
152.9
167.6
54.2
267.6
169.7
113.3
131.3
(1.66)
(0.93)
(2.73)
(4.62)
(1.97)
(5.50)
(10.61)
(0.75)
(3.60)
156.2
25.5
604.8
198.2
154.5
198.9
179
17.7
191.8
(2.65)
(0.46)
(8.73)
(2.63)
(2.66)
(3.76)
(3.26)
(0.48)
(3.08)
345
44
525.6
189.7
127.5
374.8
160.2
1536.8
413
(8.55)
(0.99)
(9.72)
(3.95)
(2.72)
(12.50)
(5.39)
(29.92)
(9.22)
* For phytoplankton biomass. 3.3 Correlations and CCA analysis
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Main environmental variables responsible for the greatest phytoplankton community variability were identified with CCA that simultaneously represented environmental variables in a bidimensional space [11]. Total phytoplankton abundance was positively with COD, TP, TN and NH4+-N, but negatively with DO and SD (Table 4). Forward selection indicated that six of the eight physico-chemical variables (except for pH and BOD) made independent and significant contributions to the variance in phytoplankton data (P˘0.05). Consequently, 27 biotic and 6 abiotic variables were used in present CCA. The codes of phytoplankton were showed in Table 5. The statistical information of CCA analysis was shown in Table 6. Results strictly represented the existing relationships between environmental variables and phytoplankton species. The eigenvalues and the percentage for variance of each season in axis1 were much higher than axis 2. Species-environmental correlation for axis 1 and 2 was high, indicating a strong correlation between phytoplankton species distribution and environment variables used for ordination. Table 4 Correlation coefficients between phytoplankton abundance and environmental variables pH
DO
COD
Abundance -0.704* 0.709* * for P˘0.05, ** for P˘0.01, - not significant.
BOD
TP
TN
NH4+-N
SD
-
0.771*
0.765*
0.710*
-0.952**
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Table 5 Codes of phytoplankton species for CCA Codes s1 s2 s3 s4 s5 s6 s7 s8 s9 s10 s11 s12 s13 s14 s15 s16 s17 s18 s19 s20 s21 s22 s23 s24 s25 s26 s27
Phytoplankton Scenedesmus quadricauda Scenedesmus bijuga Chlamdam onas sp. Westella botryoides Kirchneriella sp. Cosmarium quadrum Chlorella vulgaris Dictyosphaerium pulchellum Coelastrum sphaericum Crucigenia rectangularis Crucigenia tetrapedia Lyngbya limnetica Gloeocapsa minima Dactylococcopsis irregularis Phormidium tenue Merismopedia tenuissima Gloeocapsa turgida Microcystis pulverea Oscillatoria tenuis Synedra sp. Navicula sp. Cyclotella sp. Euglena caudata Euglena acus Trachelomonas ensifera Cryptomonas ovata Cryptomonas Marssonii
Spring + + + + + + + + +
+ + + +
+ + + +
+ +
Summer + + + + + + + + + + + + + + + + + + + + + + + + + +
Autumn + + + + + + + + + + + + + + + + + + + + +
Winter + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + +
Table 6 Statistics for the first two axes of CCA performed on phytoplankton at Lake Baiyangdian Axis Season Eigenvalues Percentage
Spring 0.165 58.0
Axis1 Summer Autumn 0.157 0.122 52.5 34.6
Winter 0.157 39.1
Spring 0.059 20.7
Summer 0.057 19.1
Axis2 Autumn 0.103 29.4
Winter 0.095 23.8
Cumulative Percentage for variance
58.0
52.5
34.6
39.1
78.7
71.6
64.0
62.9
Species -environment correlations
0ˊ99 8
0.994
1.000
1.000
0.993
0.881
0.997
1.000
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Fig. 2 Spatial ordination resulting from CCA of most abundant phytoplankton species with respect to physical-chemical parameters. See Table 5 for phytoplankton genus name codes. Fig. 2 showed the CCA sample biplots in relation to environmental gradients potentially influencing the phytoplankton. The length of the environmental variable arrows represented the relative explanatory of each variable in relation to individual sample positions within the ordination. TP, TN, NH4+-N and COD had positive correlations with axis1 in spring, summer and autumn. Nevertheless, SD had a negative correlation with axis 1. In spring, several phytoplankton species such as Westella botryoides, Kirchneriella sp., Dictyosphaerium pulchellum, Coelastrum sphaericum, Gloeocapsa minima, Dactylococcopsis irregularis, Phormidium tenue et al., which distributed at the right side of Axis1, were positively correlated with the content of nutriments such as TP and TN. Nevertheless, some phytoplankton species such as Crucigenia tetrapedia, G. minima, D. irregularis, P. tenue, M. tenuissima, Gloeocapsa turgida and Microcystis pulverea, distributed at the left side of axis1 in summer and autumn. These species were negatively correlated with SD. In winter, axis 2 was positively correlated with TN and TP. The strong influence of nutriments was seen on the species plot which showed Euglenophyta, like C. ovata and C. marssonii, associated with high content of TP and TN. From the CCA biplots, we considered that the number of phytoplankton species and abundances were well adapted to changing environmental parameters. The rapidly changing nutrient status had resulted in major shifts in community composition and magnitude of phytoplankton. 4 Discussion Investigation of the structure and function of phytoplankton communities are of utmost importance for studies of the lacustrine ecosystems’ dynamics[11]. Long-term phytoplankton succession has been well investigated in aquatic ecology in China[41]. Strong annual variation in the physical, chemical and biological characteristic of lakes seems play a regulatory role on phytoplankton dynamics, and annual variation in nutrient supply is an important determinant of phytoplankton variability. Previous studies of the Lake Baiyangdian concerning the phytoplankton composition [40,21] identified 129 taxa of phytoplankton in 1958 and 408 in 1995 respectively. Among the most relevant aspects of such studies were those related to population abundance and diversity, spatial and temporal distribution. Present study reported the spatial and temporal variation
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of phytoplankton in this area, as well as up to what extent the main environmental factors were responsible for the system’s phytoplankton variability. During the study period, a total of 152 taxa were identified and less than the year of 1995. The phytoplankton was dominated by Cryptophyta in early spring. The summer period showed Cyanophyta and Chlorophyta dominance (when the oxygen depletion and a large fish kills occured). The dominance of Cyanophyta, Cryptophyta and Chlorophyta was found in the autumn-winter period. Abundances and composition of phytoplankton varied significantly through the seasons in Lake Baiyangdian. Generally, differences in phytoplankton biomass and composition have been found between lakes of different trophic lakes [39, 23]. Nutrient-rich lakes of temperate areas are usually dominated by diatoms and blue-green algae whereas the commonest phytoplankton organisms in poor environments are nano- planktonic species and flagellates[42]. It is well known that changes in physico-chemical characteristics of any lake can lead to concomitant qualitative and quantitative changes in phytoplankton communities[2]. The physico-chemical factors of Lake Baiyangdian varied seasonally during the study period. The CCA ordination reflected the corresponding correlations between phytoplankton communities and major environmental variables. The ordination biplots showed that the environmental variables in turn influenced the dynamics of phytoplankton. From winter to early spring, the nutrients content such as phosphorus and nitrogen increased, which promoted the growth of phytoplankton. As a consequence, we observed an increase of phytoplankton in early spring. At the same time, the spring growth of algae is firstly dominated by Cyanophyta. Nutrient limitation was a common characteristic during summer. The CCA analysis presented a limitation of nutrients in summer and autumn. We could see that massive of phytoplankton were far away from the arrow of nutrients in these two seasons. In winter, the reduction of light energy resulted in phytoplankton abundances decreased, which is associated to cool winter temperatures. In addition, the nutrients had positively correlation with the second CCA axis, and Cryptophyt had a greater pertinence with nutrients content. The CCA performed on the data revealed that the phytoplankton abundances had obvious dynamics and were mainly regulated by DO, TP and NH4+-N. But different lakes had diverse influencing factors. Habib et al. [3]showed that Silicate was more important than nitrate and phosphate as an environmental determination influencing phytoplankton assemble in Lake Loch Lomond. Zhang et al. [43] studied driving forces shaping phytoplankton assemblages in two subtropical plateau lakes in China. The results suggested that the major driving forces in Lake Fuxian were physical variables, and particularly the underwater light climate, whereas, nutrients were the important driving force in Lake Xingyun. The nutrient supply and its ratios have a decisive effect on the species composition of the phytoplankton since different algal species have different nutrient requirements[44]. On the other hand, the role of nutrients versus grazing pressure in controlling phytoplankton in eutrophic lakes continues to be a controversial question among limnologists[37,45]. Planktivorous fish can depress nanophytoplankton predators, such as small cladocerans and rotifers, and thus allow nanophytoplankton to increase[46]. Arhonditsis et al.[47] found that phytoplankton dynamics in Lake Washington were controlled by available solar radiation, total phosphorus levels and grazing pressure. Zhang et al. [48] indicated that small-sized algae were principally regulated by bottom-up forces (nutriments), while large-sized phytoplankton was regulated both by top-down forces and competition with small-sized algae. However, Ortega-Mayagoitia et al.[49] indicated that planktivorous fish and zooplankton had minor importance in phytoplankton under hyper- eutrophic condition. Knowledge of phytoplankton community structure and dynamics, which is necessary to understand eutrophication effects, remains a research challenge[50]. Although general hypotheses and patterns of plankton succession are well described in temperate lakes, there are still few studies assessing the phytoplankton succession in shallow eutrophical lakes in China. The present study demonstrated that varying environmental variables in seasonality could strongly affect phytoplankton variability in Lake Baiyangdian. Also, it showed that phytoplankton community could generally be explained by an array of abiotic variables, despite the complexity of the environmental factors involved. The success of lake management and restoration projects depends on the detection of spatial and temporal changes in lake status that reflect changes in the surrounding environment. Sustained integrated monitoring of the water body for the purpose of modeling future changes is strongly recommended as an aid to formulating wise management policy for Lake Baiyangdian. Acknowledgments We would like to thank the Agency of Environmental Protection of Baoding for supplying the data of environmental variables. The investigations were funded by the project of research and development in Hebei Province, China (06276905); and by national 11th Fie-year magor science and technology project on control and prevention of water pollution, China (2008ZX07209-009-02). References [1]Khan TA. 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