Chinese Journal of Oceanology and Limnology Vol. 32 No. 2, P. 255-265, 2014 http://dx.doi.org/10.1007/s00343-014-3158-7
Dynamics of phytoplankton communities in the Jiangdong Reservoir of Jiulong River, Fujian, South China* TIAN Yongqiang (田永强), HUANG Bangqin (黄邦钦)**, YU Chaochao (俞超超), CHEN Nengwang (陈能汪), HONG Huasheng (洪华生) Key Laboratory of Coastal and Wetland Ecosystems, Ministry of Education; Fujian Provincial Key Laboratory of Coastal Ecology and Environmental Studies, Xiamen University, Xiamen 361005, China Received May 12, 2013; accepted in principle Jun. 22, 2013; accepted for publication Jul. 26, 2013 © Chinese Society for Oceanology and Limnology, Science Press, and Springer-Verlag Berlin Heidelberg 2014
Abstract Phytoplankton blooms occurring in the Jiangdong Reservoir of Jiulong River, Fujian Province, South China, are a potential source of contamination of the drinking water of Xiamen (Amoy) City. To understand the main factors governing phytoplankton composition and succession, we sampled phytoplankton and measured environmental parameters in the reservoir, weekly or biweekly from Jan. 2010 to Feb. 2012. We identified 123 species of phytoplankton from 7 phyla and 74 genera. The major phyla were Chlorophyta, Bacillariophyta, Cryptophyta, Cyanophyta, and Dinophyta. The main trend in the succession of phytoplankton was from prevalence of Cryptophyta-Bacillariophyta communities to those of Chlorophyta-Cyanophyta. High cell concentrations of Cryptophyta, predominantly Komma caudate, Cryptomonas marssonii, and Cryptomonas erosa, were present in winter, associated with low river discharge and cold water. Bacillariophyta, primarily Cyclotella meneghiniana, Aulacoseira granulata, and Aulacoseira granulata var. angustissima, dominated in early spring, coinciding with high turbulence and low irradiance. During early summer and autumn, Chlorophyta, comprising Scenedesmus quadricauda, Dictyosphaerium ehrenbergianum, and Pandorina sp. were prevalent during conditions of warmer water temperatures and low turbulence. Cyanophyta, with dominance of Pseudanabaena mucicola, Merismopedia tenuissima and Raphidiopsis sp. increased throughout the summer, coinciding with higher water temperatures and lower nutrient concentrations. Dinophyta content was occasionally high during winter and summer. Peridiniopsis penardii (Dinophyta) bloomed during winter 2009, with a persistently high biomass recorded into early spring. Canonical correspondence analysis indicated that phytoplankton communities were influenced by river discharge, irradiance, water temperature, and nutrient concentrations. Keyword: phytoplankton composition; community succession; environmental factors; canonical correspondence analysis (CCA); Jiulong River
1 INTRODUCTION Phytoplankton are important contributors to primary production in aquatic ecosystems. However, they can also cause algal blooms, which damage ecosystem health. In recent years, algal blooms have occurred with increasing frequency in freshwater Chinese lakes and rivers (Xu et al., 2009; Liu et al., 2010). Environmental factors, such as water temperature, nutrients, irradiance, and zooplankton grazing, can influence phytoplankton biomass, composition and succession in aquatic systems (Shapiro, 1997;
Salmaso and Braioni, 2008). Seasonal temperature variations are small in tropical zones. At these latitudes, dynamics of phytoplankton communities in freshwater systems are primarily related to seasonal monsoon rains (Ibañez, 1998). For example, heavy rainfall reduces water column light attenuation, resulting in a decrease in phytoplankton biomass.
* Supported by the Bureau of Science and Technology of Xiamen Municipal Government, China (No. 3502Z20091005), the National Natural Science Foundation of China (No. 40925018), and the State Oceanic Administration (SOA) Program (No. 201105021) ** Corresponding author:
[email protected]
CHIN. J. OCEANOL. LIMNOL., 32(2), 2014
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This causes a successional shift in the phytoplankton community structure (Chellappa et al., 2008). In subtropical zones, seasonal changes in temperature and hydrological variables are more pronounced, and can have a greater impact on the phytoplankton composition and succession in freshwater ecosystems (Grover and Chrzanowski, 2006). In reservoirs, factors affecting phytoplankton growth are relatively more complex because such systems integrate the characteristics of both rivers and lakes. Changes in reservoir morphology and current flow may cause variations in available light and nutrients, contributing to the seasonal succession of phytoplankton in these ecosystems (Kimmel et al., 1990). Phytoplankton growth and dynamics in reservoirs are affected by climate change, hydraulic conditions, geographical and morphological structure, size, nutrient structure, hydraulic retention time and operation (Tundisi et al., 1993). In the Three-Gorges region, since the construction of the huge dam in the upper reach of the Changjiang (Yangtze) River, there has been an increasing concern for the gradual rise in the level of eutrophication within the river’s tributaries. The frequency of algal blooms within the Three-Gorges Reservoir and tributaries has also increased (Zeng et al., 2006). In the upstream section of the Xiangxi River, studies have demonstrated significant correlations between phytoplankton growth and the concentrations of nutrients, such as nitrate and silicate (Zeng et al., 2006), total phosphorus and total nitrogen (Ye et al., 2006) and dissolved silicate and dissolved inorganic nitrogen (Ye et al., 2007). In the Changjiang River, Zeng et al. (2007) indicated that phytoplankton abundance was negatively correlated with river discharge, and that phytoplankton abundance and biomass were mainly determined by the hydrological conditions. The Jiangdong Reservoir at Beixi Creek, Jiulong River, Fujian Province, China, is an important source of drinking water, accounting for about 80% of the water supply to the economically important city of Xiamen (South China). In recent years, the water quality of the Jiulong River has deteriorated because of climate change and human activities. Phytoplankton blooms have accompanied the changes in water quality. During January 2009, a bloom of Peridiniopsis penardii (Dinophyta, non-toxic) occurred in a stream segment over 20 km long in the Jiangdong Reservoir, which threatened the drinking water supply in Xiamen. To date, few studies have been carried out on the seasonal variations of phytoplankton in Jiulong
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Fig.1 Location of sampling site (SK) in the Jiangdong Reservoir at Beixi Creek, Jiulong River, Fujian Province from January 2010 to February 2012
River or Jiangdong Reservoir. Therefore, it is necessary to investigate and understand the phytoplankton community succession and dynamics in the reservoir and to establish a monitoring system to guarantee water safety. Based on the characteristics and succession of the dominant phyla, the main factors influencing phytoplankton succession were examined. The results presented herein will serve as a baseline for the management of water quality in the Jiangdong Reservoir and other similar water bodies.
2 MATERIAL AND METHOD 2.1 Sampling site The Jiulong River crosses the southern and central subtropical zone of the western Fujian Province. The watershed of the river is influenced by a subtropical oceanic climate (e.g. monsoon-controlled), and is exposed to significant temporal variations in temperature and precipitation. Beixi Creek, with a drainage area of 9 640 km2, is one of the two main tributaries of the Jiulong River. The Jiangdong Reservoir, centrally located downstream of Beixi Creek, is the largest flood prevention project in the Fujian Province. In this study, we sampled phytoplankton weekly or biweekly at the drinking water intake (site SK) from January 2010 to February 2012 (Fig.1).
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TIAN et al.: Phytoplankton in the Jiulong River, Fujian Province, China
2.2 Sample collection, identification, measurement of phytoplankton biomass
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Qualitative samples were collected via plankton net (20 μm mesh bolting-silk) and preserved in 4% formalin for analysis. A small amount of each preserved sample was observed microscopically (400× or 1 000× magnification; Nikon 90i), to identify phytoplankton species according to Hu and Wei (2006). Quantitative samples (1 L) were collected with an organic-glass sampler at the surface layer and immediately preserved with 1% Lugol’s iodine solution. The samples were deposited for 48 hours and subsequently concentrated to 30 mL (APHA, 1992). After mixing, 0.1 mL of concentrated sample was placed onto a counting chamber and enumerated with an optical microscope (400× magnification). Phytoplankton geometric shapes were measured to obtain the approximate cell volume. From this, the biomass (wet weight) was derived assuming a wet weight density of 1 g/cm3 (Wetael and Likens, 2000). 2.3 Environmental data measurements Environmental data were provided by the Xiamen Environmental Monitoring Central Station. Water temperature (T), dissolved oxygen (DO) and pH were measured using an YSI-85 water quality meter. Water transparency was measured in situ using a Secchi disk. Detection methodologies for nutrients followed the protocols of the State Environmental Protection Bureau (SEPB, 2002). Total nitrogen (TN) was determined by the alkaline potassium persulfate digestion, using UV spectrophotometry. Ammonia nitrogen (NH+4-N) was detected by Nessler’s reagent spectrophotometry. Nitrate nitrogen (NOˉ3-N) and nitrite nitrogen (NOˉ2-N) were determined by ion chromatography. Dissolved inorganic nitrogen (DIN) was estimated from the sum of three types of inorganic nitrogen (NOˉ3-N, NH+4-N, and NOˉ2-N). Soluble reactive phosphorus (SRP) and total phosphorus (TP) content were measured by the phosphomolybdenum blue spectrophotometric method and the ammonium molybdate spectrophotometric method, respectively. The average daily discharges of the reservoir were provided by the Taiwan Strait Data Center. 2.4 Data analysis Principal component analysis (PCA) was used to select the variables that made significant and independent contributions to the phytoplankton distribution. Canonical correspondence analysis
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(CCA) was carried out between the significant environment variables and the dominant species of phytoplankton. Prior to CCA, all data were logarithmically transformed to meet the assumptions of normality and homogeneity of variances (NaselliFlores and Barone, 1998). The significance (P
