Effects of fish farming on phytoplankton community under the thermal stress caused by a power plant in a eutrophic, semi-enclosed bay: Induce toxic dinoflagellate (Prorocentrum minimum) blooms in cold seasons

Marine Pollution Bulletin 76 (2013) 315–324

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Effects of fish farming on phytoplankton community under the thermal stress caused by a power plant in a eutrophic, semi-enclosed bay: Induce toxic dinoflagellate (Prorocentrum minimum) blooms in cold seasons Zhibing Jiang a,b,c, Yibo Liao b,c, Jingjing Liu b, Lu Shou b, Quanzhen Chen b, Xiaojun Yan c, Genhai Zhu b, Jiangning Zeng a,b,⇑ a

State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, State Oceanic Administration, No. 36 Baochubei Road, 310012 Hangzhou, China Key Laboratory of Marine Ecosystem and Biogeochemistry, Second Institute of Oceanography, State Oceanic Administration, No. 36 Baochubei Road, 310012 Hangzhou, China c Key Laboratory of Applied Marine Biotechnology, Ministry of Education, Marine College of Ningbo University, No. 818 Fenghua Road, 315211 Ningbo, China b

a r t i c l e

i n f o

Keywords: Fish farming Thermal discharge Phytoplankton Eutrophication Prorocentrum minimum Xiangshan Bay

a b s t r a c t Six cruises were conducted in a fish farm adjacent to the Ninghai Power Plant in Xiangshan Bay, East China Sea. Fish farming significantly increased NHþ 4 , DIP, and TOC concentrations, while it significantly decreased the DO level. These increase/decrease trends were more pronounced in warmer seasons. Although culture practices did not significantly increase phytoplankton density, it drastically enhanced dinoflagellate abundance and domination. Significant differences in species diversity and community composition between the cages and the control area were also observed. Temperature elevation caused by thermal discharge associated with eutrophication resulted in a dominant species shift from diatoms alone to dinoflagellates and diatoms. This is the first report of stress-induced toxic dinoflagellate (Prorocentrum minimum) blooms in winter and the winter–spring transition in this bay. Therefore, the effects of aquaculture activity and power plant construction in such a eutrophic, semi-enclosed bay require further attention. Ó 2013 Elsevier Ltd. All rights reserved.

1. Introduction Mariculture has expanded in scale globally and supplies abundant seafood for human consumption, relieving pressure on food supplies and ocean fisheries (Naylor et al., 2000). Likewise, China’s mariculture industry has been growing steadily with the transformation of traditional fishing since the 1980s. Currently, China is the biggest aquaculture country in the world. Such rapid development of mariculture provides enormous nutritional and economic benefits, and decreases the intensity of exploitation on declining wild resources (Yang et al., 2004), thus alleviating China’s food pressures caused by population growth. However, the haphazard development and excessive exploitation of aquaculture have potentially negative environmental effects, particularly with fish culture, which requires a lot of nutrient (diet) and energy input (Dong et al., 2008; Yang et al., 2004; Jiang et al., 2012). The negative effects of fish farming on the environment are highest compared with various other aquaculture systems (e.g. ⇑ Corresponding author at: Key Laboratory of Marine Ecosystem and Biogeochemistry, Second Institute of Oceanography, State Oceanic Administration, No. 36 Baochubei Road, 310012 Hangzhou, China. Tel.: +86 571 81963227; fax: +86 571 88071539. E-mail address: [email protected] (J. Zeng). 0025-326X/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.marpolbul.2013.07.006

seaweed, mollusks, and crustaceans), although the economic benefit might be the highest (Jiang et al., 2012). Intense fish cages generate considerable particulate organic wastes and soluble inorganic wastes (Alongi et al., 2009; Carroll et al., 2003; Dalsgaard and Krause-Jensen, 2006; Dong et al., 2008; Lauer et al., 2009; Modica et al., 2006; Sara, 2007; Wu, 1995). The huge terrestrial nutrient input combined with the nitrogen (N) and phosphorus (P) discharging from intensive mariculture have led to eutrophication intensification in major Chinese coastal systems (Xiao et al., 2007; Yang et al., 2004), particularly in semi-enclosed waters. The symptoms of eutrophication can cause a succession of serious losses in the ecological, economic, and social benefits of coastal waters (Bricker et al., 2008). Phytoplankton play a key role in the assimilation of organic matter and excess nutrient inputs in the water column from farming, these primary producers are then grazed by groups higher in the trophic chain (e.g., ciliates, zooplankton, and shellfish) (Azim et al., 2003; Alongi et al., 2009; Olsen et al., 2007; Pitta et al., 2009; Silva et al., 2012). Nevertheless, eutrophic levels and the alteration of nutrient content composition (proportion) induce harmful algal blooms (HABs) (Buschmann et al., 2006; David et al., 2009), which in turn affect the caged fish (San Diego-McGlone et al., 2008). The effects of fish cages on marine phytoplankton (Navarro et al., 2008; San Diego-McGlone et al., 2008; Sidik et al.,

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2008; Skejic´ et al., 2011; Wang et al., 2006, 2009) and nutrient distribution (Alongi et al., 2003; La Rosa et al., 2002; Neofitou and Klaoudatos, 2008; Pitta et al., 2006, 2009; Wu, 1995; Yucel-Gier et al., 2008) has been well documented. But few of these studies completely focused on phytoplankton community structure and its relationship with environmental parameters under mariculture stress. Furthermore, many coastal power plants have been constructed in China recently (Jiang et al., 2009), and their thermal discharges promote microalgal growth in aquaculture regions adjacent to receiving waters (Jiang et al., 2012). However, almost no research has been done to explore the phytoplankton distribution in fish cages near these power plants. Therefore, it is necessary to investigate the response of phytoplankton assemblages to fish farming under thermal stress. Fish farming in Xiangshan Bay (XSB, a subtropical, eutrophic, semi-enclosed bay), East China Sea (ECS) has expanded in the last three decades (Ning and Hu, 2002; You and Jiao, 2011; Jiang et al., 2013). In addition, the Ninghai Power Plant, located in the inner bay, began operations on the 17th December 2005. In this context, we hypothesized that fish farming and thermal stress would change the phytoplankton community structure and exacerbate algal blooms in cage areas adjacent to the power plant. To test this hypothesis, we chose a fish farm close to the plant. We then examined the spatio-temporal distribution of phytoplankton abundance, species richness, diversity, evenness, dominant species, and community structure as well as the chemical parameters of the water column in the fish cages (FC) and the control area (CA) from 2009 to 2010. Our objectives were (1) to explore the spatio-temporal distribution of phytoplankton assemblages and water chemistry and to ascertain their relationship in the fish farm and surrounding waters and (2) to evaluate the combined effects of fish farming and thermal discharge on phytoplankton community composition and structure.

90% water exchanges, respectively) (Ning and Hu, 2002). Six cruises were conducted in the FC and CA during winter (31/01/ 2010), winter–spring transition (WST, 29/02/2009), spring (23/ 04/2010), early summer (14/07/2010), midsummer (04/08/2009), and autumn (16/11/2010). This farm mainly consisted of Japanese seaperch (Lateolabrax japonicus) and black seabream (Sparus macrocephalus) with acreage of about 18.7 ha. Two stations were set in the center of the fish farm and 1000 m south of the farm (control station, Fig. 1). The sampling areas were surrounded by water with 0.5–1 °C temperature elevation caused by the large (82.5 m3 s1) thermal discharges (Jiang et al., 2013). According to our unpublished measured data, the surface temperature at the station 100 m away from the outlet was much (about 10 °C in winter and 8 °C in summer) higher than that at the inlet.

2. Materials and methods

Surface and bottom water (400–500 mL, each 3 samples) were collected in 500-mL bottles at each station. All samples collected were stored with 2% formalin. After sedimentation (at least 48 h), identification and counting of phytoplankton taxa were carried out on a scaled slide (0.1 mL) under 200 or 400 using a light microscope (Leica DM2500, Leica Microsystems GmbH, Wetzlar, Germany) and at least 300 units (individual cells or colonies) were counted for each sample, according to the morphological classification (Yamaji, 1966; Jin et al., 1982, 1991; Tomas, 1997; Guo and

2.1. Study area and sample sites The XSB (121°250 –122°300 E, 29°250 –29°470 N, Fig. 1) is located in Northern Zhejiang Province, China with a tidal flat area of 198 km2 and water area of 365 km2. It is also a long (ca. 60 km in length), narrow embayment connected to the ECS, with long residence times in the inner and middle sections (about 80 and 60 days for

2.2. Environmental parameters Surface (0.5 m depth) and bottom (0.5 m from the bottom) water were collected at each station in 10-L plastic buckets. Water depth, pH, temperature, and salinity were monitored in situ. Water temperature and salinity were measured with a YSI model 30 salinity meter (YSI Inc., Yellow Springs, OH, USA), turbidity with a Secchi disc, dissolved oxygen (DO) by Winkler titrations, and pH with an Orion 868 acidity meter (Thermo Electron Co., Waltham, MA, USA). For detecting other parameters, including dissolved silicate (DSi), dissolved inorganic nitrogen (DIN:  þ NO 3 þ NO2 þ NH4 ), phosphorus (DIP), total organic carbon (TOC), and suspended solids (SS), water samples in 5-L buckets were stored in the dark at 0 °C prior to analysis following the methods of Jiang et al. (2012).

2.3. Phytoplankton community

29.8N

29.7N

29.6N

29.5N

29.4N 121.5E

121.6E

121.7E

121.8E

121.9E

122E

122.1E

122.2E

Fig. 1. Study area in the inner section of Xiangshan Bay. Solid circles indicate the inlet and outlet of Ninghai Power Plant; hollow circles indicate the sampling stations.

Z. Jiang et al. / Marine Pollution Bulletin 76 (2013) 315–324

Qian, 2003). Species having a minimum of 2% contribution to total abundance were considered to be the dominant species. 2.4. Data analysis The software PRIMER 5.0 was used to calculate the phytoplankton Shannon–Wiener diversity (H0 ) and Pielou’s evenness (J0 ) indices. A two-way (station and water layer) analysis of variance (ANOVA) was used to test for significant differences in phytoplankton community and environmental variables. Prior to ANOVA, all variables were tested for normality (Kolmogorov–Smirnov test) and homogeneity (Levene’s test), and the data were log-transformed to meet the assumptions for ANOVA where necessary. This analysis was carried out in SPSS 13.0. Species abundances were transformed by log(x + 1) and standardized before the estimation of Bray–Curtis similarities between sample pairs. A two-way (station and water layer) crossed analysis of similarity (ANOSIM) was used to test phytoplankton community differences in different seasons. Canonical correspondence analysis (CCA) was applied to log(x + 1) transformed phytoplankton abundance and environmental data (except for pH) to reveal the spatial patterns of the phytoplankton community and the effect of environmental parameters on community structure (ter Braak and Smilauer 2002). If the frequency of species occurrence in samples was less than 10% or its cell density percentage of the total number was less than 1.0, the species were arbitrarily excluded from the CCA. These analyses were performed in either CANOCO 4.5 or PRIMER 5.0. 3. Results 3.1. Taxonomic composition A total of eight phyla and 221 species (including varieties, forma, and undetermined species) of phytoplankton were collected during six cruises (Supplementary data 1), consisting of 172 diatom, 36 dinoflagellate, and 13 other taxonomic species (Table 1). The species richness in different seasons were midsummer (125) > early summer (108) > autumn (96) > WST (81) > winter (61) > spring (49). Diatoms dominated the phytoplankton community with 80.7% of the average species number and 73.8% of the total abundance. The dinoflagellates followed with 15.8% and 19.4% (Fig. 2C) total phytoplankton species and abundance, respectively. 3.2. Abundance Despite the seasonal differences (Fig. 2), fish farming significantly (P < 0.01) increased dinoflagellate abundance and its proportion in total cell density by 49.1% and 44.0% (seasonal average), respectively, but did not significantly increase microalgal

Table 1 Number of phytoplankton species in different seasons. a: winter–spring transition 2009; b: midsummer 2009; c: winter 2010; d: spring 2010; e: early summer 2010; f: autumn 2010. Phyla

a

c

d

f

Total

Bacillariophyta Dinophyta Cyanophyta Chlorophyta Euglenophyta Cryptophyta Chrysophyta Xanthophyta

75 6 0 0 0 0 0 0

b 96 21 3 3 0 1 1 0

49 10 0 0 2 0 0 0

40 8 0 0 0 1 0 0

e 85 17 2 1 1 1 1 0

71 21 1 0 1 1 0 1

172 36 5 3 2 1 1 1

Total

81

125

61

49

108

96

221

317

abundance when compared with the CA (Tables 2 and 3). Table 3 also shows a significant difference (P < 0.05) in dinoflagellate parameters between seasons and water layer. Because of the Prorocentrum minimum bloom, resulting in the highest abundance during WST, dinoflagellate abundance accounted for 38.0% of the total cell numbers.

3.3. Species richness, diversity, and evenness Compared with the CA, phytoplankton species richness, diversity, and evenness in the FC differed seasonally (Fig. 2). Threeway ANOVA indicated that fish farming significantly (P < 0.05) affected H0 and J0 , but not S. Based on seasonal averages, this culture type slightly enhanced the S, H0 , and J0 by 0.7%, 5.0%, and 5.0%, respectively (Table 3).

3.4. Dominant species The dominant species in different seasons mainly consisted of diatoms (e.g., Chaetoceros, Coscinodiscus, Navicula, Nitzschia, Skeletonema, and Thalassiosira) and dinoflagellates (e.g. Prorocentrum, Gyrodinium spirale, Karenia mikimotoi, and Scrippsiella trochoidea), as well as Cyanophyta (Trichodesmium thiebautii), Chlorophyta (an unidentified single-cell species with small cell size < 5 lm), Chrysophyta (Ebria tripartita), Euglenophyta (Eutreptiella gymnastica), and Cryptophyta (Plagioselmis prolonga) species dominating in summer and autumn. P. minimum bloomed in the cold seasons, including winter, WST, and spring. Also, an unidentified Prorocentrum species (with the length 30 lm and width 20 lm) dominated in the WST and winter. There were obvious differences in the composition (or dominance) of dominant species between the FC and CA in all seasons, however this was not tested statistically (Fig. 3). For example, Thalassiosira sp1. in the FC was much higher than that in the CA during spring, while the opposite was true for Skeletonema costatum. 3.5. Community composition analysis Cluster analysis showed that the phytoplankton community could be classified into inside (FC) and outside (CA) groups in all seasons (Fig. 4). There were also obvious differences among different sampling seasons (especially between the cold and warm seasons) and water layers. Further pair-wise comparisons tested by two-way crossed ANOSIM revealed significant spatial differences (P < 0.05) between the FC and CA, as well as significant differences (P < 0.05) between the surface and bottom communities (Table 4). 3.6. Environmental variables Comparison results of the surface and bottom environmental variables between the FC and CA are shown in Fig. 5. Temperature (9.8–29.6 °C) and salinity (18.7–25.9) varied among the seasons but not much changed between the FC and CA (Table 3). DIN (0.794 ± 0.300 mg L1), DIP (0.073 ± 0.023 mg L1), DSi 1 (1.226 ± 0.361 mg L1), and NHþ (0.025 ± 0.011 mg L ) concentra4 tions were high in the study area, varying in the range of 0.397– 1.212, 0.048–0.121, 0.772–1.719, and 0.013–0.042 mg L1, respectively. Except for DSi, the concentrations of other nutrients (NHþ 4, DIN and DIP), TOC, and SS in the FC were generally higher than those in the CA (Fig. 5). However, the levels of DO and N/P in the FC were much lower than those in the CA. Tables 2 and 3 show that farming practices significantly increased (P < 0.05)

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Fig. 2. Phytoplankton community parameters (Mean ± SD) in the fish cages (FC) and control area (CA) in different seasons. WST: winter–spring transition, S: surface, B: bottom, S: species richness, H0 : Shannon–Wiener diversity index, J0 : Pielou’s evenness index. Symbols above the histogram denote significant differences between the FC and CA. nsNo significance, P < 0.05, P < 0.01, P < 0.001.

Table 2 Increased percentage (%) of the phytoplankton community and environmental parameters of the water column between the FC and CA on different dates. WST: winter–spring transition; DA: dinoflagellate abundance; TA: total abundance; S: species richness, H’: Shannon–Wiener diversity index; J’: Pielou’s evenness index; DIN: dissolved inorganic nitrogen; DIP: dissolved inorganic phosphorus; DSi: dissolved silicate; TOC: total organic carbon; DO: dissolved oxygen; SS: suspended solids. Time

DA

DA/TA

TA

S

H0

J0

DIN

DIP

DSi

NHþ 4

N/P

TOC

DO

SS

WST Midsummer Winter Spring Early summer Autumn Ave.

23.2 146.3 52.1 118.4 43.8 102.4 49.1

10.0 22.3 19.9 94.4 20.3 177.3 44.0

13.0 104.2 41.0 12.8 28.7 18.4 7.0

13.4 11.4 22.2 0.9 2.4 31.9 0.7

10.2 3.5 4.9 1.7 3.2 36.9 5.0

5.9 0.6 3.2 1.6 3.7 27.0 5.0

3.0 8.8 3.4 10.2 2.5 1.7 4.9

4.2 18.7 2.9 39.6 17.3 8.2 15.2

1.2 14.0 4.2 4.3 7.2 0.2 0.1

2.5 16.1 12.2 102.5 72.6 23.8 38.3

1.2 8.9 0.6 20.3 12.6 7.7 8.4

286.8 6.4 20.9 11.2 22.9 3.9 58.7

0.0 11.9 0.2 3.7 5.6 0.5 3.6

40.3 10.7 19.4 62.2 15.1 9.6 9.2

the NHþ 4 , DIP, and TOC levels, with a seasonal average increment of 38.3%, 15.2%, and 58.7%, respectively. DIN and SS concentrations in the water column at the FC were appreciably elevated (by 4.9% and 9.2%, respectively) in comparison with the CA, although those differences were not statistically significant. However, fish farming did significantly decrease the DO level (P < 0.01). These increase/decrease trends were more pronounced in warmer seasons, particularly in summer. Fig. 5 shows that surface NHþ 4 levels were much higher that of the bottom layer in warm seasons, while the opposite was true in cold seasons due to the N discharged from sediments. 3.7. Canonical Correspondence Analysis (CCA) CCA showed highly significant scores (P = 0.002) for axis 1 and all canonical axes, thus the ordination results are authentic. These 13 environmental variables in the CCA explained 87.0% of the total

variation in the phytoplankton community. The eigenvalues of axis 1 and 2 were 0.505 and 0.435, respectively, which describes 25.5% and 21.9% of the total variance, respectively. The species-environment correlations were 0.994 for axis 1 and 0.989 for axis 2, indicating a significant relationship between the environmental variables and dominant species. Fig. 6 shows nutrients (DIN, DIP, and DSi), temperature, salinity, and irradiance (transparency and SS) were the main variables associated with the phytoplankton community distribution. As indicated by the CCA bi-plot (Fig. 6), four main taxonomic groups could be distinguished. The right quadrant (Group I) in particular consisted of small cell-size microalgae, including diatoms (e.g., Cyclotella stylorum, N. longissima, S. costatum, T. nordenskioldii, and Chaetoceros spp.), dinoflagellates (Prorocentrum sigmoides and S. trochoidea), and one Cryptophyta species (P. prolonga) which were profiting mostly from the increased temperature, irradiance (high transparency and low SS), and nutrient (DIN, DIP, and DSi)

Z. Jiang et al. / Marine Pollution Bulletin 76 (2013) 315–324 Table 3 Results of the three-way ANOVA for phytoplankton community and environmental parameters. BA: diatom abundance; Tra: Transparency; Temp: Temperature. Parameters

Time

Station

Water layer

TA BA DA DA/TA BA/DA S H’ J’ Temp Salinity DO pH SS NHþ 4 DIN DIP DSi N/P TOC

F(5,71) = 40.6*** F(5,71) = 32.5*** F(5,71) = 48.2*** F(5,71) = 25.9*** F(5,71) = 27.0*** F(5,71) = 97.0*** F(5,71) = 61.0*** F(5,71) = 38.3*** F(5,23) = 377.2*** F(5,23) = 124.7*** F(5,23) = 356.4*** F(5,23) = 356.2*** F(5,23) = 22.54*** F(5,23) = 15.0*** F(5,23) = 64.8*** F(5,23) = 46.6*** F(5,23) = 113.0*** F(5,23) = 377.2*** H(5,24) = 14.9*

F(1,71) = 0.9ns F(1,71) = 0.5ns F(1,71) = 8.7** F(1,71) = 9.7** F(1,71) = 8.7** F(1,71) = 1.0ns F(1,71) = 5.0* F(1,71) = 9.2** F(1,23) = 0.2ns F(1,23) = 0.1ns F(1,23) = 10.5** F(1,23) = 15.9*** F(1,23) = 0.0ns F(1,23) = 12.3** F(1,23) = 1.0ns F(1,23) = 13.7** F(1,23) = 0.0ns F(1,23) = 0.2ns F(1,24) = 3.9*

F(1,71) = 14.0*** F(1,71) = 9.2** F(1,71) = 10.1** F(1,71) = 4.1* F(1,71) = 4.5* F(1,71) = 1.8ns F(1,71) = 0.0ns F(1,71) = 0.1ns F(1,23) = 1.0ns F(1,23) = 1.7ns F(1,23) = 2.3ns F(1,23) = 0.8ns F(1,23) = 0.9ns F(1,23) = 0.1ns F(1,23) = 4.8* F(1,23) = 5.4* F(1,23) = 0.7ns F(1,23) = 1.0ns F(1,24) = 0.1ns

ns

No significance. P < 0.05. ** P < 0.01. *** P < 0.001. *

levels. In the left lower quadrant, Group II was mainly composed of centric diatoms (Coscinodiscus jonesianus, Melosira sp., and Thalassiosira spp.) that were favored by increased DSi, DIP, and Si/N and relatively lower temperatures, DIN, and N/P. Group III, in the left upper quadrant, were those species (Nitzschia subtilis, Paralia sulcata, and P. minimum) more tolerant to high levels of organic substances, suspended solids, and salinity but low nutrient availability, most of these species bloomed in winter 2009 due to thermal discharge. In the topside of the upper quadrant plot (Group IV), Corethron hystrix, T. thiebautii, and an unidentified Chlorophyta species dominated with high temperature, salinity, N/P, and DIN.

319

4. Discussion 4.1. Effects of fish farming on water quality Nutrient load enhancement is one of the most severe negative consequences of fish farming. Low feeding efficiency, high fish density, and feed quantity due to intensive cage farming usually lead to mass loss of nutrients. For example, Islam’s conceptual model shows that based on the usual feed conversion rate, 132.5 kg N and 25.0 kg P are released into the environment for per one ton yield of fish (Islam, 2005). In the present study, the average seasonal increase in NHþ 4 , DIN, and DIP percentages inside the FC compared with the CA were 38.3, 4.9, 15.2, respectively (Table 2), although the absolute nutrient concentration values inside the FC were slightly higher than those outside (Fig. 5). This might be related to the small scale (about 18.7 ha) of the farm and extremely high levels of background nutrients (Jiang et al., 2012), especially the nitrate concentration. The relatively abundant N led to an N/ P reduction (by 8.4%) in the FC (Table 2). These findings are consistent with previous reports (Alongi et al., 2003; Navarro et al., 2008; Neofitou and Klaoudatos, 2008; Pitta et al., 2006, 2009; Wu, 1995). However, few studies have detected significant changes in the nutrient content of the water column in the vicinity of fish farms (La Rosa et al., 2002; Yucel-Gier et al., 2008), despite the large amount of nutrient waste discharged into the marine environment. The reasons for this were the rapid assimilation of the input nutrients by the microbial food web (Navarro et al., 2008; Olsen et al., 2007; Pitta et al., 2009), as well as water movement and exchange with the surroundings (Dalsgaard and Krause-Jensen, 2006). Fig. 5 shows high levels of DIN, DIP, and DSi in the study area, with average seasonal concentrations of 0.794, 0.073, and 1.226 mg L1, respectively. These values were higher than in 2000 when the annual average concentrations of DIN (0.766 mg L1) and DIP (0.031 mg L1) were measured (Ye et al., 1 2002). However, the present NHþ ) was much 4 level (0.025 mg L 1 lower than (0.082 mg L ) in 2000, due to the decrease in the scale of the cage farm. These results indicate a serious eutrophication situation in this mariculture area mainly caused by terrestrial nutrients. It also verifies the nutrient data of Nobre et al. (2010):

Fig. 3. Dominant phytoplankton species at different stations and seasons.

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2534 t of DIN and 1039 t of P came from the fish cages. So the high eutrophic level in this bay is the result of catchment input as well as excessive aquaculture activities. If wastewater with high N and P concentrations is continuously discharged without treatment, the corresponding area may have persistently high concentrations of total organic matter, especially in badly managed or poorly located sites (Cao et al., 2007). In fact, cage culture in China is mostly located in sheltered coastal areas or semi-closed bays that are protected from strong winds and large waves, such as the XSB (Fig. 1). The small quantity of water exchange in semi-closed bays combined with the decline in flow velocity caused by the rafts and nets of cage culture reduce the diversion and diffusion of excessive nutrients, resulting in long-term nutrient accumulation exceeding the environmental carrying capacity (Ning and Hu, 2002; Jiang et al., 2013). The resistance and hysteresis of an ecosystem might cause the required delay for the slow and persistent process of eutrophication. However, if the fish farm is located in an open sea area with greater water depths and a strong hydrodynamic regime, the negative effects on the environment will be negligible (Vezzulli et al., 2008). Besides the soluble inorganic wastes (e.g., above-mentioned nutrients), intense fish cages also generate considerable particulate organic waste (Alongi et al., 2009; Carroll et al., 2003; Dalsgaard and Krause-Jensen, 2006; Dong et al., 2008; Lauer et al., 2009; Modica et al., 2006; Sara, 2007; Wu, 1995). Thus, both the TOC and SS are also deeply affected by cage fish farming. In this investigation, the TOC (Fig. 5M) and SS (Fig. 5G) inside the FC were much higher than those outside. On average, fish farming drastically increased the TOC and SS levels by 58.7% and 9.2%, respectively (Table 2), compared with the CA. In contrast, because of the oxygen consumed by the cultured fish and organic matter decay, DO concentration of water column in the FC was lower than in the CA in all seasons (Fig. 5F). This declining trend was more pronounced in warmer seasons, especially summer, during which DO levels decreased by 11.9% in 2009 and 5.6% in 2010 (Table 2). Similarly, Yoshikawa et al. (2007) found that DO at the surface markedly decreased below the critical level during cloudy weather (restraining planktonic photosynthesis) in summer and autumn, while in winter, DO remained at high levels throughout the water column due to an active supply from the air and vertical mixing. With increasing temperature in warm seasons, growth, feeding, and excretion in Japanese seaperch and black seabream increased (Ning and Hu, 2002), and the levels of nutrients and organic matter inside the farm were much higher than those outside (Table 2). In the present investigation, we observed organic matter enrichment of the sediment with a strong feculent odor and a deep anoxic layer at the FC, which has also been reported by other workers (Belias et al., 2007; Ning and Hu, 2002). TN, TP, and TOC concentrations in sediment samples taken at the stations (author unpublished data) indicated that the nutrients discharged from feeding and remaining organic particles (mostly from unconsumed feed and fecal material) accumulated in the sediment (Carroll et al., 2003; David et al., 2009; Dong et al., 2008; Lauer et al., 2009). Because of the enhancement of microbial activity in spring and summer, more nutrients discharged from the sediment meant that the DIN,

CABc CABc CABc CASc CASc CASc FCBc FCBc FCBc FCSc FCSc FCSc FCSd FCSd FCSd FCBd FCBd FCBd CABd CABd CABd CASd CASd CASd CASa CASa CASa FCBa FCBa FCBa FCSa FCSa FCSa CABa CABa CABa FCBe FCBe FCBe FCSe FCSe FCSe CABe CABe CABe CASe CASe CASe CABf CABf CABf CASf CASf CASf FCBf FCBf FCBf FCSf FCSf FCSf FCBb FCBb FCBb CABb CABb CABb FCSb FCSb FCSb CASb CASb CASb

20

40

60

80

100

Similarity Fig. 4. Cluster analysis based on a Bray–Curtis similarity matrix of phytoplankton samples in different seasons. Lowercase af indicate the sampling seasons, shown in Table 1.

annual nutrient load in this bay from the catchment was estimated to be about 4015 t of DIN and 730 t of P, meanwhile, most likely

Table 4 Results of a two-way crossed analysis of similarity (ANOSIM) for phytoplankton community in different areas and water layers. Groups

FC vs. CA S vs. B

WST

Midsummer

Winter

Spring

Early summer

Autumn

R

P

R

P

R

P

R

P

R

P

R

P

0.74 0.59

0.01 0.04

0.63 0.98

0.02 0.01

1.00 1.00

0.01 0.01

0.89 0.78

0.01 0.01

1.00 1.00

0.01 0.01

1.00 1.00

0.01 0.01

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Fig. 5. Surface and bottom environmental variables (Mean ± SD) in the FC and CA in different seasons.

in the region of the farms and their adjacent waters (Cao et al., 2007; Islam, 2005; Wu, 1995), especially in a weak water-exchange, semi-closed fiord with high cage densities, which promotes phytoplankton growth (Gao et al., 2012) and induces red tides (David et al., 2009). Unfortunately, fish cages in China are mostly located in semi-closed bays and the fish are usually fed on excessive formulated diets and fresh trash fish for high yield, which leads to the accumulation of organic matter in the bottom water and sediment (Jiang et al., 2012). The N, P, and organic carbon deposits are subsequently released into the upper water column when favorable conditions occur, which both causes and intensifies the eutrophication process (Alongi et al., 2009; Belias et al., 2007; Dong et al., 2008; Lauer et al., 2009).

4.2. Effects of fish farming on the phytoplankton community under thermal stress Fig. 6. CCA ordination of main phytoplankton species with environmental variables. 1: C. knipowitschi; 2: C. tortissimus; 3: C. hystrix; 4: Coscinodiscus bipartitus; 5: C. curvatulus; 6: C. jonesianus; 7: C. stylorum; 8: Melosira sp.; 9: N. corymbosa; 10: N. hungarica; 11: N. longissima; 12: Nitzschia subtilis; 13: P. sulcata; 14: P. aestuarii; 15: R. delicatula; 16: S. costatum; 17: T. nordenskioldii; 18: T. pacifica; 19: Thalassiosira sp.1; 20: Thalassiosira spp.; 21: P. minimum; 22: P. sigmoides; 23: S. trochoidea; 24: Chlorophyceae species; 25: T. thiebautii; 26: P. prolonga. Full species names see Fig. 3.

NHþ 4 , and DIP levels in the bottom water were significantly higher than in the CA (Fig. 5). It should be noted that the study fish farm was commonly fed with trash fish (Gao et al., 2012). Consequently, the levels of nutrient loading would be several orders of magnitude higher in this area. These discharged nutrients could exacerbate eutrophication

Based on the circulation system in the ECS, XSB is mainly influenced by the diluted waters of the Changjiang and Qiantangjiang, Taiwan Warm Current (TWC), and runoffs around the bay (Ning and Hu, 2002; Jiang et al., 2013). Therefore, the area has a complicated species composition with high numbers of phyla (8) and species (245), e.g. freshwater and brackish species (e.g., Melosira sp.) from runoff and diluted water, coastal eurythermal species (e.g., C. knipowitschi, P. minimum, and S. costatum), local or from the diluted waters, and coastal warm-water species and offshore hightemperature species (mostly dinoflagellates) from the TWC (Jiang et al., 2013). In addition, in warmer seasons, many offshore highsalinity species (e.g., T. thiebautii) are brought in by the TWC, causing a visibly higher species richness in summer and autumn (Table 1).

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Phytoplankton cell numbers inside the fish farm were significantly lower than those outside in winter, summer, and autumn 2010, but they were significantly higher or approximately equal in samples from other seasons (Fig. 2). These results support previous studies that failed to establish a clear relationship between fish farm inputs and phytoplankton biomass (Alongi et al., 2003; La Rosa et al., 2002; Navarro et al., 2008; Sidik et al., 2008; Yucel-Gier et al., 2008). On the one hand, a decrease in flow velocity (Ning and Hu, 2002) and N/P as well as an increase in nutrient content in the FC promoted microalgal growth (Wang et al., 2006, 2009). Laboratory experiments by Gao et al. (2012) indicated that trash fish tissue could be a direct source of nutrients (e.g., dissolved organic nitrogen) supporting phytoplankton growth. On the other hand, an increase in SS might restrain photosynthesis and subsequently repress phytoplankton propagation and growth. Besides, some phytoplankton associated with attached microalgae were grazed by protozoans, metazoan zooplankton species (Doi et al., 2008; Navarro et al., 2008; Olsen et al., 2007; Pitta et al., 2009), shellfish (Silva et al., 2012), and cultured fishes (Azim et al., 2003) and thereby entered into the food web. Pitta et al. (2009) deemed that grazing played a key role in regulating phytoplankton biomass, keeping Chl a at very low levels and effectively transferring nutrients up the food web, on the basis of the comparison results from bioassays with and without grazer exclusion. Similarly, Dalsgaard and Krause-Jensen (2006), using macroalgal and phytoplankton bioassays, found high primary productivity near the fish cages, which rapidly decreased with distance from the farms. Thus, phytoplankton communities in the fish cages were simultaneously affected by the top-down and bottom-up effects, causing ambiguous increases or decreases in abundance (Table 2). However, fish farming drastically increased dinoflagellate abundance and its proportion in total cell density by 49.1% and 44.0%, respectively (Table 2). This finding agrees with the investigation in a fish farm in Cape Bolinao, Philippines, where the diatom/dinoflagellate ratio decreased significantly (San Diego-McGlone et al., 2008). Similarly, Buschmann et al. (2006) suggested that the presence of salmon farms led to a significant increase in the abundance of dinoflagellates in short-term pulses. The reason being the increase in organic matter and ammonia, which stimulated dinoflagellate growth because they (particularly the heterotrophic types) can graze diatoms (Sherr and Sherr, 2007) and consume organic carbon, N, and P (Fagerberg et al., 2010; Purina et al., 2004). For example, Fagerberg et al. (2010) revealed that the input of larger river DOM molecules to N-limited coastal systems might influence the composition of the coastal phytoplankton community in favor of dinoflagellates. In the present investigation, fish farming slightly increased phytoplankton diversity as a whole, though this change varied seasonally (Table 2). However, Sidik et al. (2008) and Skejic´ et al. (2011) found approximate phytoplankton diversity between the fish farms and control areas in their studies. Fish cages use many nets and ropes and large amounts of microalgae attach to these structures (Doi et al., 2008). During phytoplankton species identification and counting, several epiphytic microalgal taxa (e.g., Diploners, Navicula, and Nitzschia) were found, although their abundances were very low. Therefore, diversity inside the FC was higher than outside. Because of the variances in species composition in the community and abundance (or occurrence) of dominant species (Fig. 3), the phytoplankton community structure was significantly (P < 0.05) different between the FC and CA, which was also confirmed by cluster analysis (Fig. 4) and ANOSIM (Table 4). The CCA perfectly exhibited the spatial distribution of dominant phytoplankton taxa along with the environmental variables (Fig. 6). Temperature and nutrients were the most important variables affecting the phytoplankton community. The fish farm, just located near the outfall of the Ninghai Power Plant (Fig. 1), was

enveloped with curve-like surroundings of a 0.5 °C temperature elevation caused by vast thermal discharge (82.5 m3 s1) (Jiang et al., 2013). In the cold seasons, such temperature increases in receiving waters were far more significant. In fact, before the power plant began operations microalgae blooms usually occurred during warm seasons in XSB (You and Jiao, 2011). However, after the power plant opened in December 2005, red tide outbreaks occurred much earlier in January 2006 and 2009 (Jiang et al., 2012). Because of the long residence time (80 days for 90% water exchange) in the inner bay (Ning and Hu, 2002) thermal effluents remain for quite long periods in the cages (Jiang et al., 2013). Compared with the historical data, it was clear that the nutrient levels in the XSB mariculture area had changed remarkably (see par. 2 in Section 4). Hence, the present seasonal average abundance (17.18 cells mL1) in the farm was much higher than the annual value (10.25 cells mL1) in 2000 (Ning and Hu, 2002). In particular, cell numbers in winter (10.00 cells mL1), WST (25.42 cells mL1), and spring (18.02 cells mL1) were higher by one order than in winter (1.65 cells mL1) and spring (5.73 cells mL1) 2000. Furthermore, this change led to variation of the microalgal community composition under the above-mentioned thermal stress; we found that the phytoplankton taxa were co-dominated by diatoms (Chaetoceros, S. costatum and Thalassiosira) and dinoflagellates (e.g., P. minimum, S. trochoidea and K. mikimotoi). Our results were not consistent with earlier survey results, which found that only diatoms dominated (Chaetoceros, Cyclotella, Navicula, Melosira, and S. costatum) (Ning and Hu, 2002). This shift was similar to that in the subtropical Daya Bay, South China Sea, which is also influenced by a power plant (Li et al., 2011). Liao et al. (2008) reported that eutrophication intensification combined with moderate temperature elevation distinctly promoted phytoplankton growth, their results agree with the field survey in this paper. 4.3. P. minimum blooms caused by fish farming and thermal discharge Fish farming and thermal discharge from the power plant seemed to trigger P. minimum blooms. Driven by the favorable conditions of temperature elevation, better light permeation, and flow velocity reduction, P. minimum formed blooms in winter (10.7 °C) and WST (11.0 °C), with an abundance of 1.07 (dominated by 10.7% of the total abundance) and 9.13 cells mL1 (dominated by 35.9% of the total abundance), respectively. The bloom in WST did not occur in other regions, such as the oyster and kelp farms adjacent to the fish farm (Jiang et al., 2012) suggesting that fish farming induced P. minimum growth. Likewise, San Diego-McGlone et al. (2008) reported that P. minimum was the cause of the harmful bloom and the low diatom/dinoflagellate ratio observed in a fish farm in Cape Bolinao, Philippines. Red tide caused by several species (mostly the diatoms), such as S. costatum, Chaetoceros socialis, and P. sulcata have occurred in the study are in the past (You and Jiao, 2011). However, no P. minimum red tides have been recorded to date. Therefore, the possibility of such a HAB should not be ignored, because of its toxicity to fish and other animals (Heil et al., 2005). P. minimum is described as a eurythermal and euryhaline species and blooms can occur under a wide range of environmental conditions (Tango et al., 2005). Heil et al. (2005) reported that its blooms occurred in relatively warm, low-turbulence, and highirradiance environments, which were often characterized by eutrophication. However, temperature may not be a limiting factor for the development of P. minimum blooms. In the Mediterranean, P. minimum can be a prominent species at temperatures from 4 to 27 °C (Grzebyk and Berland, 1996). In addition, Springer et al. (2005) followed the progression of a P. minimum bloom in the Neuse Estuary, North Carolina, and found that it persisted for several months during winter. Similarly, the present investigation

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showed that this HAB species formed blooms during winter and WST with temperatures close to 10 °C under thermal and eutrophic stimulation. Organic matter from the fish cages might play an active role in P. minimum bloom formation via direct effects on growth, yield, and photosynthesis under nutrient additions (Heil, 2005). Rothenberger et al. (2009) found that P. minimum abundance was positively related to dissolved organic N and SS concentrations, based on a 13-yr record of environmental data and phytoplankton assemblages in the Neuse Estuary. Our study agrees with their finding, as indicated by the CCA bi-plot (Fig. 6), P. minimum abundance was closely related to the presence of organic matter. Obviously, these P. minimum blooms are consistent with the present environmental conditions caused by fish farming and power plant operations.

5. Conclusion The present study confirms our hypothesis that fish farming and thermal stress has changed the phytoplankton community structure and induced algal blooms in cages adjacent to the power plant. Fish farming slightly increased the total microalgal abundance, species richness, diversity, and evenness, but significantly increased dinoflagellate abundance and domination. Because of the relatively small size of the mariculture area and the high nutrient concentration of the surrounding waters, eutrophication did not significantly exacerbate in this area, although NHþ 4 and DIP concentrations increased significantly. This trend was more pronounced in warmer seasons, especially in summer. Nutrients and temperature were the two most important environmental parameters determining phytoplankton distribution in the cages and surrounding waters, suggesting that fish farming and thermal stress caused by thermal discharge from the power plant strongly impacted the phytoplankton assemblage in the inner XSB. In addition, thermal effluent associated with eutrophication intensification caused a shift in the dominant species from diatoms to co-dominance between dinoflagellates and diatoms. This is the first record that these combined stresses induced P. minimum blooms in winter and WST in this bay. Thus, thermal discharge and eutrophication should not be ignored or underestimated. With more power plants recently coming into operation along China’s bays, we should invest more effort into the study of phytoplankton responses to cultivation activities under the stress of temperature increases. In the future more consideration should be given to the adverse effects of aquaculture practices and power plant construction in such eutrophic, semi-enclosed bays.

Acknowledgements This Project was supported by the National Basic Research Program of China (2010CB428903), National Marine Public Welfare Research Project of China (201305043-3 and 201305009), National Natural Science Foundation of China (41176142 and 41206103), Natural Science Foundation of Zhejiang Province, China (Y5110131), the Innovative Team on Marine Aquiculture of Zhejiang Province, China (2010R50025), and Basic Scientific Research Project of Second Institute of Oceanography, State Oceanic Administration (JG1222 and JG1221).

Appendix A. Supplementary material Supplementary data Species catalogue associated with this article can be found, in the online version, at http://dx.doi.org/ 10.1016/j.marpolbul.2013.07.006.

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