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Stocking density effects on growth and stress response of juvenile turbot (Scophthalmus maximus) reared in land-based recirculating aquaculture system
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Baoliang LIU1, Rui JIA1, 2, Kuifeng ZHAO3, Guowen WANG3, Jilin LEI1, 2, Bin HUANG1, *
Acta Oceanologica Sinica | 2017, 36(10) : 31 - 38
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Acta Oceanologica Sinica | 2017, 36(10): 31-38
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Stocking density effects on growth and stress response of juvenile turbot (Scophthalmus maximus) reared in land-based recirculating aquaculture system
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Baoliang LIU1, Rui JIA1, 2, Kuifeng ZHAO3, Guowen WANG3, Jilin LEI1, 2, Bin HUANG1, *
Affiliations
  • 1 Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences; Key Laboratory of Sustainable Development of Marine Fisheries, Ministry of Agriculture; Qingdao Key Laboratory for Marine Fish Breeding and Biotechnology, Qingdao 266071, China
  • 2 Wuxi Fisheries College, Nanjing Agricultural University, Wuxi 214081, China
  • 3 Shandong Oriental Ocean Sci-Tech Co., Ltd, Yantai 264000, China
Published: 2017-06-01 doi: 10.1007/s13131-017-0976-4
Outline
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Stocking density is widely recognized as a critical factor in aquaculture and a potential source of long-term stress. The influence of stocking density on growth and stress response of juvenile turbot (Scophthalmus maximus, ~3–75 g, initial to final weight) was examined in fish held under low (LD, ~0.21–5.31 kg/m2, initial to final density), medium (MD, ~0.42–10.81 kg/m2) and high stocking density (HD, ~0.63–14.27 kg/m2) for 120 days in a recirculating aquaculture system (RAS). In this trial, the growth curve for weight of juvenile turbot in RAS, all fitted by the Schnute model. No significant difference was found in growth performance among the three densities until at the final sampling (Day 120). The final weight and body weight increase (BWI) in the HD group were significantly lower than in other groups (P<0.05, weight: (75.83±2.49) g, (75.39±2.08) g, (65.72±2.86) g and BWI: (2 436.12±28.10)%, (2 421.29±4.64)%, (2 097.88±20.99)% in LD, MD and HD groups, respectively). Similarly, the specific growth rate (SGR), feed conversion ratio (FCR) and coefficient of variation for weight (CVw) were adversely affected by high stocking density (P<0.05). However, there was no difference in survival and Fulton’s condition factor (K) of turbot among the different groups. Physiological analyses demonstrated a clear increase in the plasma cortisol level and an obvious decrease in growth hormone (GH) concentration in the HD group on Day 120 (P<0.05). There was no significant effect of stocking density on plasma glucose, Cl– and protein levels. All these findings would provide a reference for selecting the optimal stocking density of juvenile turbot in RAS.

growth performance  /  recirculating aquaculture system  /  Scophthalmus maximus  /  stress physiology  /  stocking density
Baoliang LIU, Rui JIA, Kuifeng ZHAO, Guowen WANG, Jilin LEI, Bin HUANG. Stocking density effects on growth and stress response of juvenile turbot (Scophthalmus maximus) reared in land-based recirculating aquaculture system[J]. Acta Oceanologica Sinica, 2017 , 36 (10) : 31 -38 . DOI: 10.1007/s13131-017-0976-4
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1 Introduction
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In the last decade, aquaculture based on industrial rearing systems together with intensive fish farming has become the focus of interest. In intensive aquaculture systems, stocking density is an important factor that governs productivity (Riche et al., 2013) because it is often found to influence growth, survival, water quality, fish welfare and health (Yi and Lin, 2001; Bolasina et al., 2006; Salas-Leiton et al., 2010; Biswas et al., 2013). High stocking density may induce chronic stress associated with deterioration in water quality or adverse social interactions, and this can result in negative biochemical changes (Montero et al., 1999; Bolasina et al., 2006). This can affect the rate and efficiency of feeding and digestion, and subsequently growth (Rowland et al., 2006).
Turbot (Scophthalmus maximus) is farmed in Europe and China, and in commercial operations, 100 g turbot can be reared at a density of 25 kg/m2, whereas stocking densities generally range from 30 kg/m2 to 40 kg/m2 for larger fish (Aksungur et al., 2007; Baer et al., 2011). However, this management strategy ignores the possible negative impact of high densities on fish growth.
Recirculating aquaculture system (RAS) is an important model in global aquaculture industry, given its cost-effective, environment-friendly and product safety features as well as easy regulation of water quality (d’Orbcastel et al., 2009). Land-based RAS farming has been developed on an industrial scale by overcoming the technical challenges. It is used in fish farming, including sea bass (Dicentrarchus labrax) (Deviller et al., 2005), cobia (Rachycentron canadum) (Resley et al., 2006), rainbow trout (Oncorhynchus mykiss) (Mansfield et al., 2010) and Atlantic salmon (Salmo salar) (Burr et al., 2012). The relationships between stocking density and growth performance, metabolism, immunity and well-being of these fish were reported in RAS (Heinen et al., 1996; Sammouth et al., 2009; Liu et al., 2014). However, in turbot, few studies have focused on the effects caused by stocking density in a common commercial model of RAS. Lab-scale studies evaluating the impact of stocking density on growth performance might not easily scale up to the larger commercial environment. Therefore, in this study, we evaluated the effects of stocking density on the growth and stress physiology of juvenile turbot under production conditions using the common commercial model of RAS. These results enhance the management of RAS-cultured turbot in determining the optimal stocking density.
2 Materials and methods
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2.1 Experimental facilities
The study was conducted in a commercial land-based RAS at Shandong Oriental Ocean Sci-Tech Co., Ltd (Shandong, China). The RAS consisted of ten octagonal tanks, ten whirl-separators, a filter screen, a foam-separation unit, a bio-filter section consisting of four separate bio-filters in parallel (each 35 m3), a UV sterilizer, and a DO regulating tank (Fig. 1). Each rearing tank (30 m2 and 1.1 m in depth) was equipped with a mixture of water supersaturated with oxygen from an oxygen cone. The oxygen content and water level (0.5–0.55 m in depth) in the fish tanks were monitored via the RAS computer. The volume of the whirl-separator was approximately 300 L, which was adequate to collect most of the feces. Water (16–18°C) was pumped from a depth of 20 m in the Laizhou Bay of China, mechanically filtered by two sand filters (5 μm filtration) and UV-sterilized before entering the RAS units. Water flows through standpipes covered with 2.0 cm screen to rearing tank were about 16 m³/h, and no more than 10% volume of water in the system was displaced with fresh seawater every day during culture. The temperature in each tank varied slightly throughout the day, but in all cases was maintained at (18±1)°C throughout the trial. The photoperiod was maintained at 12 h light/12 h dark using fluorescent light banks. Further, the nitrification function in bio-filters was already established in the RAS prior to trial.
2.2 Experimental design and management
Turbot were obtained from this facility and reared in the RAS for 15 d to acclimatize to the experimental conditions. The fish (average individual weight (2.99±0.21) g) were reared under three stocking densities: low, 2 200 fish per tank ((0.21±0.01) kg/m2); medium, 4 400 fish per tank ((0.42±0.02) kg/m2); and high, 6 600 fish per tank ((0.63±0.05) kg/m2), and tested in triplicate. In total, 39 600 fish were investigated in nine rearing tanks of a commercial land-based RAS (the tenth tank is empty) and cultured under experimental conditions for 120 days. No differences in mean initial weight and coefficient of variation (CV) in initial weight were found among the three densities. The turbot were fed a commercial-pellet diet (Ningbo Tech-Bank Co., Ltd, Zhejiang China), which contained 52% crude protein, 12% crude lipids, 16.0% crude ash, 3.0% crude fiber, 12% water, 5% Ca, 0.5% P, ≥2.3% lysine, and ≤3.8% sodium chloride. The fish were fed at ration of approximately 2.5% of the tank biomass which divided into four meals daily by hand (06:30, 11:30, 16:30 and 21:30). The feed rations were adjusted based on feeding behavior, weight as well as the practical production experience. The ration of 2.5% was chosen because it was close to satiation, allowed an optimum growth and no excess feed appeared on the bottom of the tanks. Fish were not fed on the sampling day to minimize handling stress.
2.3 Water quality monitoring
Temperature, dissolved oxygen (DO), pH, and salinity were measured daily using a YSI-556 (YSI Incorporated, Yellow Springs, Ohio, USA). Orthophosphates (PO4-P), total ammonium nitrogen (TAN), nitrite-nitrogen (NO2-N), and chemical oxygen demand (COD) were analyzed using a standard method (Chen, 2006) every ten days.
2.4 Growth parameters and survival
Dead fish in all tanks were recorded daily to evaluate the survival rate over the entire study period. Fish growth was evaluated biometrically every 9 d, by randomly measuring the individual weight and standard length of 200–400 fish in each tank (Garcia et al., 2013), to calculate stocking density, specific growth rate (SGR), feed conversion ratio (FCR), Fulton’s condition factor (K), coefficient of variation for weight (CVw), and percentage of covered area (PCA). At the end of this trial, the final body weight increase (BWI) were also calculated. These growth parameters were calculated as follows:
Survival=100×final number of fish/initial number of fish;
BWI=100×(final weight–initial weight)/initial weight;
SGR=100×(ln(final weight)–ln(initial weight))/number of days;
FCR=food consumed/biomass increment;
K=100×weight/length3;
CVw=100×weight standard deviation/mean weight;
Stocking density=(N×Wt)/A;
PCA=N×(Af/A)×100;
Af (m2)=(102.5×weight+3 595.0)×10–6, when weight is Less than 100 g (Irwin et al., 1999);
where N is number of individuals, Wt is average weight (g) at sampling day, A is the bottom area of the fish tank, and Af is the area of the fish body.
2.5 Fish sampling and biochemical assays
The fish were starved for 24 h before sampling for biochemical parameters on Days 30, 60, 90 and 120. Twenty fish were randomly netted from each tank, immediately anesthetized in 0.05% tricaine methane sulfonate (MS-222, Sigma Diagnostics INS, St. Louis, MO, USA). Blood samples were obtained from the caudal vein using heparinized syringes. The plasma was separated by centrifugation (5 000 r/min, 4°C, 15 min) and stored at –80°C for analysis of cortisol, growth hormone (GH), glucose, chloride ion (Cl–) and protein. Plasma cortisol was determined using radioimmunoassay kit (Nanjing Jiancheng Bioengineering Institute, Nanjing, China) available on market as previously described methods (Liu et al., 2015). Plasma GH (μg/L) were measured using a commercially available ELISA kit (Mlbio, Shanghai, China) as previously described by Drennon et al. (2003). Plasma glucose and chloride (mmol/L) were analyzed using an i-STAT Portable Clinical Analyzer (Abbott Inc, Illinois, USA) with EC8+ disposable cartridges (Abbott Laboratories, Illinois, USA). Protein concentrations in plasma was determined by the Bradford method (Bradford, 1976), using bovine serum albumin as a standard; final number of fish=initial number of fish–dead fish (not include the loss in sampling).
2.6 Statistical analysis
The differences among the different groups were analyzed using one-way analysis of variance (ANOVA) and P<0.05 was taken as statistically significant. All experimental treatments of growth performance, and plasma parameters were performed in triplicate. Data were expressed as mean±SD. Linear regression was used to analyze the possible effects of stocking density on SGR. A third degree polynomial was used to compare the density and FCR (Björnsson et al., 2012). All statistical analyses were carried out using SPSS Version 18.0 software (SPSS Inc, Chicago, IL, USA).
3 Results
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3.1 Water quality
During the course of experiment, the water temperature, DO, pH and salinity were maintained at (18±1)°C, (8±1) mg/L, 7.9±0.3, and 27.3±1, respectively, in all tanks. There were no significant differences among the three stocking densities in NO2-N and PO4-P levels (Fig. 2). Between Days 0 and 120, the TAN concentration showed an increasing trend, with no differences among the three stocking densities before Day 100, while it was significantly higher in the HD group than in the LD group at and after Day 100 (P<0.05, Fig. 2). For the period including Days 0 to 110, the variation in COD concentration was similar in all tanks, however, a significant increase occurred in HD group on Day 120 compared with the LD group (P<0.05, Fig. 2).
3.2 Growth performance
During the experiment, no disease outbreak or other signs of disease were observed. Survival was extremely high (>99.5%) in all treatments with no significant differences (Tables 1 and 2). The stocking density and PCA increased gradually with culture time (Figs 3a and b). The final densities (Day 120) were, respectively, (5.31±0.48), (10.81±1.04) and (14.27±1.21) kg/m2 for LD, MD and HD. The final proportion of the tank bottom covered by turbot (PCA) in the LD, MD and HD were (82.81±0.65)%, (165.49±0.12)% and (226.18±1.71)%, respectively. There were no significant differences in individual weight and standard length associated with treatments from Day 0 to Day 110, while both the parameters in LD and MD were obviously higher than in HD on Day 120 (P<0.05, Figs 3c and d). At the end of the trial, significantly lower SGR and significantly higher FCR were observed in HD compared with LD and MD (P<0.05, Table 2). In the study, the Fulton’s condition factor (K) did not differ.
The CVw was not significantly different among the three density groups until the final Day 120 when the CVw of the HD group was clearly larger than in the MD and LD groups (P<0.05, Fig. 4).
In the four growth periods, a significant negative correlation between stocking density and SGR was observed (Fig. 5a), although only 69.50% of the variation was explained by the correlation (R2=0.695, N=36, P<0.01). There was only a minor increase in FCR with density increasing from 0.5 to 11 kg/m2 but a growing increase from 10 to 14 kg/m2 (Fig. 5b). In addition, we also found the growth curve for weight-for-age data for juvenile turbot reared at three different densities in RAS, fitted by the Schnute model (Fig. 6).
3.3 Physiological parameters
The physiological parameters between the treatment densities at different time points in the experiment are listed in Table 3. Post-hoc analysis demonstrated a higher level of plasma cortisol under HD treatment compared with LD treatment on Day 120. Conversely, the plasma GH concentration in HD and MD treatment was evidently lower than in LD treatment. In addition, the plasma glucose, Cl– and protein levels did not vary between groups during the rearing experiment.
4 Discussion
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In the RAS, water quality was maintained at safe levels recommended for turbot, with the outlet O2 concentration always above 6 mg/L, TAN concentration lower than 0.25 mg/L, and NO2–-N concentration under 0.4 mg/L (Poxton and Allouse, 1987; Skøtt Rasmussen and Korsgaard, 1996; Person-Le Ruyet et al., 1997; Aubin et al., 2006). Although the values of TAN and COD were significantly higher in HD than in LD between Days 100 and 120 of the trial, the variations did not affect turbot negatively (Aubin et al., 2006; Song et al., 2012).
In the present study, the survival was excellent and was not significantly affected by stocking density. Although many studies reported an inverse correlation between survival and density (Yi and Lin, 2001; Hitzfelder et al., 2006; Rowland et al., 2006), a few studies fail to report any significant effect of density on survival in teleosts (Webb et al., 2007; Laiz-Carrión et al., 2012; Riche et al., 2013). Stocking density affected the growth in fish, but the correlation between the two parameters may not be uniformly linear positively or negatively in a given species (Irwin et al., 1999). Studies with cobia (Rachycentron canadum) demonstrated a direct inverse relationship between growth and density (Webb et al., 2007). However, conflicting results showed an increased growth rate under high density in juvenile silver perch (Bidyanus bidyanus) (Rowland et al., 2006). In the current study, stocking density affected the values of individual weight and standard length, SGR and BWI at the end of the trial in RAS, suggesting that when stocking densities increased to a certain level (e.g., exceed to 14.27 kg/m2) could negatively affected the growth performance of flatfish populations (Irwin et al., 1999; Sánchez et al., 2013). The FCR was remarkably stable and low during the first three months at all three densities, but it increased rapidly in the high-density group during the final growth period. Similar findings were reported in juvenile cod and rockfish (Sebastes schlegeli) (Björnsson et al., 2012; Hwang et al., 2014). In addition, the growth curves of juvenile turbot reared at three densities in RAS fitted by the Schnute model, were consistent with the growth model in turbot reported by Baer et al. (2011).
Notably, weight heterogeneity (often expressed as the CVw) has been suggested as an indicator of the social environment within fish populations, where a higher CVw may be attributed to increased stocking density and indicate inter-individual competition within the fish group (Irwin et al. 1999; North et al. 2006; Rowland et al. 2006). A few species such as piabanha (Brycon insignis) (Tolussi et al., 2010), pearl spot (Etroplus suratensis) (Biswas et al., 2013) and European sea bass (Dicentrarchus labrax) (Di Marco et al., 2008), showed homogeneous CVw in different stocking densities. In this study, the results indicated a higher CVw in HD treatment at the conclusion of the experiment suggesting that the fish weight in HD treatment varied drastically. Similar data were also reported in turbot cultured in small experimental RAS (Irwin et al., 1999).
Plasma cortisol levels may be an effective indicator of primary stress response in fish (Ellis et al., 2012). High stocking density has been considered as a chronic stressor producing a chronic elevation of plasma cortisol (Ellis et al., 2002; Bolasina et al., 2006). The fish reared under high density presented significantly higher cortisol levels on Day 120, implying that high density produced a stress response. This effect has been described in different species, including Japanese flounder and Senegalese sole (Bolasina et al., 2006; Salas-Leiton et al., 2010; Costas et al., 2013). Further, glucose is an important stress factor, which might be up-regulated with the increase in the cortisol level (van Raaij et al., 1996). However, the current study showed that glucose level was not affected by stocking density in turbot, which was consistent with reports in sea bass (Dicentrarchus labrax) and juvenile cod (Gadus morhua) (Foss et al., 2006; Di Marco et al., 2008). GH has also been associated with stress (Rodríguez et al., 2000; McCormick, 2001). Menezes et al. (2015) observed lower values of GH expression in silver catfish (Rhamdia quelen) reared at high density compared with low density. Similar results were displayed in our study with lower GH levels in MD and HD groups at the end of the trial.
5 Conclusions
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This study showed that juvenile turbot was efficiently cultured on a commercial scale in RAS. Our findings suggested that a stocking density up to 11.7 kg/m2 (corresponding to Day 110 in HD group) was not associated with any negative impact on the growth of juvenile turbot. However, the growth performance and the physiological response were adversely affected at a stocking density of 14.27 kg/m2 (Day 120). Our results provide a reference standard for the selection of stocking densities of turbot in commercial land-based RAS.
Funding
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  • The National Natural Science Foundation of China under contract Nos 31402315 and 31240012; the Modern Agriculture Industry System Construction of Special Funds under contract No. CARS-50-G10; the Key R & D Program of Jiangsu Province under contract No. BE2015328; a foundation from the Key Laboratory of Mariculture & Stock Enhancement in North China’s Sea, Ministry of Agriculture, China.
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Article Info
doi: 10.1007/s13131-017-0976-4
  • Receive Date：2016-05-24
  • Online Date：2026-04-16
  • Published：2017-06-01
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  • Received：2016-05-24
  • Accepted：2016-07-02
Funding
The National Natural Science Foundation of China under contract Nos 31402315 and 31240012; the Modern Agriculture Industry System Construction of Special Funds under contract No. CARS-50-G10; the Key R & D Program of Jiangsu Province under contract No. BE2015328; a foundation from the Key Laboratory of Mariculture & Stock Enhancement in North China’s Sea, Ministry of Agriculture, China.
Affiliations
    1 Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences; Key Laboratory of Sustainable Development of Marine Fisheries, Ministry of Agriculture; Qingdao Key Laboratory for Marine Fish Breeding and Biotechnology, Qingdao 266071, China
    2 Wuxi Fisheries College, Nanjing Agricultural University, Wuxi 214081, China
    3 Shandong Oriental Ocean Sci-Tech Co., Ltd, Yantai 264000, China

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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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