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Article

Effects of Feeding Frequency on Turbot (Scophthalmus maximus) Performance, Water Quality and Microbial Community in Recirculating Aquaculture Systems

by
Xiaoyang Guo
1,2,3,4,†,
Jiyuan Li
1,2,3,5,†,
Shihong Xu
1,2,3,
Xin Jiang
6,
Teng Guo
5,7,
Feng Liu
1,2,3,
Guang Gao
1,2,3,
Jun Li
1,2,3,
Yanfeng Wang
1,2,3,* and
Wei Jiang
8,*
1
CAS and Shandong Province Key Laboratory of Experimental Marine Biology, Centre for Ocean Mega-Science, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China
2
Laboratory for Marine Biology and Biotechnology, Qingdao National Laboratory for Marine Science and Technology Center, Qingdao 266071, China
3
State Key Laboratory of Breeding Biotechnology and Sustainable Aquaculture, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266000, China
4
School of Life Sciences, Qingdao Agricultural University, Qingdao 266109, China
5
University of Chinese Academy of Sciences, Beijing 100049, China
6
Yantai Institute, China Agricultural University, Yantai 264670, China
7
College of Marine Life Sciences and Institute of Evolution & Marine Biodiversity, Ocean University of China, Qingdao 266003, China
8
Observation and Research Station of Bohai Eco-Corridor, First Institute of Oceanography, Ministry of Natural Resources of the People’s Republic of China, Qingdao 266061, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Fishes 2025, 10(3), 125; https://doi.org/10.3390/fishes10030125
Submission received: 14 January 2025 / Revised: 24 February 2025 / Accepted: 26 February 2025 / Published: 12 March 2025
(This article belongs to the Section Sustainable Aquaculture)
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Abstract

Recirculating aquaculture systems (RAS) have promising applications in aquaculture. Feed is recognized as a major source of input to the RAS, and feeding frequency will not only impact the performance of turbot, but will also impact the quality of the cultured water. In order to rationally manage feeding and reduce aquaculture pollution, this study investigated the effects of feeding frequency on the performance of turbot (Scophthalmus maximus), nitrogen removal (ammonia and nitrite) characteristics and microbial communities in biofilters. The experiment was designed with three treatment groups, which were categorized into feeding once/day (FF1), feeding twice/day (FF2) and feeding three times/day (FF3) for 30 days. The results indicated that weight gain rate (WGR) and specific growth rate (SGR) significantly increased (p < 0.05) in the FF2 group and FF3 group compared with the FF1 group. The feed conversion ratio (FCR) was significantly lower (p < 0.05) in the FF2 group and FF3 group than in the FF1 group. There was no significant change in condition factor (CF). Ammonia and nitrite concentration decreased and water quality fluctuated less as the feeding frequency increased. FF2 showed the highest ammonia and nitrite removal rates. Feeding frequency did not significantly affect biofilter alpha diversity, but significantly altered beta diversity. PICRUSt functional prediction analysis revealed that the relative abundance of functional genes for nitrogen metabolism (amoA, amoB, amoC, hao, nxrA and nxrB) was highest in FF2. Therefore, feeding frequency of twice/day not only benefits the performance of turbot but also stabilizes the water environment and improves the removal of ammonia nitrogen and nitrite in RAS. These results provide theoretical and practical basis for further water improvement by seawater RAS.
Keywords:
feeding frequency; microbial community; ammonia removal; recirculating aquaculture systems (RAS)
Key Contribution: Increasing feeding frequency can slow down the fluctuation of water quality. Feeding frequency did not significantly affect biofilter alpha diversity, but significantly altered beta diversity. Feeding frequency (twice/day) showed the highest ammonia and nitrite removal rates.

1. Introduction

Recirculating aquaculture system (RAS) is an intensive model of aquaculture that is not limited by seasons and land, and is designed to maintain good water quality and provide safe, healthy and high-quality aquatic products [1]. The RAS is composed of several relatively independent units, such as culture units, solid separation units, and biofiltration units, among others [2]. In addition, the system receives daily water changes of 5–10% to prevent the accumulation of nitrates [3]. Compared with RAS, aquaculture wastewater generated by the traditional aquaculture model contains a large amount of organic matter and nutrients, and is usually discharged into the natural environment without any treatment, causing significant environmental pollution [4]. Kawasaki et al. showed that the release of excess nutrients into the environment is one of the main impacts of aquaculture on the surrounding environment [5]. RAS, as one of the aquaculture methods to minimize wastewater discharge, saves water by optimizing waste management and biofiltration, thus reducing the discharge of aquaculture wastewater to offshore areas [6]. Exogenous inputs in the RAS mainly come from feeds, unconsumed feeds and feces from feed digestion are discharged directly into the water, causing pollution of aquaculture water. This phenomenon will be further exacerbated when the cultured organisms are not fed properly. Feeding frequency, as an important feeding pattern, has gradually become the focus of RAS attention [7,8,9].
In aquaculture, nitrogen from fish excreta and food residues is released into culture water in the form of ammonia, which is toxic to aquatic animals, e.g., it damages the gills, viscera, and osmoregulatory functions of fish [10,11]. Secondly, ammonia can be further oxidized into nitrite, which is also detrimental to aquatic animals [12,13]. Therefore, the concentration of ammonia and nitrite in culture water is closely related to the welfare of fish. Biofilter as the core water treatment equipment of RAS, in which the attached microbial community plays a vital role in the treatment of aquaculture wastewater. Autotrophic ammonia-oxidizing bacteria (AOB) in the biofilter perform the first step to convert ammonia to nitrite, and nitrite-oxidizing bacteria (NOB) perform the second step to oxidize nitrite to nitrate. Heterotrophic bacteria also function in the biofilter, and these bacteria facilitate the removal of carbon metabolic wastes through assimilation [14,15]. Autotrophic and heterotrophic bacteria interact and compete with each other, thus aggregating into large, complex ecological networks [16,17].
The microbial community within the biofilter interacts with the environment, resulting in significant differences in community composition between RAS [18,19]. In general, the different operating conditions of RAS create a unique microbial community for the facility. Chen et al. showed that sucrose addition had a significant effect on the bacterial community in the biofilter, and carbon addition transformed the dominant bacterial taxa of the biofilter from the Proteobacteria dominated by Rhodobacteraceae to the Planctomycetes dominated by Planctomycetaceae [20]. Li et al. found that the number of ammonia-oxidizing archaea (AOA) OTU in the high-temperature (25 °C) biofilter was 1.9 and 1.5 times higher than that of the low-temperature biofilter (10 °C) and intermediate-temperature biofilter (19 °C), respectively, and the AOA were more predominant and diversified at the higher temperature [21]. Bernhard et al. showed that nitrification rates were inhibited under high salinity conditions and that salinity appeared to be an important factor in determining AOB distribution [22]. The performance of RAS biofilters, particularly the nitrification process, is also sensitive to changes in daily feeding, fish density, oxygen concentration and daily water exchanges [23,24]. Where salinity and temperature cannot be adjusted significantly, external inputs and organisms themselves become critical to the performance of seawater RAS biofilters. Feeding frequency not only affect food conversion rates and fish performance, but also aquaculture water quality. Feeding frequency cause fluctuations in water quality, thus directly affecting the efficiency of the biofilter. Improper feeding can lead to accumulation of waste and increase the levels of ammonia and nitrite in the water. Poxton and Allouse demonstrated that feeding turbot causes periodic fluctuations in ammonia and nitrite [25]. However, there are few studies on the effects of feeding frequency on biological communities in RAS biofilters.
Today, high-throughput DNA sequencing is widely used to study bacterial communities in the RAS [26,27,28,29]. 16S rDNA is the coding sequence of ribosomal features in prokaryotes, which is commonly used in the classification of bacteria and archaea as well as in the study of evolutionary relationships. In this study, 16S rDNA gene sequencing was utilized to investigate the bacterial communities in a turbot RAS biofilter under different feeding frequency. The study will provide useful information on the operation of the RAS and provide clues for further water purification, wastewater treatment.

2. Materials and Methods

2.1. Experimental Systems

The experiments were conducted at the Weihai Institute of Marine Biological Industry Technology, Chinese Academy of Sciences. The experimental area was equipped with recirculating aquaculture systems, each comprising a tank (1 m3), a whirl-separator, a mechanical microfilter, a protein separator, a decarbonization tower, a moving-bed biological filter (biofilter), a UV disinfection. Biofilter (0.80 m length, 0.30 m width and 0.70 m height) was filled with porous packing carrier as the substrate for biofilm growth. Each aquaculture tank is equipped with such a system, and each of these systems is identical to the one described above. During operation, the porous aeration disk was placed at the bottom of the filter, and aerated the reactor through an air compressor to provide sufficient dissolved oxygen for the growth of nitrifying bacteria, and stirred the porous packing carrier to form a moving bed structure. Membrane installation and start-up of biofilter refer to the method of Roalkvam et al. [30].

2.2. Experimental Design

Healthy, active and non-traumatized turbot (from Guoxin Oriental Circulating Water Aquaculture Base) were selected for the experiment. Turbots were divided into three groups and each group was acclimatized in the RAS for 30 days and fed twice daily at a feeding rate of 0.7% of the fish mass. Samples were taken at the end of the two culture periods, and the average weight of single turbot was 154.65 ± 6.83 g. The stocking density of 30 fish per cubic meter was chosen based on previous research and the carrying capacity of our system [31]. Each tank was stocked with 30 turbots, and the three groups were divided into three treatment groups, and each group was then subjected to a 30-day culture trial, with each treatment in triplicate. The first group (FF1) was fed once a day (8:00); the second group (FF2) was fed twice a day (8:00 and 20:00); and the third group (FF3) was fed three times a day (8:00, 16:00 and 24:00). The three treatment groups were fed the same daily amount of commercial pelleted feed (53% crude protein, 12% crude lipids, 16.0% crude ash, 4.0% crude fiber, 12% water, 0.5% P and 2.3% lysine) at a 0.7% feeding rate. FF2 dispensed one-half of the daily meal at 8:00 and 20:00, respectively. FF3 dispensed one-third of the daily meal at 8:00, 16:00 and 24:00, respectively. The amount of feed presented each day was recorded. 8:00 was set as 0 h after feeding, 9:00 was set as 1 h after feeding, and this pattern continued in increments of one hour over the next 24 h. Next, the daily water exchange rate was set at 5–10% and the water flow rate was approximately 0.18 m s−1 [32]. The temperature of the culture water was 18 ± 2 °C [33].

2.3. Growth Performance

At the end of experiment, 6 turbots were randomly sampled from each tank to measure their body weight (g) and body length (cm). Feed conversion ratio (FCR), weight gain rate (WGR), specific growth rate (SGR) and condition factor (CF) were separately calculated.
SGR = [ln(Wt) − ln(W0)]/t × 100
WGR = (Wt − W0)/W0 × 100
FCR = Ingested feed quantity/(Wt − W0)
CF = Wt/(body length)3 × 100
where W0 is the initial weight of the fish (g), Wt is the end weight of the fish (g) and t is number of feeding days.

2.4. Water Quality Parameters

Daily recordings of water parameters were taken at 08:00 am., including temperature, dissolved oxygen (DO), salinity and pH using a Handheld Multi-Parameter Water Quality Analyzer (YSI Incorporated, Yellow Springs, OH, USA). Temperature, DO, salinity and pH varied between 11.3 and 12.9 °C, 7.10 and 7.72 mg/L, 22.10 and 22.22 ‰ and 6.96 and 7.13, respectively. The water quality of different treatment groups after feeding was tracked and tested, and water samples were collected every 7 d from the inlet and outlet of the biofilter every 1 h for 24 h after feeding. The content of total ammonia and nitrite were measured in the water at each time point using Nessler’s reagent colorimetric method and the N-1-Naphthylethylenediamine photometric method (GB 13580.7–92), respectively [34,35].
Removal rate (%) for a water quality parameter = (biofilter influent concentration − biofilter effluent concentration)/biofilter influent concentration × 100%

2.5. DNA Extraction, PCR Amplification and Sequencing

On the last day, every ten porous filler carriers were used as one sample. Five samples were collected from each treatment group. Biofilm elution was performed with reference to Zhang et al. [36]. Microbial DNA was extracted using the HiPure Soil DNA Kits (Magen, Guangzhou, China), according to manufacturer’s protocols. The 16S rDNA V3-V4 region of the ribosomal RNA gene were amplified by PCR (94 °C for 2 min, followed by 30 cycles at 98 °C for 10 s, 62 °C for 30 s, and 68 °C for 30 s and a final extension at 68 °C for 5 min) using primers 341F: CCTACGGGNGGCWGCAG; 806R: GGACTACHVGGGTATCTAAT [37]. PCR reactions were performed in triplicate 50 μL mixture containing 5 μL of 10 × KOD Buffer, 5 μL of 2 mM dNTPs, 3 μL of 25 mM MgSO4, 1.5 μL of each primer (10 μM), 1 μL of KOD Polymerase and 100 ng of template DNA.
Amplicons were extracted from 2% agarose gels and purified using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA), according to the manufacturer’s instructions, and quantified using ABI StepOnePlus Real-Time PCR System (Life Technologies, Foster City, CA, USA). Purified amplicons were pooled in equimolar and paired end sequenced (2 × 250) on an Illumina platform, according to the standard protocols.

2.6. Processing of Sequencing Data

To obtain high quality and pure reads, FASTP was used to remove reads containing more than 10% unknown nucleotides (N) and to remove reads containing less than 50% bases with quality (Q-value) >20. Paired end clean reads were merged as raw tags using FLSAH (version 1.2.11) with a minimum overlap of 10bp and mismatch error rates of 2% [38]. Noisy sequences of raw tags were filtered by QIIME (version 1.9.1) pipeline under specific filtering conditions to obtain the high-quality clean tags [39,40]. Clean tags were searched against the reference database (version r20110519, http://drive5.com/uchime/uchime_download.html (accessed on 8 September 2023) to perform reference-based chimera checking using a UCHIME algorithm [41]. All chimeric tags were removed and finally obtained effective tags were used for further analysis.

2.7. Bioinformatics Analysis

The representative sequences were classified into organisms by a naive Bayesian model using RDP classifier (version 2.2) based on the SILVA database (version 132), with the confidence threshold values ranged from 0.8 to 1 [42,43]. The stacked bar plot of the community composition was visualized in R project ggplot2 package (version 2.2.1) [44]. Circular layout representations of species abundance were graphed using circos (version 0.69-3) [45].
For alpha diversity analysis, the Chao1 index (species richness) and Shannon index (community diversity) were computed with QIIME v1.9.1 [40]. OTU rarefaction curve were plotted in R project ggplot2 package (version 2.2.1) [44]. The Bray–Curtis distance matrix was calculated in the R project Vegan package (version 2.5.3) [46]. PCoA (principal coordinates analysis) of Bray–Curtis distances were generated in the R project Vegan package (version 2.5.3) and plotted in the R project ggplot2 package (version 2.2.1) [44,46].
A Venn analysis was performed between the groups in the R project VennDiagram package (version 1.6.16) and an upset plot was performed in the R project UpSetR package (version 1.3.3) to identify unique and common Species [47,48]. Biomarker features in each group were screened by LEfSe software (version 1.0) in the R project [49]. The KEGG pathway analysis of the OTUs was inferred using PICRUSt (version 2.1.4) [50].
Species comparison, alpha diversity index and analysis of function difference among clusters and groups were computed by the Kruskal–Wallis H test in the R project Vegan package (version 2.5.3) [46].

2.8. Ethics Statement

Fish treatment was approved by the Animal Protection and Utilization Committee of the Institute of Oceanography, Chinese Academy of Sciences.

2.9. Statistical Analysis

The growth performance data of turbot, including weight gain rate (WGR), specific growth rate (SGR), feed conversion ratio (FCR) and condition factor (CF), were analyzed using one-way ANOVA with IBM SPSS Statistics 26 to test for significant differences among treatment groups. A significance level of p < 0.05 was applied. For microbial community data, alpha diversity (Chao1 and Shannon indices) and beta diversity (Bray–Curtis distance) were calculated using QIIME v1.9.1. Differences in microbial community composition and functional predictions were assessed using LEfSe and PICRUSt2 analyses. All statistical analyses were performed with a significance level of p < 0.05.

3. Result

3.1. Turbot Growth

WGR were significantly increased (p < 0.05) in the FF2 group and FF3 group compared with the FF1 group (Figure 1A). Similar trends were observed for SGR under different feeding frequency (Figure 1B). In addition, another key indicator, FCR, was also significantly lower (p < 0.05) in the FF2 group and FF3 group than in the FF1 group (Figure 1C). No significant change in CF (Figure 1D).

3.2. Water Quality

Temperature, salinity, DO and pH were relatively stable and not significantly different between groups throughout the experiment. In Figure 2, the FF1 group 24 h ammonia concentration range is 0.59–1.1 mg/L and the nitrite concentration range is 0.25–0.30 mg/L. The average removal rate of ammonia and nitrite is 18.46% and 5.75%, respectively. The 24 h ammonia concentration range is 0.55–0.92 mg/L and the nitrite concentration range is 0.24–0.29 mg/L in FF2 group. The average removal rates of ammonia and nitrite were 22.49% and 6.93%, respectively. The 24 h ammonia concentration in FF3 group ranged from 0.49 to 0.76 mg/L and the nitrite concentration ranged from 0.24 to 0.29 mg/L. The average removal rates of ammonia and nitrite were 20.33% and 5.77%, respectively. Both ammonia and nitrite peaked in concentration 4–6 h after feeding at different feeding frequency.

3.3. Microbial Community Composition and Structure

A total of 1,919,515 clean reads were obtained from 15 biofilm samples, resulting in 24,024 unique OTUs. At the phylum level, the bacterial community of the biofilm was mainly composed of Proteobacteria, Verrucomicrobiota, Bacteroidota, Planctomycetota and Chloroflexi. In the biofilm of FF1, the proportions of the above five phylum were Proteobacteria (43.77%), Verrucomicrobiota (22.71%), Bacteroidota (15.56%), Planctomycetota (7.50%) and Chloroflexi (1.64%). In the biofilm of FF2, the percentage of Proteobacteria, Verrucomicrobiota, Bacteroidota, Planctomycetota and Chloroflexi were 38.82%, 23.44%, 13.07%, 13.22% and 2.81%, respectively. Among the biofilms of FF3, the percentages of Proteobacteria, Verrucomicrobiota, Bacteroidota, Planctomycetota and Chloroflexi were 40.00%, 27.86%, 12.93%, 7.14% and 2.99%, respectively (Figure 3).
At genus level, the dominant bacteria in the three groups were Rubritalea (16.16–18.10%), Planktotalea (5.14–18.47%), Persicirhabdus (3.59–7.23%), Bythopirellula (2.36–5.80%) and Pseudophaeobacter (0.59–4.70%) (Figure 4).

3.4. Microbial Community Diversity

Alpha diversity is an indicator of the abundance and diversity of species in a given habitat or ecosystem and is usually calculated using two key indicators: species richness (variety) and species evenness (distribution). The Shannon index reflects the uniformity of the microbial community. Chao index reflects the richness of the microbial community. The rarefaction curve of both Shannon and Chao reached the plateau period. The Shannon and Chao indices of the FF1 group were 6.15 and 1580.98, respectively; the Shannon and Chao indices of the FF2 group were 6.46 and 1616.54, respectively; the Shannon and Chao indices of the FF3 group were 6.05 and 1524.26, respectively; the Shannon and Chao indices of the FF2 group were the highest, but there was no significant difference compared to the FF1 and FF3 groups.
Beta diversity is a comparison of diversity between different ecosystems and is used to represent the response of biological species to environmental heterogeneity. In general, the calculation of Beta diversity of communities under different environmental gradients consists of both species alteration and species generation. Based on these two important metrics, beta diversity analysis was conducted using the Bray index. In the PCoA analysis, PCO1 (44.65%) was much higher than PCO2 (26.67%), indicating that PC1 was the main factor influencing the composition of the bacterial community (Figure 5). The three treatment groups were clearly separated in the direction of PCO1. Based on the division results, the Anosim grouping test was performed, and the p-value of FF1-vs-FF2-vs-FF3 was 0.001, indicating that there were significant differences among the three clusters (Figure 6).

3.5. Microbial Community Differences

The Venn diagram shows the similarities and differences between the three treatment groups directly at the OTU level. The FF1 group had a total of 1173 OTUs, the FF2 group had a total of 1207 OTUs and the FF3 group had a total of 1139 OTUs. There were 656 identical OTUs in the 3 treatment groups. The FF1 group had 363 unique OTUs, the FF2 group had 344 unique OTUs and the FF3 group had 304 unique OTUs (Figure 7).
Differential abundance taxa (biomarkers) at the genus level were identified using LEfSe analysis in three clusters categorized by feeding frequency. Using LDA = 3.6 as a threshold, four classes, three orders, three families and seven genera were identified. The seven genera are Planctomicrobium, Rhodopirellula, Bythopirellula, Pseudophaeobacter, Planktotalea, Flavobacterium and Persicirhabdus (Figure 8).

3.6. Functional Prediction

The microbial function at different feeding frequency was predicted by PICRUSt2 in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. As shown in Figure 9, the most active metabolic pathways (level 1) were metabolism (81.73–82.19%), followed by genetic information processing (11.38–11.60%), cellular processes (4.32–5.10%), environmental information processing (1.98–2.19%), human diseases (0.25–0.30%) and organismal systems (0.25–0.26%). In the metabolic subsystem, the main metabolic patterns (level 2) were amino acid metabolism (13.53–13.73%), carbohydrate metabolism (13.38–13.67%), metabolism of cofactors and vitamins (12.08–12.34%) and metabolism of terpenoids and polyketides (8.22–8.31%). The key enzyme genes that participate in nitrification and denitrification were investigated using PICRUSt2, genes associated with nitrification had higher abundances in FF2 (Figure 10).

4. Discussion

Feeding frequency is an important component of fish culture production management. The frequency of feeding is an essential factor in fish intake, growth and metabolism [51]. Strategic optimization of feeding frequency and temporal adjustment of feeding schedules can significantly enhance feed utilization efficiency, thereby accelerating fish growth [52]. Moreover, these practices can optimize feed conversion ratios, ultimately contributing to improved productivity and economic viability within aquaculture systems. Sun et al. showed that the most suitable feeding amount and feeding frequency for the growth of Atlantic salmon (Salmo salar) in RAS were 1.6% body mass a day and four meals a day, respectively [53]. Gao et al. demonstrated that feeding frequency of four times/day more closely align with the digestive physiological characteristics of juvenile tiger puffer fish (Takifugu rubripes), resulting in optimal growth performance and physiological indices [54]. Rahman and Lee showed that spotted seabass (Lateolabrax maculatus) performance was not significantly enhanced when feeding was increased from two to three times daily. [55]. In this study, WGR and SGR were significantly higher (p < 0.05) and FCR significantly lower (p < 0.05) in the FF2 and FF3 groups than in the FF1 group, which was consistent with the study by Li et al. [56]. This suggests that an appropriate increase in feeding frequency is beneficial to the growth of turbot. Wang et al. indicated that increased feeding frequency significantly enhanced turbot growth and protein retention by 7.68% ± 0.53% and 4.01% ± 0.59%, respectively. Generally, feed costs account for 30–70% of the total cost of aquaculture [57]. Therefore, precise feeding should be implemented in RAS according to the feeding habits of different species, which can promote the performance of culture animals and save costs and improve efficiency.
In the RAS, ammonia and nitrite are primary metabolic wastes from residual feed and fish excretion [58]. Ammonia and nitrite are toxic to aquatic organisms, causing stress and oxidative damage to aquatic organisms [13,59]. Therefore, the elimination of ammonia and nitrite in the aquatic environment of fish culture is crucial. In this study, ammonia and nitrite concentrations in the effluent of the RAS biofilter were lower than those in the influent, and ammonia and nitrite could be oxidized by the bacteria in the biofilter within 24 h, which indicated that the biofilter in the RAS played a certain role in the removal of ammonia and nitrite and ensured the health and stability of the aquaculture environment [60]. Peak concentrations of both ammonia and nitrite occurred 4–6 h after feeding, which may be related to the accumulation of fecal and ammonia excretion in turbot. Dosdat et al. showed that sea bass (Dicentrarchus labrax), sea bream (Sparus auratus), and turbot showed peak ammonia excretion within 2–4 h after feeding [61]. Fang et al. found that maximum ammonia excretion was observed in Mandarin Fish (Siniperca chuatsi) and Grass Carp (Ctenopharyngodon idellus) at 4–8 h and 2–4 h after feeding, respectively, which is similar to our findings [62]. The rate of ammonia excretion began to increase after feed intake and continued to increase until it reached a maximum level, after which it decreased until the start of the next feed [63,64]. In this study, ammonia and nitrite in the FF3 group showed three peaks but the least fluctuation, and ammonia and nitrite in the FF1 group showed one peak but the greatest fluctuation, which suggests that increasing feeding frequency can alleviate the drastic changes in water quality. Hou et al. showed that water quality indicators were lower at eight feedings/day than at four feedings/day, and the daily variation was small [9]. Phillips et al. showed that ammonia concentration decreased with increasing feeding frequency [50]. Therefore, reasonable adjustment of feeding frequency is conducive to maintaining the stability of aquaculture water environment and can reduce the pollution of aquaculture wastewater to a certain extent. The feeding schedule in FF2 (8:00 and 20:00) may align better with the circadian rhythm of digestive enzyme activity in turbot, leading to more efficient feed utilization and reduced ammonia excretion peaks. Previous studies have shown that feeding times can influence the digestibility of diets due to the circadian rhythm of enzyme activity [65]. In our study, the FF2 group exhibited the highest ammonia and nitrite removal rates, likely due to the balanced feeding intervals that minimized fluctuations in water quality. This suggests that the timing of feeding plays a crucial role in optimizing nitrogen removal in RAS.
Biofilter, as an important part of RAS, which purifies water through the microbial processes like nitrification [66,67]. Proteobacteria and Verrucomicrobiota were found to be the predominant phylum in seawater RAS biofilters [67,68,69,70]. Proteobacteria are diverse members in RAS biofilters and play important roles in reactions such as nitrification, anaerobic ammonia oxidation and denitrification [71,72,73]. Verrucomicrobiota members significantly contribute to the carbon cycle, possessing a large number of genes involved in carbohydrate degradation [71,74,75]. In addition, Verrucomicrobiota does not appear to utilize nitrate or other nitrogenous organic compounds, but rather absorbs and utilizes non-ionized ammonia [76]. In our study, high microbial diversity was observed in all three treatment RAS biofilters, with Proteobacteria, Verrucomicrobiota and Bacteroidota as the dominant phylum, which is consistent with the bacterial community analysis of other seawater RASs [70]. Beta diversity analysis showed that the bacterial communities of FF1, FF2 and FF3 biofilters were clearly classified into three classes, which implies that specific bacterial communities were formed in each RAS under different feeding frequency. Seven genera of bacteria (Planctomicrobium, Rhodopirellula, Bythopirellula, Pseudophaeobacter, Planktotalea, Flavobacterium, Persicirhabdus) were identified as biomarkers through LEfSe analysis. Planktotalea, Flavobacterium and Persicirhabdus as FF3 biofilter biomakers, in which both Flavobacterium and Persicirhabdus have denitrification capacity and play an important role in the nitrogen cycling process [77,78]. Planctomicrobium, Rhodopirellula, Bythopirellula, Pseudophaeobacter act as FF2 biofilter biomakers, all of which play a role in the marine carbon cycle. Among them, Rhodopirellula is a chemoheterotrophic bacterium widely distributed in marine sediments [79]. In the limitation of ammonia, Rhodopirellula lived freely and did not form aggregates, and the presence of ammonia initiated cell aggregation and induced Rhodopirellula to attach to particles and form biofilms [80]. However, He et al. showed that feeding frequency altered the total post feeding ammonia excretion and digestion process of snakehead (Channa argus) [81]. Guan et al. demonstrated that the feeding frequency changed feed residence time in the intestines and stomach, as well as the activities of digestive enzymes in Dabry’s sturgeon (Acipenser dabryanus) [82]. We hypothesized that feeding frequency may alter digestion and absorption in the gut of turbot, thus altering the C/N in the water. The C/N ratio of FF2 may be more favorable for biofilm formation and decomposition of organic matter in biofilters.
The proportion of nitrifying bacteria in the microbial community as the key bacteria for nitrogen control in RAS biofilters is quite different from previous reports. In this study, Nitrosomonas was the dominant AOB and Nitrospira was the dominant NOB. The proportions of Nitrosomonas in the biofilter communities of FF1, FF2 and FF3 biofilters were 0.47%, 0.55% and 0.31%, respectively. The proportions of Nitrospira in the biofilter communities of FF1, FF2 and FF3 biofilters were 0.98%, 0.92% and 0.30%, respectively. This is quite different from the proportions of Nitrosomonas (2.4%) and Nitrospira (12.3%) found in the study by Roalkvam et al. [30]. This may be due to the low nitrogen input in the experimental RAS and the slow growth rate of chemoautotrophic nitrifying bacteria, which resulted in their low proportion in the bacterial community. Ammonia and nitrite removal in biofilters may not be limited to traditional ammonia-oxidizing bacteria (Nitrosomonas, Nitrosospira and Nitrosococcus) and nitrite-oxidizing bacteria (Nitrobacter, Nitrospina and Nitrospira) [83]. In microbial ecosystems, degradation of organic pollutants and transformation of nutrients depend on the whole system, not on individual microbial species [16]. Generally, the removal of ammonia and nitrite in biofilters is related to nitrifying enzymes. The first step in the nitrification process is accomplished by two enzymes: ammonia monooxygenase, which oxidizes ammonia to hydroxylamine, and hydroxylamine dehydrogenase, which oxidizes hydroxylamine to nitrite [84,85,86,87]. In this step, the rate of nitrification is controlled by ammonia monooxygenase, a key enzyme in all known bacterial and archaeal ammonia oxidizing bacteria [87]. Second, all NOBs oxidize nitrite to nitrate using nitrite oxidoreductase, which is encoded by two genes (nxrA and nxrB) [88,89,90]. PICRUSt2 predictions in this study indicated the highest abundance of key enzyme genes (amoA, amoB, amoC, hao, nxrA and nxrB) in FF2, which is compatible with the highest ammonia nitrogen removal rate in FF2.
With the operation of the RAS, nitrate converted from ammonia and nitrite will always accumulate. Although low concentrations of nitrate are often considered non-toxic, high concentrations of nitrate can often contribute to eutrophication in aquatic ecosystems and even jeopardize the health and growth of aquatic animals [91,92,93]. Nitrate removal occurs mainly by denitrification, a process carried out by parthenogenetic anaerobic microorganisms [92]. However, due to the aeration of the RAS biofilter, the DO concentration was at a high level (above 7.00 mg/L), and denitrification in the biofilter was greatly inhibited, ultimately leading to the accumulation of nitrate. The presence of denitrifying bacteria was detected in this experimental system. We hypothesize that although all biofilters have relatively high DO levels, micro anaerobic zones evolved in the deeper parts of the biofilm. Micro anaerobic zones allow the presence of denitrifying bacteria in RAS biofilters, which may play an important role in nitrogen removal, especially in the absence of a denitrification reactor unit [94]. Some members of Planctomycetes function as anammox bacteria, converting ammonia and nitrite to dinitrogen gas under oxygen limitation [95]. In addition, a recent study found that Flavobacteriaceae of Bacteroidota, a parthenogenetic anaerobic bacterium, can utilize nitrate as an electron acceptor for anaerobic respiration, revealing that denitrification may be occurring in biofilters [95]. Therefore, the question of how to remove the accumulated nitrate efficiently will be an important issue for seawater RAS and needs to be further investigated in the near future.
The primary objective of this study was to investigate the effects of different feeding frequencies on turbot performance, water quality and microbial community dynamics in a recirculating aquaculture system (RAS). Our findings demonstrate that feeding frequency significantly influences turbot growth, with FF2 (two meals per day) showing the highest weight gain rate (WGR) and specific growth rate (SGR), as well as the lowest feed conversion ratio (FCR). Additionally, water quality parameters, particularly ammonia and nitrite concentrations, were more stable in FF2, suggesting that this feeding frequency aligns better with the digestive physiology of turbot and promotes efficient nitrogen removal. The microbial community analysis further revealed that FF2 supported a more balanced and diverse bacterial community, with higher abundances of key nitrifying bacteria and enzymes, contributing to enhanced ammonia and nitrite removal. These results collectively highlight the importance of optimizing feeding frequency to improve turbot performance, maintain water quality and support a stable microbial ecosystem in RAS. Future studies should focus on further refining feeding strategies to maximize productivity and sustainability in turbot aquaculture.

5. Conclusions

In the seawater RAS, ammonia and nitrite concentration decreased and water quality fluctuated slower as the feeding frequency increased. FF2 showed the highest ammonia and nitrite removal rates. Feeding frequency did not significantly affect biofilter alpha diversity but significantly altered beta diversity. PICRUSt functional prediction analysis showed that the relative abundance of functional genes for nitrogen metabolism (amoA, amoB, amoC, hao, nxrA and nxrB) was highest in FF2. These results provide a theoretical and practical basis for seawater RAS to further improve water quality.

Author Contributions

X.G. was responsible for writing—original draft, data curation and validation. J.L. (Jiyuan Li) was responsible for writing—original draft and data curation. S.X. was responsible for investigation and methodology. X.J. was responsible for investigation and methodology. T.G. was responsible for formal analysis. F.L. was responsible for software. G.G. was responsible for investigation. J.L. (Jun Li) was responsible for formal analysis. The corresponding author Y.W. was responsible for writing—review and editing, supervision, project administration and validation. The corresponding author W.J. was responsible for validation and supervision. All authors have read and agreed to the published version of the manuscript.

Funding

China Agriculture Research System (CARS-47-G21).

Institutional Review Board Statement

The Animal Protection and Utilization Committee of the Institute of Oceanography, Chinese Academy of Sciences. Approval Code: IOCAS20230705PPFA0003 Approval Date: 5 July 2023.

Informed Consent Statement

Not applicable.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors on request.

Acknowledgments

Special thanks to China Weihai Institute of Marine Biotechnology (Zhe Liu, Xiaoyang Ma, Jialin Li, Jingqiang Yang) and Meng Li and Zuoliang Sun of Ocean University of China for their generous support.

Conflicts of Interest

The authors declare that authors Xin Jiang was employed by Shenzhen Jingyu Technology Co., Ltd. Xin Jiang carried out investigation and methodology the manuscript during the research. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Figure 1. Effect of different feeding frequencies on growth performance. (A) Weight gain rate (WGR), (B) specific growth rate (SGR), (C) feed conversion ratio (FCR) and (D) condition factor (CF). The vertical bars represent mean ± S.E. Different lowercase letters indicate significant differences (p < 0.05).
Figure 1. Effect of different feeding frequencies on growth performance. (A) Weight gain rate (WGR), (B) specific growth rate (SGR), (C) feed conversion ratio (FCR) and (D) condition factor (CF). The vertical bars represent mean ± S.E. Different lowercase letters indicate significant differences (p < 0.05).
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Figure 2. Variations and removal rates of total ammonia nitrogen (TAN, N −NH3 + N −NH4+) and nitrite-nitrogen (NO2− −N) under different feeding frequencies. (A) TAN, N −NH3 + N −NH4+ concentration, (B) TAN, N −NH3 + N −NH4+ removal rate, (C) NO2− −N concentration, (D) NO2− −N removal rate.
Figure 2. Variations and removal rates of total ammonia nitrogen (TAN, N −NH3 + N −NH4+) and nitrite-nitrogen (NO2− −N) under different feeding frequencies. (A) TAN, N −NH3 + N −NH4+ concentration, (B) TAN, N −NH3 + N −NH4+ removal rate, (C) NO2− −N concentration, (D) NO2− −N removal rate.
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Figure 3. Taxonomic assignment of bacteria in biofilm at phylum.
Figure 3. Taxonomic assignment of bacteria in biofilm at phylum.
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Figure 4. Circos diagram of the relationship between samples and genus.
Figure 4. Circos diagram of the relationship between samples and genus.
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Figure 5. The PCoA of bacterial communities in biofilm based on Bray.
Figure 5. The PCoA of bacterial communities in biofilm based on Bray.
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Figure 6. UPGMA tree and taxonomic assignment of bacterial samples at the genus level.
Figure 6. UPGMA tree and taxonomic assignment of bacterial samples at the genus level.
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Figure 7. Unique and shared OTUs in biofilms at different feeding frequency.
Figure 7. Unique and shared OTUs in biofilms at different feeding frequency.
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Figure 8. Biomarkers identified under different feeding rhythms. (A) is the LDA score, which identified the size of differentiation among the three clusters with a threshold value of 3.6. (B) Differences were then mapped to taxonomic trees.
Figure 8. Biomarkers identified under different feeding rhythms. (A) is the LDA score, which identified the size of differentiation among the three clusters with a threshold value of 3.6. (B) Differences were then mapped to taxonomic trees.
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Figure 9. PICRUSt2 predictions of the functional composition of the biofilm samples. (A) represents the abundance of the KEGG pathway at level 1, (B) represents the abundance of the main pathways at level 2.
Figure 9. PICRUSt2 predictions of the functional composition of the biofilm samples. (A) represents the abundance of the KEGG pathway at level 1, (B) represents the abundance of the main pathways at level 2.
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Figure 10. Heatmap plot of predicted functional genes involved in nitrogen metabolism according to KEGG orthology.
Figure 10. Heatmap plot of predicted functional genes involved in nitrogen metabolism according to KEGG orthology.
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Guo, X.; Li, J.; Xu, S.; Jiang, X.; Guo, T.; Liu, F.; Gao, G.; Li, J.; Wang, Y.; Jiang, W. Effects of Feeding Frequency on Turbot (Scophthalmus maximus) Performance, Water Quality and Microbial Community in Recirculating Aquaculture Systems. Fishes 2025, 10, 125. https://doi.org/10.3390/fishes10030125

AMA Style

Guo X, Li J, Xu S, Jiang X, Guo T, Liu F, Gao G, Li J, Wang Y, Jiang W. Effects of Feeding Frequency on Turbot (Scophthalmus maximus) Performance, Water Quality and Microbial Community in Recirculating Aquaculture Systems. Fishes. 2025; 10(3):125. https://doi.org/10.3390/fishes10030125

Chicago/Turabian Style

Guo, Xiaoyang, Jiyuan Li, Shihong Xu, Xin Jiang, Teng Guo, Feng Liu, Guang Gao, Jun Li, Yanfeng Wang, and Wei Jiang. 2025. "Effects of Feeding Frequency on Turbot (Scophthalmus maximus) Performance, Water Quality and Microbial Community in Recirculating Aquaculture Systems" Fishes 10, no. 3: 125. https://doi.org/10.3390/fishes10030125

APA Style

Guo, X., Li, J., Xu, S., Jiang, X., Guo, T., Liu, F., Gao, G., Li, J., Wang, Y., & Jiang, W. (2025). Effects of Feeding Frequency on Turbot (Scophthalmus maximus) Performance, Water Quality and Microbial Community in Recirculating Aquaculture Systems. Fishes, 10(3), 125. https://doi.org/10.3390/fishes10030125

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