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Article

Effects of Stocking Density of Filter-Feeding Fishes on Water Quality and Bacterial Community in Rice–Crayfish Polyculture System

by
Yuanyuan Zhang
1,
Liangjie Zhao
2,
Jiaoyang Duan
3,
Yongtao Tang
2 and
Jun Lv
1,*
1
Henan Academy of Fishery Sciences, Zhengzhou 450044, China
2
College of Fisheries, Xinyang Agriculture and Forestry University, Xinyang 464000, China
3
Centre for Research on Environmental Ecology and Fish Nutrition of the Ministry of Agriculture, Shanghai Ocean University, Shanghai 201306, China
*
Author to whom correspondence should be addressed.
Water 2024, 16(16), 2296; https://doi.org/10.3390/w16162296
Submission received: 14 July 2024 / Revised: 5 August 2024 / Accepted: 13 August 2024 / Published: 14 August 2024
(This article belongs to the Section Biodiversity and Functionality of Aquatic Ecosystems)
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Abstract

:
To evaluate the effects of filter-feeding fishes on water quality and bacterial community in the rice–crayfish coculture system, four different stocking densities of bighead carp (0, 500, 1000, 1500 ind./200 m2) were set up in rice–crayfish coculture systems. Water samples in the systems were collected biweekly to detect dissolved oxygen (DO), temperature (T), potential of Hydrogen (pH), ammonia nitrogen (NH4+-N), nitrite nitrogen (NO2−-N), nitrate nitrogen (NO3−-N), total nitrogen (TN), total phosphorus (TP), and Chlorophyll-a (Chl-a); the bacterial community in the water was analyzed simultaneously, then the correlation between water quality and microorganisms were studied. The results showed that concentrations of TN, TP, NO2−-N, and NH4+-N decreased while DO and NO3−-N increased along with the breeding process. NO2−-N, NO3−-N, TN, and NH4+-N were important environmental factors affecting the bacterial community structure in water (p < 0.05). Bighead carp stocking had an impact on the diversity, richness, and evenness of the bacterial communities in the systems. The dominant bacteria in the four different carp density groups were Proteobacteria, Actinomycetes, Bacteroidetes, and Cyanobacteria. Bighead carp increased the abundance of Bacteroidea but reduced that of Actinomycetes, Cyanobacteria, and Proteobacteria. The introduction of bighead carp promoted the conversion of nitrogen and phosphorus, reducing the risk of cyanobacterial blooms. Group 1000 ind./200 m2 exhibited the best effect on the removal of nitrogen and phosphorus from the water body.

1. Introduction

Integrated rice and fish farming is one of the main models of ecological circular agriculture in China today. It integrates rice planting with aquaculture to establish a diversified and multi-level symbiotic system. It utilizes natural resources such as water, soil, weeds, aquatic animals, and insects to leverage the mutual benefits between rice and aquatic animals [1]. This not only improves the water environment, increases the activity frequency and range of aquatic animals, and enhances the quality of aquatic products, but also promotes the recycling of nutrients in the rice fields. It achieves biological control of rice diseases and pests as well as field weeds, reducing the use of pesticides and chemical fertilizers, thereby decreasing agricultural environmental pollution [2]. The main models of rice and fish integrated farming currently carried out in China include rice–crayfish (Procambarus clarkii), rice–loach (Misgurnus anguillicaudatus), rice–crab (Eriocheir sinensis), rice–turtle (Trionyx sinensis), rice–carp, rice–frog (Pelophylax nigromaculatus), and rice–field snails (Cipangopaludina chinensis) [3]. Rice–crayfish coculture has become the most popular mode with the largest culturing area and highest production, and it is also the main culture mode of crayfish in China [4].
Microorganisms are extensively found in environments such as water bodies, soil, and air [5]. They are numerous and diverse, constituting an important biological component of agricultural ecosystems. They decompose substances like chemical fertilizers, pesticides, waste feed, and excrement, driving the material cycles and energy flow of the entire ecosystem and playing a crucial role in promoting energy flow and material circulation. Moreover, the composition of microbial communities is closely related to water quality conditions and can reflect the water quality status and the health of the aquatic ecological structure of specific ecosystems [6]. Zhang et al.’s study on the integrated culture model of rice–crayfish–eel has shown that the introduction of eels had a significant impact on the water and bacterial composition in the rice–crayfish coculture fields [7,8]. It is of great significance for further understanding the role and mechanism of microorganisms in artificial ecosystems, providing a reference for ecological protection, healthy aquaculture, and human health.
Bighead carp (Aristichthys nobilis) are one of the four major species of Chinese carp, widely cultured in ponds as a mode of polyculture, and in lakes and reservoirs as a measure of stock-enhanced fishery. Due to their ability to filter-feed on phytoplankton, they are often used to enhance water conditions and to control the overgrowth of phytoplankton as an approach to biomanipulation in lakes, reservoirs, and ponds in China [9]. A multitude of research has indicated that bighead carp can effectively control algal growth and cyanobacteria blooms, especially in lakes and reservoirs [10,11,12]. In this study, we used different densities of bighead carp to test the feasibility of using these carp to improve water quality in a rice–crayfish system. The objectives are as follows: (1) to evaluate the effects of different densities of bighead carp on water conditions and bacterial populations; (2) to innovate a novel coculture mode that harmonizes water quality and production efficiency.

2. Materials and Methods

2.1. Experiment Design and Management

The experiment was carried out in Luoshan County (32°8′7.41″ N, 114°29′25.50″ E), Xinyang City, Henan province, China. Ten 200 m2 paddy fields were set up and a ditch (1.5 m deep, 0.9 m wide, 1:1.2 slope) was excavated in each field as a refuge for crayfish. A 0.3 m high nylon net was built around each side of the field to prevent crayfish from escaping. Rice plants were transplanted on 15 July, and the rice planting density was 30 cm × 30 cm. One control group (RC) and three experimental groups (RCA1, RCA2, RCA3) (Table 1) were set up for this study. Each group had three replicates. A total of 1000 crayfish (2–5 g/ind.) were stocked in each field on 1 August 2020. One week later, bighead carp (5–10 g/ind.) were placed into paddy fields. Table 1 presents the initial group and stocking data [13]. Initially, the animals were allocated feed at a rate of 2% of their body weight, with subsequent adjustments based on their intake. Crayfish and bighead carp were purchased from local fish breeding farms in Luoshan County. There was no fertilization or drainage during the culture experiment, and groundwater was supplemented every 15 days to compensate for water loss due to evaporation. The water depth of the field was kept at 10–12 cm during the experiment. The culture experiment lasted for a period between July and October 2020. After the breeding was completed, different groups of crayfish and bighead carp were caught for yield measurement.

2.2. Sample Collection and Analysis

Water samples were collected at 10 a.m. on 22 August, 6 September, 24 September, 11 October, and 25 October, respectively. Water temperature (T), dissolved oxygen (DO), and potential of Hydrogen (pH) were determined by YSI in situ. Water samples of 1.5 L at 0.5 m below the water surface layer were collected for chemical and biological analyses in the laboratory, among which, 1 L water samples were used for the determination of total nitrogen (TN), total phosphorus (TP), ammonium nitrogen (NH4+-N), nitrate nitrogen (NO3−-N), nitrite nitrogen (NO2−-N), and chlorophyll a (Chl-a), according to the China national standards for testing surface and groundwater, and for wastewaters [14]. The remaining 0.5 L water samples were filtered with a 0.22 um GF/C filter membrane (Whatman, Clifton, NJ, USA) to collect microorganisms. The filtered membrane was installed in a 1.5 mL sterile EP tube and stored in a refrigerator at −20 °C for DNA extraction.

2.3. DNA Extraction, PCR Amplification and Sequencing

The filtered membranes were divided into segments with dry heat-sterilized scissors and tweezers, and then comprehensively crushed using a fully automatic rapid-grinding apparatus. The total DNA of microorganisms on the filter was extracted using the E.Z.N.A.® soil DNA Kit (Omega Bio-tek, Norcross, GA, USA). After genomic DNA extraction, the extracted genomic DNA was detected by 1% agarose gel electrophoresis. The universal primer sets for amplification of 16S rRNA gene (V4–V5) were 515F(5′-GTGCCAGCMGCCGCGG-3′) and 907R(5′-CCGTCAATTCMTTTRAGTTT-3′). The PCR mixtures contained 5 × TransStartFastPfu buffer 4 μL, 2.5 mM dNTPs 2 μL, forward primer (5 μM) 0.8 μL, reverse primer (5 μM) 0.8 μL, TransStartFastPfu DNA Polymerase 0.4 μL, template DNA 10 ng, and finally ddH2O up to 20 μL. In this experiment, high-throughput sequencing of samples and preliminary processing of data was performed on an Illumina MiSeq platform according to the standard protocols at Majorbio Bio-Pharm Technology Co., Ltd. (Shanghai, China) [15]. The raw reads were deposited into the NCBI Sequence Read Archive (SRA) database (Accession Number: PRJNA1143720).

2.4. Statistical Analysis

The raw 16S rRNA gene sequencing reads were demultiplexed, quality-filtered by fastp version 0.20.0, and merged by FLASH version 1.2.7. Operational taxonomic units (OTUs) with a 97% similarity cut-off were clustered using UPARSE version 7.1, and chimeric sequences were identified and removed [15]. Excel 2010 was used for data sorting and statistics, and all data were represented by mean ± SD. One-way ANOVA and multiple comparative analyses were performed using SPSS 19.0, with p < 0.05 as the significant level of difference. The bacterial composition and diversity of different groups, principal coordinates analysis (PCoA) analysis on the correlation between water quality and bacterial composition, and redundancy analysis (RDA) were analyzed using the Majorbio Cloud platform (https://cloud.majorbio.com, accessed on 10 February 2021). All data were tested for normality and homogeneity of variance before analysis, and logarithmic transformations were performed if necessary to comply with hypothesis testing for analysis of variance [14].

3. Results

3.1. Water Quality

Nine water quality parameters, including DO, T, pH, NO3−-N, NO2−-N, TN, TP, NH4+-N, and Chl-a, were tested five times during the experiment, in which seven parameters (DO, TN, TP, NO2−-N, NO3−-N, NH4+-N, and Chl-a) were found to have significant effects on the bacterial community, as shown in Figure 1.
DO content in each field varied between 2.83 and 7.80 mg/L during the experiment. It decreased in the first interval of monitoring and then increased continually, reaching the peak at the end of the experiment. The DO of the RCA2 group was always the lowest, and it was significantly lower than that of other groups in the first and last phases of the experiment (p < 0.05). The average DO ranking was as follows: RCA1 (6.254) > RC (6.250) >RCA3 (5.77) > RCA2 (5.04).
The TN content varied between approximately 0.708 and 1.471 mg/L during the experiment. All groups had similar TN changing trends. The TN of the RC group was higher at the end of the experiment than at the beginning, whereas the TN of the RCA groups was lower at the end than at the beginning. Compared with the background TN level, the average TN increments (ΔTN) of each group ranked as RCA2 (0.27) > RCA 1 (0.19) > RCA3 (0.04) > RC (−0.09). During the culture experiment, the content of TN in the RCA groups was ranked as RCA2 > RCA1 > RCA3. At the end of the experiment, TN in the RC group was the highest among all groups, but there were no significant differences among the groups (p > 0.05).
TP did not show a clear variation pattern in all groups, and there were no significant differences in each group both at the beginning and through to the end of the experiment. Compared with the background TP value, the average TP reductions in each group ranked as RCA2 (41.7%) > RCA1 (41.5%) > RCA3 (40.6%) > RC (39.7%).
The NO2−-N curves of each group did not show a similar change pattern before September 24, but thereafter they began to decline significantly. The NO2−-N contents of all groups were significantly lower at the end than at the beginning (p < 0.05) of the experiment. NO2−-N in the RC group was low at the beginning of the study but highest at the end. On the contrary, NO2−-N in the RCA3 group was the highest in each group at the beginning but lowest at the end (p < 0.05). Compared with the background NO2−-N value, the average NO2−-N increments (ΔNO2−-N) ranked as RCA3 (91.6%) > RCA1 (90.9%) > RCA (80%) > RC (50%).
The NO3−-N content of RC and RCA1 initially decreased, then increased sharply to the peak on 11 October, and then declined again. The NO3−-N content of RCA1 and RCA2 initially increased, reached the peak on 11 October, and thereafter began to decline. The NO3−-N content was significantly higher for the RCA1 group compared to other groups at the beginning (p < 0.05). On 24 September, the NO3−-N content of the RC group was significantly higher than that in the RCA groups (p < 0.05), then the NO3−-N contents of all groups increased sharply and decreased after 11 October. The NO3−-N contents in all groups were significantly higher at the end than at the beginning (p < 0.05), but there was no significant difference among groups (p > 0.05). Compared with the background NO3−-N value, the average NO3−-N increments (ΔNO3−-N) ranked as RCA3 (0.110) > RC (0.108) > RCA2 (0.091) > RCA1 (0.051).
Similar to TP, the change of Chl-a in each group also showed no similar trend. At the beginning of the experiment, the Chl-a content of the RCA2 group was significantly higher than that of the RC group (p < 0.05), but, at the end of the experiment, the Chl-a content of the RC group was significantly higher than that of other groups (p < 0.05). Compared with the background Chl-a value, the average Chl-a variety ranked as RCA3 (73.9%) > RCA2 (−45.2%) > RCA1 (−52.9%) > RC (−83.6%).
NH4+-N varied between approximately 0.316 and 0.801 mg/L during the experiment. The NH4+-N content increased slowly at the beginning (with the exception of RC), reached a peak on 24 September, and then decreased. The NH4+-N content was significantly lower at the end of the experiment than at the beginning except for RCA2 (p < 0.05). The NH4+-N content in the RC group was the highest at the beginning of the experiment, but the lowest at the end. On the contrary, the NH4+-N content in the RCA2 group was the lowest at the beginning of the experiment, but the highest at the end. Compared with the background NH4+-N value, the average NH4+-N deductions ranked as RC (52.2%) > RCA3 (31.4%) > RCA1 (27.5%) > RCA2 (2%).

3.2. Bacterial Community Structures

A total of 2,850,354 effective sequences were identified from the water samples. After extraction, 4104 operational taxonomic units (OTUs) were identified according to a 97% similarity level. Based on the OTUs, the bacteria species detected belonged to 46 phyla, 139 classes, 328 orders, 540 families, 1006 genera, and 1807 species. As shown in Table 2, there were 35 phyla, 92 classes, 218 orders, 361 families, 623 genera, and 983 species in group RC, which was the lowest among all groups. The bacterial species of the RCA1 group belonged to 45 phyla, 132 classes, 303 orders, 502 families, 915 genera, and 1595 species, which was the highest among all groups. The Simpson diversity indices showed the trend of RC > RCA3 > RCA2 > RCA1. The ACE and Chao indices of RCA1 were the highest and significantly higher than those of other groups (p < 0.05), and the ACE and Chao indices of RC were the lowest and significantly lower than those of other groups (p < 0.05), indicating that RCA1 had the highest community richness. The Pielou index showed the trend RCA2 ≈ RC > RCA1 > RCA3 (p > 0.05), indicating that the RCA2 group had the highest evenness of bacterial community (Table 2).
As shown in Figure 2A, the number of bacterial species (OTUs, genus level) in the RC group showed a downward trend, while that in the RCA groups showed an upward trend. OTUs number in the RCA3 group was the lowest at the beginning and then rose to the second place at the end of culture. On the contrary, the number of OTUs in the RC group was the highest at the beginning but declined to the lowest at the end. The OTUs number in the RC group was significantly lower than that in RCA1 and RCA2 (p < 0.05), extremely significantly lower than that in RCA3 (p < 0.01), and the OTUs number in the RCA2 group was significantly lower than that in the RCA3 group (p < 0.05). There was no significant difference among other groups (p > 0.05). The average OTU number in each group ranked as RCA3 (431) > RCA1 (421) > RCA2 (395) > RC (346).
The variation patterns of the Shannon diversity index of the four groups in five samplings are shown in Figure 2B, which shows an upward trend overall, indicating that the bacterial community diversity increased temporally within the culturing course. Among them, the Shannon diversity index of the RC1 group was the lowest at the beginning but rose to the highest at the end. The Shannon diversity of the RC group had the largest amplitude within different periods, which reflected that the bacterial community in rice–crayfish coculture system was very unstable compared with that in the RCA groups. The average Shannon diversity indices ranked as RCA2 (3.61) > RCA3 (3.59) > RCA1 (3.57) > RC (3.51), which suggested that the bacterial diversity in the RC group was lower than that in the RCA groups (Figure 2B).
As shown in Figure 3A, at the phylum level, the bacterial community structures of different groups were similar. There were eight bacteriaphyla with abundances of more than 1%, including Proteobacteria, Actinobacteriota, Bacteroidota, Cyanobacteria, Verrucomicrobiota, Chloroflexi, Planctomycetota, and Armatimonadota. Proteobacteria, Actinobacteriota, Bacteroidota, and Cyanobacteria were the dominant phyla in all groups, which accounted for more than 90% in total. The average proportions of Proteobacteria were the highest, ranking as RC (35.8%) > RCA3 (33.7%) > RCA2 (32.7%) > RCA1 (31.6%), and the proportion of Proteobacteria initially increased and then decreased during the culture (Figure 4). The average proportions of Actinobacteria ranked as RC (27.9%) > RCA3 (26.0%) > RCA2 (23.3%) > RCA1 (19.9%). The average proportions of Bacteroidetes were ranked as RCA3 (19.0%) > RCA2 (18.7%) > RCA1 (16.7%) > RC (14.4%). The average proportions of Cyanobacteria were ranked as RCA1 (17.6%) > RCA3 (17.5%) > RCA2 (17.4%) > RC (14.9%). The proportion of Bacteroidota and Verrucomicrobiota showed an increasing trend, while the proportion of Cyanobacteria and Chloroflexi showed a decreasing trend over time. Variation analysis between groups showed that there was no significant difference in the relative abundance of the dominant bacteria (p > 0.05). Others denoted phylum with relative abundance less than 1%. At the genus level (Figure 3B), there were 32 bacteria genera with an abundance of more than 1%. There were seven genera with an abundance of more than 2%, including HGCl-Clade, Cyanobacteria, Polykaryotes, Pseudarcicella, Fluviicola, Cl500-29_marine_group, and Jerseys. Variation analysis between groups showed that there were no significant differences among genera except for in Cl500-29_marine_group. Others represented bacteria genera with a relative abundance of less than 1%.
The hierarchical clustering analysis results of each sample are described in Figure 5. All samples were divided into two clusters in the first branch. Cluster 1 included samples of 11 and 25 October, which were the last two samples. Cluster 2 included samples of 22 August and 6 and 24 September, which were the first three samples. As shown in Figure 5, the proportion of Proteobacteria initially decreased and then increased during the experiment. The proportion of Bacteroidota and Verrucomicrobiota showed an uptrend overall. Cyanobacteria, Chloroflexi, and Planctomycetota showed an overall declining trend. There were no significant changes in other phyla.

3.3. Correlation Analysis of Water Quality and Bacterial Community

Spearman correlation analyses were performed between eight water quality indices, the top eight bacterial phyla (Figure 6A), and the top 30 bacterial genera (Figure 6B). The horizontal axis showed the clustering result of the water quality index and the vertical axis represented the clustering results of the bacterial community. Both at the phyla and genus level, water quality indices were classified into two main clusters, the same as in the bacteria. At the phyla level, the first branch of the horizontal axis included NO3−-N, DO, and CODMn, and the second branch included NO2−-N, TN, NH4+-N, TP, and Chl-a. The first branch of the vertical axis included Actinobacteriota, Bacteroidota, Verrucomicrobiota, and Armatimonadota. They were positively correlated with NO3−-N and DO, and negatively correlated with NO2−-N, TN, and NH4+-N. The second branch included Proteobacteria, Cyanobacteria, Chloroflexi, and Planctomycetota. These were positively correlated with NO2−-N, TN, and NH4+-N, while negatively correlated with NO3−-N and DO. At the genus level, the first branch of the horizontal axis included DO and NO3−-N, and the second branch included NO2−-N, TN, NH4+-N, CODMn, TP, and Chl-a. The first branch of the vertical axis included Rubrivivax, hgcI_clade, CL500-29_marine_group, Sphaerotilus, and Cyanobium_PCC-6307, which were positively correlated with NO2−-N, TN, NH4+-N, CODMn, TP, and Chl-a, while negatively correlated with NO3−-N and DO. The second branch mainly included Polynucleobacte, Candidatus, Methylocystis, Armatimonas, Dinghuibacter, Algoriphagus, Rhodobacter, Flavobacterium, Pseudarcicella, Fluviicola, Sediminibacterium, Limnohabitans, and Rhodoluna, which were positively correlated with DO, NO3−N, and CODMn, and negatively correlated with NO2−-N,TN, NH4+-N, and TP.

4. Discussion

4.1. Effects of Bighead Carp of Different Densities Cocultured with Rice–Crayfish on Water Environmental Factors

DO is an important environmental factor affecting fish and bacterial communities. DO could affect the physiological activities of aquatic animals, such as metabolism, growth, and breeding. Low DO led to the production of intermediate products, such as ammonia, nitrite, and hydrogen sulfide, which were harmful to aquatic animals [16,17]. In this study, in the rice–crayfish–bighead carp coculture systems, especially in RCA2, DO decreased significantly relative to the rice–crayfish coculture system. This may be related to the increase in respiration and biological oxygen consumption after adding bighead carp, and the activity of bighead carp increasing the disturbance of the sediment. The nutrients released because of this action promoted the massive reproduction of plankton and further increased the biological oxygen consumption [18,19]. After the experiment concluded, spot checks were conducted on the survival rates of bighead carp in different experimental groups, and the catch of bighead carp in the RCA3 group was less than that in the RC2 group. It is speculated that, throughout the entire breeding period, compared to RAC3, RCA2 may had a higher survival rate and biological standing stock and the disturbance of fish in RCA2 may have further accelerated the release of organic matter from sediments and thus increased oxygen consumption causing low levels of dissolved oxygen. Meanwhile, since the crayfish had already burrowed and hibernated after the breeding was completed, there was no accurate statistical data available.
NO2−-N is an intermediate product of nitrogen cycling, which can be converted into NO3−-N by nitrification when DO is sufficient and converted into N2 by denitrification when DO is low [20]. The contents of NO2−-N and NH4+-N are two main limiting factors that affect the growth and physiological function of crayfish and crabs [21,22]. When the NO2−-N content is at a high level, it will combine with ferrohemoglobin blood to form methemoglobin, which will inhibit the oxygen-carrying capacity of the blood. Furthermore, it will lead to the death of fish due to hypoxia [23]. Toxicological experiments of NH4+-N conducted by researchers on fish showed that a low content of NH4+-N in water can play a greater role in preventing diseases [24]. In this study, the groups with bighead carp had lower DO contents than the rice–crayfish coculture group, and low DO content stimulated denitrification which caused lower levels of NO2−-N and promoted the generation of nitrogenous gases to reduce the nitrogen in the rice–crayfish–bighead carp groups to lower than that of the other group. In other words, bighead carp stocking in the rice–crayfish coculture system was of benefit in reducing the accumulated uneaten food, feces, and metabolic waste.
TN, TP, and Chl-a are eutrophication indicators of water which can reflect the pollution degree [25,26]. Previous studies have shown that the introduction of silver carp and bighead carp can reduce the levels of total nitrogen (TN) and total phosphorus (TP) in the water, as well as the biomass of algae and Chl-a, thereby improving the water quality [11,27]. These experimental results showed that the system with bighead carp had lower levels of TN, TP, and Chl-a in the water. It showed that bighead carp could promote the migration and transformation of nitrogen and phosphorus in water, improve the nutritional status of water, and enhance the material circulation efficiency of the system. At the same time, the filter-feeding behavior of bighead carp and the reduced levels of nitrogen and phosphorus had further led to a decrease in the level of Chl-a, indicating a lower risk of cyanobacterial blooms in the water body [12,28]. In the present study, when 1000 bighead carp were put in every 200 m2 (37.5 g/m2), the survival rate of bighead carp was the highest, and the removal effect of nitrogen and phosphorus was the best, but, at the same time, the DO content of the water decreased, while the NH4+-N content increased (p < 0.05). Therefore, we suggested that, during the feeding period, measures such as reasonably turning on the aerator, bottom aerating, and adding new water should be undertaken, to adjust the water quality over time.

4.2. Effects of Bighead Carp Coculture with Different Densities on Bacterial Community in Water

The bacterial community is an important part of the aquatic ecosystem, which plays a vital role in the recycling and utilization of carbon, nitrogen, phosphorus, and sulfur in water [7,29]. In recent years, Illumina Miseq sequencing technology has the advantages of high throughput, good data integrity, economic efficiency, and rapidity, and has been widely used to study the bacterial community [30]. Zhao et al. [6] made an Illumina Miseq analysis of the microorganisms in the water and sediment of a rice–fish (Odontobutis potamophila) system. The results showed that there were significant differences in the bacterial community structure in water and sediment between the rice–fish system and the paddy fields, which indicated that the cultured fish may play an important role in the regulation of the bacterial community structure. In this study, there was no significant difference in the dominant bacterial populations among each group, with Proteobacteria, Actinomycetes, Bacteroides, and Cyanophyta as dominant phyla. However, the relative abundance of Proteobacteria and Actinomycetes in water with bighead carp was reduced, while that of Bacteroidea and Cyanobacteria was increased. Proteobacteria is the largest phylum of bacteria, and it is the dominant phylum in many ecosystems, with highly complex functions, most of which are related to the decomposition and circulation of organic matter [31]. In this study, it mainly included γ-Proteus and α-Proteus. Actinomycetes are widely distributed in nature and exist in various ecosystems such as on land [32] and in freshwater [33] and oceans [34]. It is an important type of microorganism that mediates the decay of litter or sediment and the formation of soil organic matter and plays an important role in natural ecosystems [35]. Kaisa et al. [36] reported that Actinomycetes could thrive easily in environments with high organic concentrations. In this study, the relative abundance of Actinomycetes in the RC group was higher than that in the other groups, which indicated that the trophic extent in the rice–crayfish coculture system was higher than that in the rice–crayfish–bighead carp coculture system, which was consistent with the results of the water quality index. In the groups with bighead carp, with the increase in stocking density, the relative abundance of Actinomycetes increased. It was probably because the activities of bighead carp in the high-density group increased the disturbance to the sediment and promoted the release of flora in the sediment. Gerber et al. [37] found that Actinomycetes can produce odorous substances, leading to an earthy smell of aquatic animals, which directly affected the sales of aquatic products and the benefits of farmers. This experiment showed that bighead carp stocking was good for improving water circulation, reducing the release of peculiar smell substances in water, improving the quality of aquatic products, and enhancing the overall benefits. Bacteroides is a gram-negative bacterium, which often exists in the intestinal tract of animals and plays an important role in the metabolism and transformation of polysaccharides in feed. Cyanobacteria are mostly aerobic photosynthetic bacteria, which are widely distributed in freshwater, seawater, and soil. They are an important part of the primary productivity of water bodies [10]. Studies have shown that many cyanobacteria have the ability to fix nitrogen, which can improve the fertility of soil through nitrogen fixation [38,39]. In this study, the abundance of Cyanobacteria in the RC group was higher than that in groups with bighead carp. It may be related to the grazing effect of bighead carp on zooplankton, which reduced the predation pressure of zooplankton on Cyanobacteria, and the relative abundance of Cyanobacteria in the RCA3 group was the lowest. It may be related to the filter-feeding of bighead carp on Cyanobacteria, which also proves that bighead carp have the ecological effect of controlling the eutrophication of water bodies. On the whole, bighead carp stocking not only reduces the release of odor substances in the water body and improves the quality of aquatic products but also promotes the conversion efficiency of N in the water body and reduces the risk of cyanobacteria outbreak, which has significant economic and ecological significance.
Bacterial diversity has an important response to the biochemical reactions in its ecosystem, so the study of bacterial diversity in the environment can be used to monitor environmental changes [40,41]. In this study, with the development of aquaculture, the water quality factors had changed obviously, and the bacterial community had also changed accordingly. As shown in the Spearman Heatmap, the amount of Proteobacteria was positively correlated with NH4+-N, Planctomycetota and Chloroflexi were negatively correlated with NO3−-N, and Bacteroidota and Verrucomicrobiota were positively correlated with NO3−-N. In the later stage of the experiment, NO3−-N and DO increased, NH4+-N decreased first and then slowly increased, while TN and NO2−-N decreased. Therefore, it may be inferred that the relative abundance of Proteobacteria decreased first and then increased, the relative abundance of Planctomycetota and Chloroflexi should gradually decrease, and the relative abundance of Bacteroidota and Verrucomicrobiota should gradually increase. The results were consistent with the sample community analysis chart (Figure 5).

5. Conclusions

Compared with the rice–crayfish coculture system, DO, TN, NO2−N, and Chl-a in the rice–crayfish–bighead coculture system reduced, NH4+-N in the water increased, and the transformation of N in the water was promoted. Different density experiments showed that, when the density of bighead carp was 37.5 g/m2, the conversion effect of N was the best. Additionally, bighead carp stocking increased OTUs, community diversity, and the evenness of the system, which increased the stability of the system. As for bacterial composition, in the bighead carp stocking groups, the abundance of Actinomycetes and Proteobacteria in the water was reduced, and the release of odorous substances in water was also reduced, which was helpful to improve the quality of aquatic products. At the same time, the abundance of Bacteroides and Cyanobacteria in the water body increased, and the nitrogen fixation ability of the water increased, which was beneficial to the growth of rice, with remarkable economic and ecological benefits. It was necessary to further analyze the bacterial community structure in the intestinal tracts of aquatic animals in the symbiotic system, and, on this basis, to evaluate the quality and biomass of aquatic products with different density gradients of bighead carp, and comprehensively evaluate the ecological effect and application value of bighead carp.

Author Contributions

Conceptualization, Y.Z. and L.Z.; data curation, J.D. and Y.T.; formal analysis, Y.Z. and J.D.; funding acquisition, Y.Z. and J.L.; writing—original draft, Y.Z., L.Z., and Y.T.; writing—review and editing, Y.Z. and Y.T. All authors have read and agreed to the published version of the manuscript.

Funding

This work was funded by the Henan Provincial Department of Science and Technology Research Project (202102110382), China Agriculture Research System of Specialty Freshwater Fish (CARS-46), and The Special Fund for Henan Agriculture Research System (HARS-22-16).

Data Availability Statement

Data are contained within the article.

Conflicts of Interest

The authors declare no conflicts of interest.

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Water 16 02296 g001
Figure 1. Water quality indices curves of four groups over five times.
Figure 1. Water quality indices curves of four groups over five times.
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Figure 2. Analysis of species richness and diversity of four groups in five sampling times. (A) OTUs; (B) Shannon diversity.
Figure 2. Analysis of species richness and diversity of four groups in five sampling times. (A) OTUs; (B) Shannon diversity.
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Figure 3. Bacterial composition and abundance in the water samples shown in Circos maps ((A): phyla; (B): genera level).
Figure 3. Bacterial composition and abundance in the water samples shown in Circos maps ((A): phyla; (B): genera level).
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Figure 4. The bacterial phyla of four groups at five times.
Figure 4. The bacterial phyla of four groups at five times.
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Figure 5. Hierarchical clustering tree on phylum level. Note: The length between branches represented the distance between samples, and different groups were presented in different colors.
Figure 5. Hierarchical clustering tree on phylum level. Note: The length between branches represented the distance between samples, and different groups were presented in different colors.
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Figure 6. Spearman heatmap analysis of water quality index and bacterial community ((A): phyla; (B): genus). Note: red indicates positive correlation and blue indicates negative correlation. * p < 0.05, ** p < 0.01, and *** p < 0.001.
Figure 6. Spearman heatmap analysis of water quality index and bacterial community ((A): phyla; (B): genus). Note: red indicates positive correlation and blue indicates negative correlation. * p < 0.05, ** p < 0.01, and *** p < 0.001.
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Table 1. Grouping and stocking data of each experiment group.
Table 1. Grouping and stocking data of each experiment group.
GroupsAreaProcambarus clarkiiAristichthys nobilis
Number DensityWeight DensityNumber DensityWeight Density
m2ind./m2g/m2ind./m2g/m2
RC (Rice–Crayfish)200512.53 ± 0.03--
RCA1 (Rice–Crayfish–Aristichthys nobilis 1)200512.54 ± 0.022.518.75 ± 0.89
RCA2 (Rice–Crayfish–Aristichthys nobilis 2)200512.52 ± 0.04537.50 ± 1.11
RCA3 (Rice–Crayfish–Aristichthys nobilis 3)200512.56 ± 0.027.556.25 ± 2.32
Table 2. Alpha diversity and species information in each group.
Table 2. Alpha diversity and species information in each group.
GroupsSimpson
Index		Ace
Index		Chao
Index		Pielou
Index		PhylaClassOrderFamilyGenusSpecies
RC0.075 ± 0.008546.25 ± 66.01491.21 ± 56.110.66 ± 0.033592218361623983
RCA10.065 ± 0.004633.16 ± 79.45579.83 ± 92.430.65 ± 0.02451323035029151595
RCA20.069 ± 0.005597.56 ± 77.91537.97 ± 71.660.66 ± 0.04441282944888771522
RCA30.074 ± 0.007585.47 ± 72.24566.44 ± 95.670.64 ± 0.02431303024969121594
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Zhang, Y.; Zhao, L.; Duan, J.; Tang, Y.; Lv, J. Effects of Stocking Density of Filter-Feeding Fishes on Water Quality and Bacterial Community in Rice–Crayfish Polyculture System. Water 2024, 16, 2296. https://doi.org/10.3390/w16162296

AMA Style

Zhang Y, Zhao L, Duan J, Tang Y, Lv J. Effects of Stocking Density of Filter-Feeding Fishes on Water Quality and Bacterial Community in Rice–Crayfish Polyculture System. Water. 2024; 16(16):2296. https://doi.org/10.3390/w16162296

Chicago/Turabian Style

Zhang, Yuanyuan, Liangjie Zhao, Jiaoyang Duan, Yongtao Tang, and Jun Lv. 2024. "Effects of Stocking Density of Filter-Feeding Fishes on Water Quality and Bacterial Community in Rice–Crayfish Polyculture System" Water 16, no. 16: 2296. https://doi.org/10.3390/w16162296

APA Style

Zhang, Y., Zhao, L., Duan, J., Tang, Y., & Lv, J. (2024). Effects of Stocking Density of Filter-Feeding Fishes on Water Quality and Bacterial Community in Rice–Crayfish Polyculture System. Water, 16(16), 2296. https://doi.org/10.3390/w16162296

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