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Article

Research on the Construction of an Integrated Multi-Trophic Aquaculture (IMTA) Model in Seawater Ponds and Its Impact on the Aquatic Environment

by
Han Yang
1,
Baogui Tang
1,2,*,
Hui Zhou
1,
Peigui Zhong
3 and
Liqiang Zhao
1
1
College of Fisheries, Guangdong Ocean University, Zhanjiang 524088, China
2
Guangdong Provincial Marine Fish Technology Innovation Center, Zhanjiang 524025, China
3
Zhanjiang Haisite Aquatic Technology Co., Ltd., Zhanjiang 524088, China
*
Author to whom correspondence should be addressed.
Water 2025, 17(6), 887; https://doi.org/10.3390/w17060887
Submission received: 21 December 2024 / Revised: 26 January 2025 / Accepted: 13 March 2025 / Published: 19 March 2025
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Abstract

:
The Integrated Multi-Trophic Aquaculture (IMTA) model is an eco-friendly aquaculture system that enhances water purification through ecological niche utilization. A study employing 16S rRNA sequencing analyzed microbial communities in aquaculture water at initial, middle, and final stages. Results indicated that physicochemical parameters were lower at the final stage. The removal efficiencies of Total Nitrogen (TN) and Total Phosphorus (TP) reached 79.10% and 63.64%, respectively. The Simpson and Shannon indices revealed that microbial diversity was significantly higher in the final stage compared to the initial and middle stages (p < 0.05). Dominant bacterial phyla included Actinobacteria, Proteobacteria, and Bacteroidetes, while dominant genera included Candidatus_Aquiluna, NS3a_marine_group, and NS5_marine_group. Functional prediction results demonstrated that metabolic pathways such as amino acid metabolism, biosynthesis of other amino acids, and energy metabolism were upregulated in the final stage compared to the initial stage. Correlation analysis of environmental factors suggested that TN and TP significantly influenced the microbial community structure. Key microorganisms such as Candidatus_Aquiluna, Marinomonas, and Cobetia played crucial roles in carbon fixation, nitrogen reduction, and phosphorus removal. In summary, the IMTA model effectively purifies water, with microbial communities contributing to the stability of the aquatic environment.

1. Introduction

China is the world’s largest marine aquaculture country. Due to the decline in marine fishery resources resulting from overfishing, environmental pollution, and other factors, the focus of the China’s aquaculture industry has shifted towards farming, leading to a steady increase in the output value of the marine aquaculture industry [1]. In 2022, China’s marine aquaculture output value increased to RMB 463.884 billion, marking a 7.8% increase compared to the previous year. The current status of marine pond aquaculture in China is characterized by continuous growth in production and technological advancements. However, limited aquaculture space has resulted in high stocking densities, frequent diseases, and concerns regarding the quality of aquaculture products and the environment. Guangdong, a major coastal aquaculture province with extensive marine pond aquaculture areas, actively promotes green and ecological fishery farming to address these issues [2]. In situ bioremediation of marine aquaculture effluent is a process that reduces the concentration or total amount of pollutants in the effluent through biological processes such as decomposition, transformation, and enrichment, all without relocating the effluent. This effectively achieves the goal of green farming [3].
Integrated multi-tropic aquaculture (IMTA) is an advanced, eco-friendly, and healthy aquaculture model aimed at enhancing the yield and quality of aquaculture products while minimizing environmental impact [4,5]. The fish–shrimp–shellfish–algae IMTA model is a circular production system that rationally farms different species such as fish, shrimp, shellfish, and algae in the same water area to create a symbiotic relationship, thereby helping to reduce environmental impact [6]. In IMTA systems, unconsumed feed and nutrients that would otherwise be lost can be recaptured by other organisms and converted into valuable nutrients for the production of harvestable seafood and crops [7]. Fish excrete nitrogen and phosphorus waste, and their dissolved components can be used as a nutrient source for the growth of farmed algae species, thereby reducing nutrient levels in the water and preventing eutrophication [8]. Additionally, filter-feeding organisms, such as mussels and oysters, effectively remove excess nutrients by effectively filtering particles from the water [9,10]. The success of IMTA systems hinges not only on selecting appropriate species but also on ensuring that these species effectively utilize the waste produced by the fed species. Within IMTA systems, bioremediation capabilities play a crucial role, leveraging the natural metabolic activities of various organisms to repair and enhance water quality [11,12,13]. This approach fosters the development of a circular economy in aquaculture, transforming waste from one component into a valuable resource for another [14].
In the early 1980s, Rakocy from the University of the Virgin Islands developed the first deep-water aquaponics system (the University of the Virgin Islands System, referred to as the UVI model) [15]; during the same period, Dr. Mark Mcmurtry from North Carolina State University developed the aquaponics system (the North Carolina State University System, referred to as the NCSU model) [16]. With the research results achieved by the UVI system, aquaponics systems entered a global expansion phase [17], and scholars worldwide began to attempt to design and improve aquaponics systems. Ding Yongliang [18] and others from the Fishery Machinery and Instrument Research Institute of the Chinese Academy of Fishery Sciences introduced the aquaponics system to China in the 1990s and conducted experimental research, designing the country’s first experimental aquaponics device. Studies have shown that aquatic plants remove nitrogen and phosphorus from water mainly through adsorption, filtration, settlement, assimilatory absorption, and microorganisms [19,20]. On one hand, aquatic plants require substantial amounts of nitrogen, phosphorus, and other nutrients during their growth, making them effective for water purification. On the other hand, plant roots can release oxygen, creating aerobic, anoxic, and anaerobic zones around them, which facilitates ammonia nitrogen nitrification and nitrate nitrogen denitrification, thereby enhancing the removal and transformation of nitrogen [21,22].
Mixed farming of fish, shrimp, and shellfish in ponds effectively achieves comprehensive utilization of the water body’s upper, middle, and lower levels. By utilizing shellfish as the primary species, the pond water is efficiently purified through their filter-feeding behavior. By incorporating the water purification capabilities of plant roots, an IMTA (Integrated Multi-Trophic Aquaculture) model incorporating fish, shrimp, shellfish, and plants is established. This model leverages their complementary ecological niches to ensure stable water quality, recycle nutrients, prevent ecological diseases, enhance product quality and safety, boost farming efficiency, and minimize farming waste emissions. In this study, we investigated the variation process of aquaculture water quality within the IMTA system, laying the groundwork for in situ treatment of aquaculture effluent in marine pond aquaculture.

2. Materials and Methods

2.1. Model Establishment

Select ponds without the administration of microecological agents. The ponds have an approximate area of 1200 square meters, with a water depth ranging from 1 to 2 m, and the water source originates from coastal areas. The aquaculture model primarily relies on IMTA to establish a farming system featuring “Scatophagus argus–Penaeus vannamei Boone–Perna viridis–Mesembryanthemum crystallinum”. The ponds were stocked with Scatophagus argus, Penaeus vannamei Boone, Perna viridis, and Mesembryanthemum crystallinum after staged cultivation. The Perna viridis are one years old, with a soaking duration of 2 h. They were sourced from the Leizhou Bay area of Donghai Island, Zhanjiang, and totaled 81 baskets, weighing approximately 61 kg. The Scatophagus argus weigh 211.21 ± 15.84 g and measure 10.8 ± 3.1 cm in length, with 200,000 specimens stocked, totaling approximately 1000 kg. The Penaeus vannamei Boone specimens weigh 4.11 ± 1.28 g and measure 5.91 ± 1.1 cm in length, with approximately 5000 specimens stocked, totaling over 10 kg. There are 300 Mesembryanthemum crystallinum in total. Each plant initially has approximately 4 to 5 true leaves, with a root length of 5.2 ± 2.1 cm and a plant height of 5.4 ± 1.8 cm. The plants are spaced 15 cm apart, and 9 plants are arranged in a 3 × 3 grid on each 50 cm × 50 cm floating bed. The floating beds are secured with ropes and placed along the shore, covering a total area of 10 square meters.
In this mode, fish, shrimp, shellfish, and plants are raised, mainly by feeding feed into the pond twice a day, respectively, at 8 a.m. and 3 p.m. The feed is a marine fish compound feed produced by Zhengda Group, which contains high-quality imported fish meal, yeast powder, peeled soybean meal, lecithin, refined fish oil, etc. The main nutritional components of the feed are shown in Table 1.

2.2. Sample Collection

Water samples (1 L) were collected from each group every 7 days using a glass water sampler. Each collection included three replicates, which were transported to the laboratory under low-temperature conditions. The water samples were filtered through 0.45 μm cellulose acetate membranes to measure physicochemical indicators of water quality. The membranes were used for total bacterial DNA extraction.

2.3. Measurement of Environmental Factors

Salinity (S), temperature (T), pH, and dissolved oxygen (DO) were measured in situ. Physicochemical parameters such as total nitrogen (TN), total phosphorus (TP), ammonia nitrogen (NH4+-N), nitrate (NO3−-N), nitrite (NO2−-N), and phosphate (PO43−-P) were measured according to the Specifications for Ocean Monitoring (GB17378-2007) [23].

2.4. DNA Extraction

The HiPure Soil DNA Kit was used to extract total DNA from water samples collected from the IMTA ponds. The concentration, purity, and integrity of the extracted DNA were assessed using a NanoDrop 2000 spectrophotometer (Thermo Fisher Technology Co., Ltd.) (Shanghai, China) and 1% agarose gel electrophoresis.

2.5. PCR Amplification

The primers used for amplifying the V3-V4 region of the bacterial 16S rRNA gene for high-throughput sequencing were 341F (5′-CCTACGGGNGGCWGCAG-3′) and 806R (5′-GGACTACHVGGGTATCTAAT-3′). Sequencing was performed using the Illumina MiSeq sequencing platform (Guangzhou GeneDenovo Technology Co., Ltd.) (Guangzhou, China).

2.6. Data Analysis

OTUs (operational taxonomic units) were clustered using Usearch software (Version 9.2.64) with a 97% similarity threshold. Species classification was performed using RDP [24] classifier software with a threshold of 0.8; classifications below this threshold were categorized as Unclassified. Diversity analysis was conducted using QIIME (version 1.14) [25] and Muscle (version 3.8.31) [26] software. Based on the species classification results, abundance values at various taxonomic ranks were obtained. The Vegan package in R language (version 2.5.3) [27] was used to compare abundance differences between samples or groups, with a screening criterion of p < 0.05. Tax4Fun (version 1.0) [28] was employed to analyze the KEGG metabolic pathways of bacterial or archaeal species in the samples. Performing Redundancy Analysis (RDA) and Canonical Correspondence Analysis (CCA) using the Vegan package (version 2.5.3) in R language [27].

3. Results

3.1. Environmental Factor Detection

According to Figure 1, both NH4-N and NO3-N indicators exhibit a clear trend of initially decreasing, then increasing, and finally decreasing again, reaching their peak points on Day 28 and Day 35, respectively. The NO2-N and P-PO4 indicators show no significant upward or downward trends, tending to remain stable, with P-PO4 peaking on Day 21. All indicators reach their lowest points at the end of the period on Day 56. According to Table 2, under the IMTA model, there is a significant decrease in the initial and final values of various physicochemical water quality indicators in the water body.

3.2. High-Throughput Sequencing

From a total of 27 samples, approximately 1,421,510 raw sequences of the 16S rRNA gene were generated. After removing low-quality sequences, approximately 19,460 sequences were utilized for subsequent microbial diversity analysis. A total of 1527 operational taxonomic units (OTUs) were identified from the 27 samples. Venn diagram analysis was conducted to investigate the microbial communities in the water bodies under the Integrated Multi-Trophic Aquaculture (IMTA) system across different cycles (Figure 2).

3.3. Diversity Index

3.3.1. α-Diversity

The Simpson and Shannon indices are commonly used to reflect community diversity. A higher Simpson index value indicates lower community diversity, whereas a higher Shannon index value indicates higher community diversity. The Ace index and Chao index, which reflect the richness of bacterial communities, increase as their numerical values rise.
According to Table 3, there are significant differences in the Simpson and Shannon indices within the cycles (p < 0.05), with the indices at the final stage being significantly higher than those at the initial and middle stages, indicating that community diversity increases over time during the culture period, reaching its peak at the final stage. Additionally, Table 3 shows that there are no significant differences in the Ace and Chao indices among different stage (p > 0.05).

3.3.2. β-Diversity

As shown in Figure 3, the initial, middle, and late stages are distant from each other, with tight clustering among groups, indicating good sample data and significant differences in community structure among different culturing periods under the Integrated IMTA mode. The stress coefficient (Stress) based on this algorithm is 0.043, indicating a good fit of the results and their ability to accurately reflect the composition of the microbial community.
Based on the above analysis, it is concluded that there are significant differences among the three periods of the IMTA water microbial community. To further identify the species with significant differences between groups, LEfSe analysis was used to identify characteristic communities with significant abundance differences, with the results shown in Figure 4. Based on the LEfSe LDA scores presented in Figure 4a, 39 microbial taxa with LDA values above 4.0 showed significant differences, among which 19 were enriched in the E group, 7 were enriched in the M group, and 14 were enriched in the L group. According to Figure 4b, the enrichment in Group E includes Vibrio, Ilumatobacteraceae, Verrucomicrobiota, etc.; the enrichment in Group M includes Marinomondaceae, Candidatus_Aquiluna, Firmicutes, etc.; and the enrichment in Group L includes Alphaproteobacteria, NS3a_marine_group, Flavobacteriaceae (from order to family), etc.

3.4. Dominant Microbial Flora

As shown in Figure 5a, the dominant phyla are Actinobacteria, Proteobacteria, Bacteroidetes, Firmicutes, and Verrucomicrobiota. The abundances of the other phyla are all below 1%. At the phylum level, only Verrucomicrobiota shows a significant increase over the cycle, while the differences in the abundances of the other phyla are not significant. Figure 5b indicates that the dominant genera are Candidatus_Aquiluna, Vibrio, NS3a_marine_group, NS5_marine_group, Marinomonas, Alteromonas, and Candidatus_Actinomarina. During the culture cycle, the abundances of Marinomonas and Alteromonas decreased significantly.

3.5. Differential Microbial Diversity

At the phylum level (Figure 6a), compared to the initial period, there is an upward trend in the diversity of Actinobacteria, Chloroflexi, Verrucomicrobiota, Bdellovibrionota, and Planctomycetota, while there is a downward trend in the diversity of Proteobacteria, Nanoarchaeota, and Bacteroidota. At the genus level (Figure 6b), compared to the initial period, there is a downward trend in the diversity of Candidatus_Aquiluna, Marinomonas, Aestuariicoccus, NS3a_marine_group, and NS5_marine_group, whereas there is an upward trend in the diversity of Flavobacterium, Rubritalea, and Acinetobacter.

3.6. Functional Prediction

The KEGG functions of the microbial community were predicted using Tax4Fun. Figure 7 shows that, compared to the initial stage (E), the metabolic pathways such as amino acid metabolism, biosynthesis of other amino acids, energy metabolism, metabolism of terpenoids and polyketides, and membrane transport are upregulated in the late stage (L). Conversely, compared to the initial stage (E), the metabolic pathways such as signal transduction, lipid metabolism, cofactor and vitamin metabolism, and cell growth and death are downregulated in the late stage (L).

3.7. Analysis with Environmental Factors

As shown in Figure 8a, under the conditions of aquaculture density and water depth, CCA1 indicates that the first constrained axis accounts for 97.14% of the explanation, indicating a high correlation. The arrow length for TN is slightly longer than that for TP, suggesting a stronger correlation between TN and the samples. The arrows for the initial and mid-stage samples align in the same direction as those for TN and TP, indicating a positive correlation between the environmental factors TN and TP and the changes in the sample communities. In contrast, the arrows for the late-stage samples are in the opposite direction to those for TN and TP, suggesting a negative correlation between the environmental factors TN and TP and the changes in the sample communities. Variance portioning analysis (VPA) was conducted at the genus level to investigate the various environmental factors in the Integrated Multi-Trophic Aquaculture (IMTA) system water bodies and their contributions to the total variation in species distribution. According to Figure 8b, TN and TP contribute 24.22% and 17.90%, respectively, to the microbial communities in the IMTA system water bodies.
Figure 8c displays the correlation analysis between environmental factors and microbial communities, showing that TN and TP are positively correlated with marine single-celled bacterial genera such as Marinomonas and Cobetia, while they are negatively correlated with Vibrio and Candidatus_Actinomarina.

4. Discussion

4.1. Role of IMTA in Water Purification

Within the cycle, the physicochemical indicators of water bodies at the late stage of the IMTA system were significantly lower than those at the initial and mid-stages, indicating that the IMTA system can effectively diminish these indicators of water bodies to a certain extent and contribute to water purification. It is speculated that shellfish farming plays a crucial role in the IMTA system, as shellfish can absorb a significant amount of impurities and nutrients through filter-feeding [29]. According to Chu et al. [30], Hyriopsis cumingii can remove up to 92.89% of total phosphorus (TP) from water bodies by continuously feeding on microalgae. Lindahl et al. [31] conducted a modeling study on mussel farming in Gullmar Fjord, Sweden, and found that mussel farming reduced nitrogen concentrations in the fjord’s water bodies by 20%. However, monoculture of shellfish can also have certain environmental impacts. DAME [32] studies have shown that although mussel farming can generally reduce the concentrations of TN and TP in water bodies, it can also lead to increased concentrations of ammonia nitrogen and total organic carbon (TOC). The integrated culture of shellfish and algae significantly improves this situation. It not only significantly reduces the concentrations of TN, ammonia nitrogen, and TOC in seawater but also enhances the carbon sequestration and enhancement function of marine areas, effectively mitigating the significant increase in ammonia nitrogen concentration caused by high-density shellfish farming and demonstrating great potential in reducing the concentration of organics in seawater [33]. Emergent plants exhibit significant effects in improving eutrophic water bodies. They can directly absorb nitrogen and phosphorus from water and substrate and assimilate them into structural components such as proteins required for their growth, fixing the assimilated substances within the plants [34]. Among the total nitrogen and phosphorus removed by emergent plants, only 2.0% to 5.0% is achieved through assimilative absorption, and their main role is to promote the denitrification process [35].
Regarding the physicochemical indicators of water bodies, they exhibited a trend of initial increase followed by a decrease. It is speculated that the rapid growth of mussels leads to the production of more metabolites, which accumulate and sink to the seabed, forming biological deposits. This results in increased organic sediment deposition, decreased oxygen levels in interstitial water, increased oxygen consumption, accelerated sulfur reduction, and enhanced ammonification. These processes may ultimately accelerate the regeneration of nutrients in aquaculture water bodies, thereby exacerbating water eutrophication.

4.2. Characteristics of Microbial Communities in Water Bodies Under the IMTA Model

Microorganisms play a significant role in nutrient cycling and biotransformation of exotic organisms in aquaculture ponds. The dominant phyla across all groups are Actinobacteria, Proteobacteria, and Bacteroidetes. In this study, Actinobacteria, as the dominant bacterial group in pond water bodies, exhibited an increasing trend over the cycle, with gradually increasing abundance. Consistent with previous research, Actinobacteria plays an important role in nutrition and energy flow within the microbial food web and has been identified as the dominant bacterial group in eutrophic shallow lakes. It not only effectively enhances water transparency but also exhibits excellent performance in the degradation of pollutants such as ammonia nitrogen and chemical oxygen demand (COD) [36]. Proteobacteria, as another dominant phylum, participates in water purification processes, degrading nitrate and organics in water bodies and playing a crucial role in the transformation of nitrogen in polluted water bodies [37]. It also plays a key role in sulfur oxidation. Bacteroidetes, the most dominant group in wastewater treatment systems, are not only the primary decomposers of algae-derived carbohydrates but also effectively decompose complex structural nutrients such as proteins and lipids. The presence of Bacteroidetes in water bodies positively promotes carbon and nutrient cycling [38]. Verrucomicrobia, typically found in uncontaminated sediments and absent with increasing pollution [39], showed increased abundance in the later stages of this study, indicating less pollution in the water bodies at the end of the cycle, consistent with the research findings.
At the genus level, the dominant bacterial genera include Candidatus_Aquiluna, NS3a_marine_group, and NS5_marine_group, among others. Many of these genera possess the ability to purify water and degrade pollutants. Additionally, Liu Pengfei’s research [40] indicates that a decrease in the abundance of NS3a_marine_group may increase the presence of pathogens or opportunistic pathogens in the intestine. Denitrifying bacteria such as Flavobacterium contribute to the degradation of excess organics and reduction of NO2−-N toxicity [40]. Flavobacterium belongs to the phylum Bacteroidetes and is a high-abundance group in seawater. It can hydrolyze organic substances such as carbon polymers and proteins, producing various extracellular enzymes to decompose or utilize complex carbon sources [41,42].

4.3. Functional Characteristics of Aquatic Microbial Communities

The functionality of microbial communities is crucial for maintaining the stability of the aquatic environment and the health of aquatic animals in aquaculture. Tax4Fun predictions for the Integrated Multi-Trophic Aquaculture (IMTA) system indicate that the top 20 major functional categories in terms of functional abundance include carbohydrate metabolism, amino acid metabolism, energy metabolism, and lipid metabolism. This suggests that carbon metabolism and nitrogen metabolism are the dominant processes in IMTA. Carbon metabolism plays a role in maintaining water quality stability, influencing water acidity and alkalinity, and affecting dissolved oxygen levels. Nitrogen metabolism, on the other hand, maintains aquatic ecological balance, influences water quality, and promotes ecological restoration. By regulating nitrogen metabolism, it is possible to reduce ammonia and nitrite levels in the water, thereby improving water quality conditions and providing a favorable living environment for aquatic animals. The upregulation of amino acid metabolism has multiple effects on water purification, including reducing ammonia-nitrogen concentrations, promoting the growth of probiotics, enhancing the immunity of aquatic organisms, stimulating algae growth, and improving water quality [43].

4.4. Influence of Environmental Factors in IMTA on Aquatic Microbial Communities

Studies have shown that, during the aquaculture process, only 36% of N and 33% of P are absorbed by the farmed animals on average [44]. The remaining nutrients are dispersed in various forms within the aquaculture environment, leading to changes in the physicochemical properties of the aquaculture water and subsequently altering the diversity of aquatic microbial communities [45]. In this study, there was a positive correlation between TN and TP with marine monocellular bacteria such as Marinomonas and Cobetia. It is speculated that marine monocellular bacteria, including Marinomonas and Cobetia, can adapt to the eutrophic environment of aquaculture waters. Sun Chengbo [46] isolated and screened Cobetia from water samples collected from shrimp biofloc aquaculture ponds. This bacterium exhibits strong removal capabilities for NH4+-N and NO2-N, possesses the functions of heterotrophic nitrification and aerobic denitrification, and can be used as a biological treatment for precise regulation of water quality in aquaculture. By relying on the safe and efficient degradation of harmful substances in water by microorganisms, it promotes green and healthy aquaculture. Candidatus_Aquiluna has been reported as an important bacterial genus in aquaculture, possessing the abilities of carbon fixation and phototrophic nutrition based on rhodopsin [47,48].

5. Conclusions

This paper investigated the structure, composition, and functionality of microbial communities in aquaculture water bodies under the Integrated Multi-Trophic Aquaculture (IMTA) model, utilizing physicochemical indicators and high-throughput sequencing technology. The findings revealed that the diversity and abundance of microbial communities in these water bodies increased progressively throughout the aquaculture cycle, peaking at its conclusion. In addition to the commonly dominant bacteria such as Proteobacteria, Actinobacteria, and Bacteroidetes, microorganisms like Marinomonas and Cobetia were also integral to nitrogen cycling, facilitated the degradation of organics, and contributed to the stability of the pond aquaculture environment. In summary, during the aquaculture cycle under the IMTA model, while the physicochemical indicators of the water body declined, the microorganisms within played a pivotal role.

Author Contributions

Contributions: H.Y.: Data curation; Writing—original draft; Writing—review & editing. B.T.: Project administration; Funding acquisition. H.Z.: Software; Validation; Data curation. P.Z.: Investigation; Resources; Supervision. L.Z.: Conceptualization; Methodology; Visualization. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Fund of Innovation team of Germplasm Resource Exploitation, Utilization and Health Assessment for Aquatic Animals [2022KCXTD013]; National Key Research and Development Program of China, “Capacity Enhancement and Carbon Sink Technology for Multi-Trophic Level Integrated Aquaculture Systems in Seawater Ponds” [2023YFD2401704]; Guangdong Modern Seeding Industry Park for Penaeus vannamei [K22218]. And The APC was funded by [2022KCXTD013].

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

Peigui Zhong was employed by the company Zhanjiang Haisite Aquatic Technology Co., Ltd. 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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Water 17 00887 g001
Figure 1. Periodic variations in physicochemical indicators of water.
Figure 1. Periodic variations in physicochemical indicators of water.
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Figure 2. OTU Venn diagram of microbial communities in water under different stage in the IMTA system. Note: E is Early stage; M is Middle stage; L is Late stage.
Figure 2. OTU Venn diagram of microbial communities in water under different stage in the IMTA system. Note: E is Early stage; M is Middle stage; L is Late stage.
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Figure 3. NMDS of microbial community structure in IMTA model water.
Figure 3. NMDS of microbial community structure in IMTA model water.
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Figure 4. Taxonomic identification of microorganisms in IMTA model water. Note: Only taxa with a Linear Discriminant Analysis (LDA) score higher than 4.0 among microorganisms in the aquaculture water bodies are shown. (a) The length of the bars represents the magnitude of the effect of the bacterial lineage. (b) Bacterial groups from phylum to genus are arranged from the center outwards. The diameter of each circle is proportional to the abundance of the bacterial taxa. Different colored nodes indicate microbial taxa that are significantly enriched in the corresponding group and have a significant impact on the differences between groups.
Figure 4. Taxonomic identification of microorganisms in IMTA model water. Note: Only taxa with a Linear Discriminant Analysis (LDA) score higher than 4.0 among microorganisms in the aquaculture water bodies are shown. (a) The length of the bars represents the magnitude of the effect of the bacterial lineage. (b) Bacterial groups from phylum to genus are arranged from the center outwards. The diameter of each circle is proportional to the abundance of the bacterial taxa. Different colored nodes indicate microbial taxa that are significantly enriched in the corresponding group and have a significant impact on the differences between groups.
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Figure 5. Composition of major microbial species within microbial communities in IMTA model water bodies: (a) phylum level and (b) genus level.
Figure 5. Composition of major microbial species within microbial communities in IMTA model water bodies: (a) phylum level and (b) genus level.
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Figure 6. Heatmap of microbial differences in IMTA model water bodies: (a) phylum level and (b) genus level.
Figure 6. Heatmap of microbial differences in IMTA model water bodies: (a) phylum level and (b) genus level.
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Figure 7. KEGG functional prediction of microbial communities in IMTA model water.
Figure 7. KEGG functional prediction of microbial communities in IMTA model water.
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Figure 8. CCA, environmental contribution degree, and Pearson analysis of various environmental factors in IMTA Model Water. Note: (a) CCA; (b) environmental contribution analysis; (c) the x-axis and y-axis are environmental factors and species, respectively. The correlation between R and P are obtained by calculation. R values are shown in different colors, and the legend on the right is the color range of different R values. * 0.01 < p ≤ 0.05; ** 0.001 < p ≤ 0.01; *** p ≤ 0.001.
Figure 8. CCA, environmental contribution degree, and Pearson analysis of various environmental factors in IMTA Model Water. Note: (a) CCA; (b) environmental contribution analysis; (c) the x-axis and y-axis are environmental factors and species, respectively. The correlation between R and P are obtained by calculation. R values are shown in different colors, and the legend on the right is the color range of different R values. * 0.01 < p ≤ 0.05; ** 0.001 < p ≤ 0.01; *** p ≤ 0.001.
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Table 1. Compound feed nutrients.
Table 1. Compound feed nutrients.
Hydration
≤		Protein
≥		Crude Fiber
≤		Crude Ash
≤		Crude Fat
≥		Total Phosphorus
≥		NaCl
≤		Lysine
≥
Compound feed12.9%40.0%6.0%20%0.7%5.0%5.0%1.5%
Table 2. Early and last values of physicochemical indicators for water.
Table 2. Early and last values of physicochemical indicators for water.
TimeNH4-NNO2-NNO3-NP-PO4TNTP
Early stage0.77 ± 0.060.16 ± 0.010.43 ± 0.011.04 ± 0.145.26 ± 1.640.33 ± 0.06
Late stage0.23 ± 0.040.05 ± 0.010.05 ± 0.010.33 ± 0.031.10 ± 0.160.12 ± 0.01
Table 3. Early, middle, and late α-diversity indices.
Table 3. Early, middle, and late α-diversity indices.
TimeOTUShannonSimpsonChaoAce
Early stage772 ± 106 b4.82 ± 0.29 b0.90 ± 0.03 b997.20 ± 154.801070.83 ± 154.85
Middle stage768 ± 71 b4.90 ± 0.16 b0.90 ± 0.00 b996.67 ± 76.401067.30 ± 82.62
Late stage875 ± 50 a5.55 ± 0.10 a0.93 ± 0.01 a1076.53 ± 59.231148.44 ± 75.20
Note: Different lowercase letters in the same column indicate significant differences (p < 0.05).
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Yang, H.; Tang, B.; Zhou, H.; Zhong, P.; Zhao, L. Research on the Construction of an Integrated Multi-Trophic Aquaculture (IMTA) Model in Seawater Ponds and Its Impact on the Aquatic Environment. Water 2025, 17, 887. https://doi.org/10.3390/w17060887

AMA Style

Yang H, Tang B, Zhou H, Zhong P, Zhao L. Research on the Construction of an Integrated Multi-Trophic Aquaculture (IMTA) Model in Seawater Ponds and Its Impact on the Aquatic Environment. Water. 2025; 17(6):887. https://doi.org/10.3390/w17060887

Chicago/Turabian Style

Yang, Han, Baogui Tang, Hui Zhou, Peigui Zhong, and Liqiang Zhao. 2025. "Research on the Construction of an Integrated Multi-Trophic Aquaculture (IMTA) Model in Seawater Ponds and Its Impact on the Aquatic Environment" Water 17, no. 6: 887. https://doi.org/10.3390/w17060887

APA Style

Yang, H., Tang, B., Zhou, H., Zhong, P., & Zhao, L. (2025). Research on the Construction of an Integrated Multi-Trophic Aquaculture (IMTA) Model in Seawater Ponds and Its Impact on the Aquatic Environment. Water, 17(6), 887. https://doi.org/10.3390/w17060887

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