Impact of Different Aquaculture Densities on the Growth Performance and Intestinal Health of Triploid Rainbow Trout (Oncorhynchus mykiss) Fry in High-Altitude Environments

Impact of Different Aquaculture Densities on the Growth Performance and Intestinal Health of Triploid Rainbow Trout (Oncorhynchus mykiss) Fry in High-Altitude Environments | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Impact of Different Aquaculture Densities on the Growth Performance and Intestinal Health of Triploid Rainbow Trout (Oncorhynchus mykiss) Fry in High-Altitude Environments yaoqiong zhang, Jianshe Zhou, Zhuangzhuang Wang, wanliang wang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6937259/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract [ Introduction ] To investigate the optimal stocking density for triploid rainbow trout seedlings in high-altitude environments, this study examined three different stocking densities: [ Method ] Low-Density group (LD) with 100 fish per barrel, Medium-Density group (MD) with 200 fish per barrel, and High-Density group (HD) with 300 fish per barrel, over a 60-day cultivation period. [ Result ] The results revealed that the final body weight (Wt) and specific growth rate (SGR) of the LD group were significantly higher than those of the HD group ( P < 0.05). Additionally, the survival rate of the LD group was significantly greater than that of the HD group ( P < 0.05). Variations are observed in the α-diversity of both water and gut microbial communities at different stocking densities. PCA analysis indicated significant differences (P < 0.05) in both the water and gut microbial communities among the different aquaculture densities. The dominant phyla in both aquatic and intestinal microbiomes were Proteobacteria, Firmicutes, and Bacteroidetes. Notably, the proportion of Pseudomonas genera was significantly higher in the HD and MD groups compared to the LD group. Co-occurrence network analysis revealed a higher average degree in the LD and MD groups across all densities, suggesting that the microbiota environments of both the intestinal and aquatic ecosystems in these groups were more stable. [ Conclusion ] In conclusion, low-density aquaculture is more suitable for plateau environments. These findings provide valuable insights for the ecological, efficient, and healthy aquaculture of triploid rainbow trout in high-altitude regions. Stocking density Oncorhynchus mykiss growth indicators water sample intestinal microbiota Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Triploid rainbow trout is a polyploid fish artificially induced through chromosome engineering techniques. From a genetic and production perspective, diploid rainbow trout generally exhibit clear advantages over their triploid counterparts. However, triploid rainbow trout show distinct benefits in terms of growth performance, with a faster growth rate (Larsen et al., 2012; Wang et al., 2024; Zhao et al., 2019), which allows for a shortened breeding cycle, meeting market specifications and enhancing breeding efficiency. Moreover, triploid rainbow trout possess superior meat quality, with delicate muscle texture, good taste, and rich nutrition, making them a highly competitive target in the market. As an artificial polyploid aquatic species cultivated via genome manipulation, triploid rainbow trout can improve the economic benefits of aquaculture and is particularly suitable for farming in Tibet. Known for being an excellent cold-water aquaculture fish, triploid rainbow trout exhibits underdeveloped gonads, alongside the advantages of fast growth, large size, and desirable meat color. The aquaculture cycle spans 2–3 years. Once the gonads mature, the fish experience super-diploid muscle growth rates, as well as improved disease resistance and meat quality (Meiler and Kumar, 2021; Wang et al., 2023; Xiang et al., 2022). Xizang is characterized by its high altitude, abundant rivers, and large lakes (Qiao et al., 2019), as well as rich cold-water resources, making it conducive to cold-water fish cultivation (Ganie et al., 2023). In recent years, significant progress has been made in rainbow trout farming, especially with the introduction of advanced technology and management experience in Shannan and Linzhi cities, leading to breakthroughs in the industrialization of high-altitude fisheries (Li et al., 2005). Numerous studies have been conducted on the breeding density and growth characteristics of triploid rainbow trout, both domestically and internationally. Research in the mountainous areas of Northeast China has demonstrated that juvenile rainbow trout exhibit good growth at a stocking density of 700 individuals per square meter, achieving high specific growth rates and faster body mass growth compared to full-length growth (Liu et al., 2020). In the Tibetan plateau region, however, growth indicators for female and juvenile rainbow trout vary significantly with different stocking densities, with final body weight, daily weight gain, specific growth rate, and net weight gain all showing a downward trend as stocking density increases (Zhou et al., 2018). Additionally, the meat quality and nutritional value of triploid rainbow trout, including high lipid and fatty acid content, have garnered attention due to their suitability for human consumption (Bao et al., 2023; Meiler and Kumar, 2021). Research conducted in Washington State, USA, has found that triploid rainbow trout exhibit similar movement patterns to diploid trout in lakes, but some lakes show lower capture and migration rates, which helps protect the genetic integrity of wild fish populations (Pease et al., 2023). Stocking density is a critical parameter in aquaculture that significantly impacts the growth performance of aquatic species (Barros et al., 2024; dos Santos et al., 2021; Irwin et al., 1999; Li et al., 2021; Manduca et al., 2020; Tolon et al., 2017). Gut microbiota is influenced by a range of factors, including host genetics, environmental variables, and diet (David et al., 2014; Dehler et al., 2017; Feng et al., 2018; Hasan and Yang, 2019), and can affect fish hosts' nutritional metabolism, diet, and disease resistance (Li et al., 2017; Roeselers et al., 2011; Wong et al., 2013). However, our understanding of how different farming densities and species interactions influence these factors remains limited (Sukhdhane et al., 2018). These interactions play a critical role in shaping community diversity, species dominance, and the composition of both broad-spectrum and commensal species in the gut microbiota of aquatic organisms. To date, no studies have examined the effects of aquaculture density on the growth performance and gut microbiota of triploid rainbow trout seedlings in high-altitude environments. This study is the first comprehensive and systematic investigation into the effects of different aquaculture densities on the growth performance, pathological tissues, microbiome composition, diversity, interspecies interactions, and community stability of triploid rainbow trout in high-altitude environments.. The results of this research will provide foundational data for determining the optimal captive breeding density of triploid rainbow trout seedlings in high-altitude environments, offering valuable insights for the ecological, efficient, and healthy aquaculture of triploid rainbow trout. 2. Materials and Methods 1. Breeding density setting The experimental site is located at the Yarlung Zangbo River Fishery Resources Breeding Base (29.638593° N, 91.030090° E). The terrain slopes from east to west and features a plateau temperate semi-arid monsoon climate. The area enjoys more than 3,000 hours of sunlight annually, with an altitude of 3,650 meters and an air pressure of 652.0 hPa. The Institute of Aquatic Sciences at the Tibet Academy of Agricultural and Animal Husbandry Sciences provided 1,800 triploid rainbow trout frys for the experiment. The experimental groups were as follows: the low-density group (LD) with 100 individuals, the medium-density group (MD) with 200 individuals, and the high-density group (HD) with 300 individuals. Each density group was subjected to a 60-day breeding experiment. The initial body weight of the fish was (2.26 ± 0.38) g, with an initial total length of (62.16 ± 3.95) mm and an initial body length of (51.77 ± 3.73) mm. The experimental fish were healthy and of similar size specifications (Table 1). Fish were randomly assigned to water tanks for aquaculture (45×45×3.14-0.03 m³ = 0.25 m³, tank diameter = 0.45 m, depth = 0.45 m). Feeding occurred three times a day (morning, afternoon, and evening), with the amount of feed being 2–3% of the body weight. The feed composition was as follows: high-quality feed pellets containing crude protein ≥ 50%, crude fat ≥ 8%, lysine ≥ 3%, crude fiber ≤ 5%, crude ash ≤ 16%, moisture ≤ 10%, and total phosphorus ≥ 1.5%. Water quality was regularly monitored, and changes in feeding amounts and the number of fish deaths in each experimental group were recorded daily (Figure 1). Ammonia nitrogen, nitrite, and pH were measured twice a week, with target values as follows: ammonia nitrogen and nitrite 5 mg/L, and temperature between 12.2°C and 13.3°C. The breeding experiment started on March 18, 2024, and concluded on May 16, 2024, lasting a total of 60 days. 2. Growth data collection Body weight, total length, and body length of the experimental fish were measured on the 0th, 15th, 30th, 45th, and 60th days of the experiment. Body length and weight were measured using a vernier caliper with an accuracy of 0.01 mm (GUANGLU) and an electronic balance with an accuracy of 0.01 g (WANT Balance Instrument Co., Ltd.), respectively. Table 1. Phenotypic growth data of triploid rainbow trout under different stocking densities Group Initial density Final density W 0 (g) W t (g) SR（%） SGR LD 1.3 kg/m 3 2.35kg/m 3 2.26±0.41 6.96±2.29 a 84.67 a 7.63 a MD 2.5 kg/m 3 4.35 kg/m 3 2.27±0.35 6.73±1.64 80.83 7.31 HD 4 kg/m 3 5.73 kg/m 3 2.25±0.39 6.56±2.27 b 72.67 b 7.07 b Note: Different letters indicate the level of significance (Duncan's multiple comparisons in analysis of variance, where the significance level is marked with "a, b, c" values indicating statistically significant differences) Survival rate (SR)=100% (N 0 - N t )/N 0 Weight gain rate (WGR)=100% (W t - W 0 )/W 0 Specific Growth Rate (SGR)=100% (lnW t - lnW 0 )/t Parameters such as initial average body weight (W₀), final average body weight (Wt), the number of feeding days (N), food intake (FI), and survival rate were calculated. Body weight (Wb) and body length (L) were also recorded, and the standard deviation of body mass (Ws) was calculated. 3. Collection and Processing of Intestinal and Water Samples After 60 days, sampling was conducted. Water samples (500 mL) were collected from a depth of 0.3 m in the breeding tanks and stored at low temperature before being transported to the laboratory. The samples were filtered using a PVDF membrane (0.22 μm pore size, GenClone Biotechnology Co., Ltd.) and the filtrate was collected in sterile centrifuge tubes, with three replicates for each group. For intestinal samples, three triploid rainbow trout were randomly selected from different density aquaculture ponds. The fish were anesthetized using MS-222 (100 mg/L, Beijing Green Hengxing Biotechnology Co., Ltd.) and dissected using sterile tools to remove the intestines. Intestinal contents were squeezed into 1 mL sterile centrifuge tubes, with every three samples combined into one tube and stored at -80°C for further analysis. 4. 16S rRNA High-Throughput Sequencing Analysis Sequencing was outsourced to Beijing Baimaike Biotechnology Co., Ltd. (Qingdao, China). PCR amplification targeted the V3-V4 regions of the bacterial 16S rRNA gene using primers 338F: 5'-ACTCCTACGGGAGGCAGCA-3' and 806R: 5'-GGACTACHVGGGTWTCTAAT-3'. Total genomic DNA was extracted from water and intestinal samples using the TGuide S96 magnetic soil/fecal DNA kit (Tiangen Biotechnology Co., Ltd.), and the PCR products were analyzed using agarose gel electrophoresis, purified with the Omega DNA purification kit (Omega Inc., Norcross, GA, USA), and sequenced on the Illumina Novaseq 6000 platform (2 × 250 bp). 5. Preparation of Liver, Gill, and Intestinal Tissue Slices 1. Liver and Gill Tissue Slicing Method For histological analysis, liver and gill tissues were fixed with 4% paraformaldehyde, processed according to standard pathology procedures, and subjected to microscopic examination. Tissue sections were observed under a white light microscope at various magnifications (100x, 200x, and 400x) using a Nikon Eclipse Ci-L microscope, and images were captured with Media Cybernetics Image Pro Plus 6.0 software. Muscle layer thickness was measured using Image Pro Plus 6.0, with five muscle layers measured per section. 2. Methodology for Measuring Intestinal Muscle Tissue Slices Use Eclipse Ci-L photographic microscope to select the muscle layer area of intestinal tissue for 100x imaging. During imaging, try to fill the entire field of view with tissue to ensure consistent background light for each photo. After imaging is completed, use Image Pro Plus 6.0 analysis software to uniformly measure the thickness of 5 muscle layers in each slice using millimeters as the standard unit (red arrows), and calculate the average value. 6. Data Processing and Statistical Analysis Statistical analysis was performed using R version 4.2.1 (https://cran.r-project.org, accessed on July 20, 2024). OTU data analysis was conducted using the dada2 package (https://benjjneb.github.io/dada2/, accessed on July 22, 2024). SPSS 21.0 software was used to analyze the growth parameters of fish and water physicochemical factors using one-way ANOVA. Alpha diversity indices and principal component analysis (PCA) were performed using the "vegan" package in R, including indices such as Shannon-Wiener diversity, Chao1, ACE, Simpson dominance, and Fisher. A heatmap of microbial community abundance was generated using R (https://blog.csdn.net/ByteNinja/article/details/132518709, accessed on July 23, 2024). A co-occurrence network of gut microbiota was constructed based on Spearman’s rank correlation coefficient and visualized using Gephi (version 0.10.1). Data visualization was performed using Origin2020 Campus Edition. 3. Results 1. Effects of Different Aquaculture Densities on Growth Performance Indicators of Triploid Rainbow Trout In a 60-day experiment, different breeding densities had a significant impact on the growth and survival rates of triploid rainbow trout fry (Table 1). On day 15, the medium-density (MD) group had the highest average weight at 3.21 g, followed closely by the high-density (HD) group at 3.18 g, while the low-density (LD) group had the lowest weight at 3.12 g (Table 1). By day 30, the LD group exhibited a significantly higher average weight compared to the HD group (P<0.001). During the periods from days 15 to 30 and from 45 to 60 (Figure 2), fish demonstrated the fastest weight gain, while the highest mortality occurred during the 30–45-day period, with the HD group showing the highest mortality rate. In terms of survival rate, the LD group had the highest at 84.6%, significantly higher than the HD group at 72.67% (P<0.05). The LD group also outperformed the MD and HD groups in final body weight, weight gain rate, specific growth rate, and feed conversion ratio (P<0.05) (Table 1). These results suggest that breeding density significantly influences the growth performance and survival rate of triploid rainbow trout fry. 2. Effects of Different Stocking Densities on Histological Structures of Liver, Gill and Intestine Tissues in Triploid Rainbow Trout (Oncorhynchus mykiss) Juveniles The histological structure of the gill tissue in triploid rainbow trout fry was affected by density stress (Figure 3). The LD group showed thickening of the gill filaments and proliferation of columnar cells, with no infiltration of inflammatory cells. In contrast, the MD and HD groups exhibited thickening of gill filaments, proliferation of columnar cells, accompanied by the presence of small vacuoles and inflammatory cell infiltration, as well as separation of the gill epithelium. Liver histology revealed that the liver cells in the LD group were arranged neatly, with dilated hepatic sinusoids and mild steatosis. In the MD and HD groups, fatty degeneration and hepatic sinusoidal dilation were more pronounced, with occasional lymphocyte infiltration. There was no significant difference in the thickness of the intestinal muscle layer among the groups. These findings indicate that density stress significantly impacts the histological characteristics of the gills and liver in triploid rainbow trout fry, while the intestinal muscle layer thickness remains unaffected. 3. Significant Changes in Gut and Water Microbiota Diversity in Triploid Rainbow Trout Under Varying Aquaculture Densities Gut and aquatic microbiota diversity in triploid rainbow trout was analyzed under different stocking densities using 16S rRNA gene sequencing. In the gut microbiota, the MD group had the highest number of OTUs (10,723), followed by the LD group (10,407), while the HD group had the lowest number (9,616), suggesting that aquaculture density impacts gut microbiota diversity. For the aquatic microbiota, the HD group had the highest number of OTUs (4,341), followed by the MD group (4,057), and the LD group had the lowest (2,987) (Figure 6). Diversity indices for gut microbiota alpha (OTU count, Chao1, ACE index, Shannon, and Simpson index) showed differences among the groups (Figure 4). The alpha diversity index for aquatic microbiota exhibited a similar trend, further indicating that aquaculture density affects both the gut and aquatic microbiota diversity in triploid rainbow trout. To better understand the distribution and similarity of gut and aquatic microbiota in triploid rainbow trout at different breeding densities, principal component analysis (PCA) was conducted (Figure 5). The PCA of gut microbiota revealed that the explanatory power of axis 1 was 14.41%, and axis 2 explained 75.54%. For aquatic microbiota, axis 1 explained 23.94%, and axis 2 explained 36.72%. The PCA results showed both similarities and differences in the gut microbiota between groups. There were significant differences in the microbial community of water samples (P<0.05). 4. Alterations in Gut Microbiota Composition of Triploid Rainbow Trout Under Different Breeding Densities At the OTU level, the MD group identified the highest number of OTUs (10,723), followed by the LD group (10,407), and the HD group (9,616) (Figure 6). The composition and relative abundance of gut microbiota in triploid rainbow trout were significantly influenced by aquaculture density. The MD group exhibited 9,695 unique OTUs, with 330 OTUs more than the HD group, 140 more than the LD group, and 254 more than both. The HD group contained 8,516 unique OTUs, and had 193 OTUs more than the LD group. The LD group had 9,627 unique OTUs (Figure 6). 5. Shifts in the Genus Composition of Intestinal Microbiota in Triploid Rainbow Trout at Various Breeding Densities The analysis of gut microbiota at the genus level showed that the proportion of Pseudomonas (Figure 7). At the phylum level, the dominant phyla were Proteobacteria, Firmicutes, and Bacteroidetes (Figure 6). Although the phylum composition was similar across groups, the abundance of each phylum varied, indicating differences in the gut microbiota composition among the groups. At the genus level, the proportion of Pseudomonas in the gut microbiota of triploid rainbow trout was higher in the MD and HD groups than in the LD group (Figure 7, Figure 1). Notably, the proportion of Pseudomonas in the HD group was twice that of the LD group. Studies suggest that Pseudomonas can cause intestinal inflammation in fish. In the water samples, the proportion of Pseudomonas was higher in the LD and MD groups than in the HD group, which was the opposite of the gut microbiota composition. However, no adverse effects on growth performance or survival rate were observed in the LD and MD groups (Figure 7). The survival rate and body weight of the LD group were significantly higher than those of the HD group (P<0.05), suggesting that the microbial composition in the LD group was more stable. 6. Network Analysis of Co-Occurrence of Gut and Water Microbiota Under Different Aquaculture Densities To further explore the interrelationships between gut and water microbiota at different aquaculture densities, co-occurrence network analysis was performed on the distribution of core gut microbiota (Table 4). The coefficients of the three modular co-occurrence networks were all greater than 0.2, indicating a significant modular structure. Network indicators, such as the number of nodes and edges, and the proportion of microbial communities, varied across the three breeding density methods. The network displayed a high modularity coefficient for the LD and MD groups (LD = 0.52, MD = 0.41), while the HD group had a lower coefficient (0.28). This suggests that the interactions between gut microbiota in the LD and MD groups were more complex than in the HD group. Further analysis revealed that the top three phyla—Proteobacteria, Firmicutes, and Bacteroidetes—exhibited a higher proportion of interactions with other taxa in the LD and MD groups, indicating that Proteobacteria plays a crucial role in the network structure at these densities (Figure 8). The network analysis aligned with growth data, which showed that the LD and MD groups had higher survival rates, and the LD group exhibited the most connections with the MD group. This indicates that the gut and aquatic microbiota environments of the LD and MD groups are more stable, which correlates with the higher survival rate observed in these groups. Finally, co-occurrence network analysis of the water microbiota revealed that the modular coefficients for the three groups were greater than 0.4, indicating significant modular structures. The LD and MD groups showed high modularity coefficients (LD = 0.62, MD = 0.42), while the HD group had a slightly lower coefficient (0.49). This suggests that the interactions between gut and water microbiota in the LD and HD groups were more complex compared to the MD group (Figure 8). 4. Discussion 1. Impact of Density Stress on Growth Indicators in Triploid Rainbow Trout Breeding density is a key factor that influences the growth, survival, and yield of artificially farmed fish(Dediu et al., 2021; Ezhilmathi et al., 2022; Görelşahin et al., 2018; Li et al., 2024). Research indicates that increasing breeding density inhibits the growth performance of fish, with the negative effects of high density becoming more pronounced (Jia et al., 2022). In this experiment, from the third sampling onward, the growth performance of the low-density (LD) group was significantly better than that of the medium-density (MD) and high-density (HD) groups(Figure 2), a trend that persisted throughout the study (Diao et al., 2023; Refaey et al., 2018). Weight and body length increased with breeding time, but significantly decreased with increasing density ( P <0.05). Different breeding densities had a significant effect on the final body weight, weight gain rate, specific growth rate, and feed conversion ratio of triploid rainbow trout fry ( P <0.05). These findings align with previous research showing that under appropriate density conditions, the specific growth rate (SGR) of fish significantly improves (Hassan et al., n.d.; Zaki et al., 2020). Initially, there was no significant difference between the groups, but after 15 days, the weight of the MD group was significantly higher than that of the HD and LD groups ( P <0.05), likely due to the stress caused by high density affecting appetite (Tian et al., 2025). By day 30, there was a significant difference in body weight between the LD group and the MD and HD groups ( P <0.05), possibly due to increased competition for space, dissolved oxygen, and food in high-density conditions, resulting in higher energy expenditure and lower growth rates (Nong et al., 2024). These results are consistent with the findings of other scholars (Dewi et al., 2020; Puspaningsih et al., 2021; Syah et al., 2020). This study demonstrated that density stress significantly affected the histology of gills and liver in triploid rainbow trout fry. The LD group showed gill filament thickening and columnar cell proliferation without inflammation, while MD/HD groups exhibited gill epithelial separation, vacuolation, inflammatory infiltration, and severe hepatic steatosis(Zhao et al., 2024). Intestinal muscle layer was unaffected, providing a basis for optimizing stocking density. 2. Effect of Density Stress on the Characteristics of Gut and Aquatic Microbial Communities in Triploid Rainbow Trout In aquaculture, the impact of stocking density on water quality and gut microbiota is critical, as it directly affects the health of aquatic animals (Clols-Fuentes et al., 2023; Xie et al., 2025). This study analyzed gut and water samples from triploid rainbow trout fry under different densities using OTUs. A total of 27,838 OTUs were identified in the gut microbiota (Figure 6) . The HD, MD, and LD groups had 8,516, 9,695, and 9,627 unique OTUs, respectively, indicating that the gut microbiota in the HD group contained fewer OTUs compared to the MD and LD groups. The water samples contained a total of 8,955 OTUs, with 3,462, 3,362, and 2,131 unique OTUs found in the HD, MD, and LD groups, respectively. As aquaculture density increased, the number of OTUs in the water gradually increased (Song et al., 2024). Additionally, with higher breeding density, the Alpha diversity index of both the aquatic and gut microbiota of triploid rainbow trout fry showed an upward trend. Notably, the Chao, ACE, and Shannon indices in the MD and HD intestinal groups were higher, reflecting greater species richness compared to the LD group(Figure 4) (Xie et al., 2025). The Simpson index for the HD gut microbiota was high, with differences observed when compared to the other groups (Song et al., 2024; Yuan et al., 2023). The dominant microbial community showed an increase in the proportion of Proteobacteria with rising density. The relative abundance of Proteobacteria significantly increased in high-density aquaculture, suggesting an elevated risk of disease and decreased stability in the gut microbiota (Chang et al., 2023; Du et al., 2024; Ma et al., 2019; Zhang et al., 2019; Zoccarato et al., 1994). The phyla Firmicutes and Bacteroidetes were most abundant in the LD group, followed by the HD group, and least abundant in the MD group. The trends for Actinobacteria and Proteobacteria were opposite, with their populations declining as density increased. These changes indicate significant differences in the gut microbiota of triploid rainbow trout under different stocking densities (Domingo-Bretón et al., 2025; Zhang et al., 2025). Principal component analysis (PCA) was used to investigate the effects of different stocking densities on the gut and aquatic microbiota of triploid rainbow trout fry(Figure 5).The results showed that the composition and structure of gut microbiota were similar across all groups, with significant ecological niche overlap and stable competitive relationships (Wong et al., 2013). However, significant differences ( P <0.05) were observed in the microbial community of water samples between the LD, HD, and MD groups, indicating that water microbiota are more sensitive to density changes. The stability of gut microbiota may be influenced by the host’s physiological status and immune system regulation (Bao et al., 2023; Irwin et al., 1999; Pease et al., 2023), while the aquatic microbiota appears to be more susceptible to density stress. In both positive and negative ion modes, the LD group water samples displayed distinct distributions, possibly indicating the presence of specific microbial communities or metabolites. In contrast, the water samples in the HD group were more similar in both ion modes, while the MD group showed a more scattered distribution, primarily clustered in the positive ion mode, suggesting higher microbial diversity in the MD group water samples, likely reflecting differences in microbial metabolic activity under density stress. 3. Network Analysis of Gut and Water Microbial Community Composition at Different Stocking Densities Based on growth data and survival rates, breeding density likely contributes to the increased proportion of Pseudomonas species. Pseudomonas, an opportunistic pathogen, can cause intestinal infections in fish, leading to disease and mortality (Barros et al., 2024; Wheatley et al., 2022). Overgrowth of Pseudomonas can disrupt the balance of gut microbiota in fish, impairing their digestion and absorption functions(Figure 7), which ultimately affects their intestinal health and growth performance (Bordignon et al., 2020). Moreover, studies suggest that changes in gut microbiota in farmed rainbow trout depend on the degree of infection (Wong and Rawls, 2012), which may be linked to inflammatory enteritis in triploid rainbow trout fry. Research has shown that Pseudomonas is more abundant in the intestines of IBD patients and plays a role in disrupting microbial communities, which can influence disease progression (Dehler et al., 2017; Li et al., 2017). Thus, excessive proliferation of Pseudomonas may exacerbate intestinal inflammation, impair intestinal barrier function, and ultimately affect the intestinal health of triploid rainbow trout fry (Roeselers et al., 2011). The high similarity in species composition between the LD and HD groups may be attributed to density stress, offering insights into the ecological adaptability and evolution of microbial communities. The LD group exhibited a higher total number of nodes and modularity coefficient within the aquatic microbial community, suggesting a more complex interaction network(Figure 8) (Wang et al., 2024). These results emphasize that breeding density significantly impacts the intestinal health of triploid rainbow trout fry, with the LD and MD groups showing superior stability in their gut microbiota (Figure 8) . 5. Conclusion In summary, density stress has a profound impact on the intestinal health of triploid rainbow trout fry. According to the data presented in this study, low-density farming conditions are more suitable for breeding triploid rainbow trout fry in high-altitude environments. This study underscores the importance of balancing microbial communities in aquaculture management to ensure the health and growth performance of triploid rainbow trout. Future research could further investigate the specific mechanisms by which Pseudomonas influences the intestinal health of triploid rainbow trout, providing more scientific support for aquaculture practices. Through a comprehensive analysis of growth data and survival rates, this study offers new perspectives for understanding the complexity of microbiota-host interactions and paves the way for future research and applications. This work highlights the critical significance of considering the impact of aquaculture density on microbial stability and host health, offering a solid scientific foundation for optimizing aquaculture management strategies. Declarations The Tibetan Autonomous Region Academy of Agricultural and Animal Sciences, Institute of Aquatic Sciences, Ethical Approval for Experimental Animals Number (No): 2025002 The paper "Impact of Different Aquaculture Densities on the Growth Performance and Intestinal Health of Triploid Rainbow Trout (Oncorhynchus mykiss) Fry in High-Altitude Environments" authored by Wang Wanliang and others from the Institute of Aquatic Sciences, Tibetan Autonomous Region Academy of Agricultural and Animal Sciences. After review by the Ethical Committee for Experimental Animals of the Institute of Aquatic Sciences, Tibetan Autonomous Region Academy of Agricultural and Animal Sciences, it is deemed that the design and implementation of this paper are scientific and feasible, with appropriate measures taken to reduce animal suffering, injury, and mortality. All processes comply with the ethical requirements for experimental animals. The Ethical Committee for Aquatic Sciences Research of the Tibetan Autonomous Region Academy of Agricultural and Animal Sciences will regulate aspects such as research protocols and informed consent to ensure the ethical and rational use of experimental animals. Ethical Committee for Aquatic Sciences Research Tibetan Autonomous Region Academy of Agricultural and Animal Sciences Declaration of Competing Interest The authors declare no conflicts of interest, financial or otherwise. Data availability Data will be made available on request. Acknowledgment This work was supported by the Aquatic Science Research Institute of the Tibet Autonomous Region Academy of Agriculture and Animal Husbandry. Author Contributions -Zhang Yaoqiong: Conceptualization, Methodology, Writing – Original Draft, Investigation, Data Curation, Writing – Review & Editing, Software, Formal Analysis, Visualization. - Wu Mengyu: Investigation. - Wang Zhuangzhuang: Review & Editing. - Zhou Jianshe: Funding Acquisition. -Zhang ning：Supervise - Wang Wanliang (Corresponding author): Supervision. Ethics statement The experiments in this study were conducted in strict adherence to the guidelines outlined in Institute of Fisheries Science, Xizang Academy of Agriculture andAnimal Husbandry Sciences, Ethical Approval for Experimental Animals. Project Support ： This project is supported by the National System for the Technology of Characteristic Freshwater Fish Industry (Project No. CARS-46). 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Effects of different stocking densities on the growth characteristics of all-female rainbow trout fingerlings. Aquaculture (in Chinese).39: 13-17. https://doi.org/10.3969/j.issn.1004-2091.2018.01.003. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6937259","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":475905914,"identity":"80400692-f5dd-41c9-9f0b-5295778c9490","order_by":0,"name":"yaoqiong zhang","email":"","orcid":"","institution":"Tibet Autonomous region academy of agricultural and animal husbandry sciences,institute of aquatic sciences","correspondingAuthor":false,"prefix":"","firstName":"yaoqiong","middleName":"","lastName":"zhang","suffix":""},{"id":475905917,"identity":"8094dde8-7e5b-4b54-9260-9c2127fa7664","order_by":1,"name":"Jianshe Zhou","email":"","orcid":"","institution":"Tibet Autonomous region academy of agricultural and animal husbandry sciences,institute of aquatic sciences","correspondingAuthor":false,"prefix":"","firstName":"Jianshe","middleName":"","lastName":"Zhou","suffix":""},{"id":475905918,"identity":"b10885ac-e109-4b0b-b67e-8c6cb6ba378a","order_by":2,"name":"Zhuangzhuang Wang","email":"","orcid":"","institution":"Tibet Autonomous region academy of agricultural and animal husbandry sciences,institute of aquatic sciences","correspondingAuthor":false,"prefix":"","firstName":"Zhuangzhuang","middleName":"","lastName":"Wang","suffix":""},{"id":475905919,"identity":"bf9802b9-3c36-4490-baec-235e0ced5656","order_by":3,"name":"wanliang wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIie2RsQrCQAxAcxzcLbE6RvyJE0EcSvsrlUJdHJycO3XyA/wMP+H0UDdnBxcpdHZUcPCquPZ0E7wHCSTkQUIAPJ5fRALTACOQuS0uKozcCgewCgHazJazLP1O4XjZsNxlBAbV+lZQjDSpeqHSHKTZrpqUrhGJaRXEkZJ0MFWnADDLjk2KMlwbVpCIKVmXU1VxIBw6FJbXiyHSODejunQrXGu7GCGlrIRPlOcteCCFWPH+QmWpcN0S7Bf983UexignFV3vYdSWZteo1E8BJmzqJK+GaBx/K3C30dbOWY/H4/lTHtmHSDID1V9gAAAAAElFTkSuQmCC","orcid":"","institution":"Tibet Autonomous region academy of agricultural and animal husbandry sciences,institute of aquatic sciences","correspondingAuthor":true,"prefix":"","firstName":"wanliang","middleName":"","lastName":"wang","suffix":""},{"id":475905921,"identity":"a6b09349-bc1d-42ad-85b8-3d2ff03151ce","order_by":4,"name":"Ning Zhang","email":"","orcid":"","institution":"Guangdong Ocean University","correspondingAuthor":false,"prefix":"","firstName":"Ning","middleName":"","lastName":"Zhang","suffix":""},{"id":475905922,"identity":"f6fac94d-2543-4f6a-8977-4f7f549faa50","order_by":5,"name":"Mengyu Wu","email":"","orcid":"","institution":"Guangdong Ocean University","correspondingAuthor":false,"prefix":"","firstName":"Mengyu","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2025-06-20 09:08:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6937259/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6937259/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85914625,"identity":"178fc2c0-081e-442f-92c8-9c507862e3a0","added_by":"auto","created_at":"2025-07-03 06:38:07","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":73128,"visible":true,"origin":"","legend":"\u003cp\u003eFigure A: Comparison of excretion between the LD group and the HD group. Figure B: Sampling of dead fry of triploid rainbow trout in the HD group\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6937259/v1/9baeb5c321d8178bd3e611ee.jpg"},{"id":85914236,"identity":"3ffc4e54-1825-4e83-bcdd-4438060e973b","added_by":"auto","created_at":"2025-07-03 06:30:07","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":37479,"visible":true,"origin":"","legend":"\u003cp\u003eBody weight changes of\u003cem\u003e \u003c/em\u003etriploid rainbow trout juvenile in different culture density groups.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6937259/v1/4f7c53905e13251abad040d7.jpg"},{"id":85914238,"identity":"9b378f02-fb96-4ca3-b135-2701633ffe0e","added_by":"auto","created_at":"2025-07-03 06:30:07","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":92507,"visible":true,"origin":"","legend":"\u003cp\u003ePathological tissue sections of triploid rainbow trout with different densities. Figure A gill tissue section, Figure B liver tissue section, Figure C intestinal tissue section\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6937259/v1/87024c2d41eed99c12d09f68.jpg"},{"id":85914624,"identity":"cee59902-1d00-4fbd-afd3-da68c5de86d6","added_by":"auto","created_at":"2025-07-03 06:38:07","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":40650,"visible":true,"origin":"","legend":"\u003cp\u003eFigure A shows the Alpha diversity index of intestinal flora, and figure B shows the Alpha diversity index of water samples.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6937259/v1/9fbbff7f02fd01677b39e770.jpg"},{"id":85914240,"identity":"22dfdce6-83cd-480d-ad71-09e06748943b","added_by":"auto","created_at":"2025-07-03 06:30:07","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":61132,"visible":true,"origin":"","legend":"\u003cp\u003eFigure A shows intestinal flora PCA, and figure b shows water sample flora PCA.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6937259/v1/1c867f74a62f1bfb84c8979a.jpg"},{"id":85914241,"identity":"3e18299f-31cf-4266-9cdf-e7fc36d46340","added_by":"auto","created_at":"2025-07-03 06:30:07","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":722631,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in gut microbiota composition of triploid rainbow trout juveniles in different aquaculture density groups\u003c/p\u003e","description":"","filename":"zhangetalfig6.png","url":"https://assets-eu.researchsquare.com/files/rs-6937259/v1/b6f61bc0a087f903dffcfad6.png"},{"id":85914242,"identity":"ba407dca-3d56-4dfc-9f21-fe244935e15b","added_by":"auto","created_at":"2025-07-03 06:30:07","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":343584,"visible":true,"origin":"","legend":"\u003cp\u003eFigure A shows the horizontal proportion of intestinal flora, and figure B shows the horizontal proportion of water sample flora.\u003c/p\u003e","description":"","filename":"zhangetalfig7.png","url":"https://assets-eu.researchsquare.com/files/rs-6937259/v1/1f70ea1f37d99f7ece728b03.png"},{"id":85914243,"identity":"90376865-e65b-44fb-8471-8adec64444a6","added_by":"auto","created_at":"2025-07-03 06:30:07","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1347047,"visible":true,"origin":"","legend":"\u003cp\u003eFigure A shows the co-occurrence network of intestines with different densities, and Figure B shows the co-occurrence network of water samples with different densities\u003c/p\u003e","description":"","filename":"zhangetalfig8.png","url":"https://assets-eu.researchsquare.com/files/rs-6937259/v1/a5ffbb8409740474577d45b0.png"},{"id":86611774,"identity":"21485941-8865-4d6c-9b51-0d67239af6a1","added_by":"auto","created_at":"2025-07-13 20:01:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2751077,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6937259/v1/491e6ed6-eb99-4548-80ef-0e1ba5179236.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impact of Different Aquaculture Densities on the Growth Performance and Intestinal Health of Triploid Rainbow Trout (Oncorhynchus mykiss) Fry in High-Altitude Environments","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eTriploid rainbow trout is a polyploid fish artificially induced through chromosome engineering techniques. From a genetic and production perspective, diploid rainbow trout generally exhibit clear advantages over their triploid counterparts. However, triploid rainbow trout show distinct benefits in terms of growth performance, with a faster growth rate\u0026nbsp;(Larsen et al., 2012; Wang et al., 2024; Zhao et al., 2019), which allows for a shortened breeding cycle, meeting market specifications and enhancing breeding efficiency. Moreover, triploid rainbow trout possess superior meat quality, with delicate muscle texture, good taste, and rich nutrition, making them a highly competitive target in the market. As an artificial polyploid aquatic species cultivated via genome manipulation, triploid rainbow trout can improve the economic benefits of aquaculture and is particularly suitable for farming in Tibet. Known for being an excellent cold-water aquaculture fish, triploid rainbow trout exhibits underdeveloped gonads, alongside the advantages of fast growth, large size, and desirable meat color. The aquaculture cycle spans 2–3 years. Once the gonads mature, the fish experience super-diploid muscle growth rates, as well as improved disease resistance and meat quality\u0026nbsp;(Meiler and Kumar, 2021; Wang et al., 2023; Xiang et al., 2022).\u003c/p\u003e\n\u003cp\u003eXizang is characterized by its high altitude, abundant rivers, and large lakes\u0026nbsp;(Qiao et al., 2019), as well as rich cold-water resources, making it conducive to cold-water fish cultivation\u0026nbsp;(Ganie et al., 2023). In recent years, significant progress has been made in rainbow trout farming, especially with the introduction of advanced technology and management experience in Shannan and Linzhi cities, leading to breakthroughs in the industrialization of high-altitude fisheries\u0026nbsp;(Li et al., 2005). Numerous studies have been conducted on the breeding density and growth characteristics of triploid rainbow trout, both domestically and internationally. Research in the mountainous areas of Northeast China has demonstrated that juvenile rainbow trout exhibit good growth at a stocking density of 700 individuals per square meter, achieving high specific growth rates and faster body mass growth compared to full-length growth\u0026nbsp;(Liu et al., 2020). In the Tibetan plateau region, however, growth indicators for female and juvenile rainbow trout vary significantly with different stocking densities, with final body weight, daily weight gain, specific growth rate, and net weight gain all showing a downward trend as stocking density increases\u0026nbsp;(Zhou et al., 2018). Additionally, the meat quality and nutritional value of triploid rainbow trout, including high lipid and fatty acid content, have garnered attention due to their suitability for human consumption\u0026nbsp;(Bao et al., 2023; Meiler and Kumar, 2021). Research conducted in Washington State, USA, has found that triploid rainbow trout exhibit similar movement patterns to diploid trout in lakes, but some lakes show lower capture and migration rates, which helps protect the genetic integrity of wild fish populations\u0026nbsp;(Pease et al., 2023).\u003c/p\u003e\n\u003cp\u003eStocking density is a critical parameter in aquaculture that significantly impacts the growth performance of aquatic species\u0026nbsp;(Barros et al., 2024; dos Santos et al., 2021; Irwin et al., 1999; Li et al., 2021; Manduca et al., 2020; Tolon et al., 2017). Gut microbiota is influenced by a range of factors, including host genetics, environmental variables, and diet\u0026nbsp;(David et al., 2014; Dehler et al., 2017; Feng et al., 2018; Hasan and Yang, 2019), and can affect fish hosts' nutritional metabolism, diet, and disease resistance\u0026nbsp;(Li et al., 2017; Roeselers et al., 2011; Wong et al., 2013). However, our understanding of how different farming densities and species interactions influence these factors remains limited\u0026nbsp;(Sukhdhane et al., 2018). These interactions play a critical role in shaping community diversity, species dominance, and the composition of both broad-spectrum and commensal species in the gut microbiota of aquatic organisms. To date, no studies have examined the effects of aquaculture density on the growth performance and gut microbiota of triploid rainbow trout seedlings in high-altitude environments.\u003c/p\u003e\n\u003cp\u003eThis study is the first comprehensive and systematic investigation into the effects of different aquaculture densities on the growth performance, pathological tissues, microbiome composition, diversity, interspecies interactions, and community stability of triploid rainbow trout in high-altitude environments.. The results of this research will provide foundational data for determining the optimal captive breeding density of triploid rainbow trout seedlings in high-altitude environments, offering valuable insights for the ecological, efficient, and healthy aquaculture of triploid rainbow trout.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003e\u003cem\u003e1.\u0026nbsp;\u003c/em\u003e\u003cem\u003eBreeding density setting\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe experimental site is located at the Yarlung Zangbo River Fishery Resources Breeding Base (29.638593\u0026deg; N, 91.030090\u0026deg; E). The terrain slopes from east to west and features a plateau temperate semi-arid monsoon climate. The area enjoys more than 3,000 hours of sunlight annually, with an altitude of 3,650 meters and an air pressure of 652.0 hPa. The Institute of Aquatic Sciences at the Tibet Academy of Agricultural and Animal Husbandry Sciences provided 1,800 triploid rainbow trout frys for the experiment. The experimental groups were as follows: the low-density group (LD) with 100 individuals, the medium-density group (MD) with 200 individuals, and the high-density group (HD) with 300 individuals. Each density group was subjected to a 60-day breeding experiment. The initial body weight of the fish was (2.26 \u0026plusmn; 0.38) g, with an initial total length of (62.16 \u0026plusmn; 3.95) mm and an initial body length of (51.77 \u0026plusmn; 3.73) mm. The experimental fish were healthy and of similar size specifications (Table 1). Fish were randomly assigned to water tanks for aquaculture (45\u0026times;45\u0026times;3.14-0.03 m\u0026sup3; = 0.25 m\u0026sup3;, tank diameter = 0.45 m, depth = 0.45 m). Feeding occurred three times a day (morning, afternoon, and evening), with the amount of feed being 2\u0026ndash;3% of the body weight. The feed composition was as follows: high-quality feed pellets containing crude protein \u0026ge; 50%, crude fat \u0026ge; 8%, lysine \u0026ge; 3%, crude fiber \u0026le; 5%, crude ash \u0026le; 16%, moisture \u0026le; 10%, and total phosphorus \u0026ge; 1.5%. Water quality was regularly monitored, and changes in feeding amounts and the number of fish deaths in each experimental group were recorded daily (Figure 1). Ammonia nitrogen, nitrite, and pH were measured twice a week, with target values as follows: ammonia nitrogen and nitrite \u0026lt; 0.02, pH between 7.6 and 7.8, dissolved oxygen \u0026gt; 5 mg/L, and temperature between 12.2\u0026deg;C and 13.3\u0026deg;C. The breeding experiment started on March 18, 2024, and concluded on May 16, 2024, lasting a total of 60 days.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.\u0026nbsp;\u003c/em\u003e\u003cem\u003eGrowth data collection\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eBody weight, total length, and body length of the experimental fish were measured on the 0th, 15th, 30th, 45th, and 60th days of the experiment. Body length and weight were measured using a vernier caliper with an accuracy of 0.01 mm (GUANGLU) and an electronic balance with an accuracy of 0.01 g (WANT Balance Instrument Co., Ltd.), respectively.\u003c/p\u003e\n\u003cp\u003eTable 1. Phenotypic growth data of triploid rainbow trout under different stocking densities\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"548\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eInitial density\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFinal density\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eW\u003csub\u003e0\u003c/sub\u003e (g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eW\u003csub\u003et\u003c/sub\u003e (g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSR（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSGR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.3 kg/m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.35kg/m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.26\u0026plusmn;0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.96\u0026plusmn;2.29\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e84.67\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.63\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.5 kg/m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.35 kg/m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.27\u0026plusmn;0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.73\u0026plusmn;1.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e80.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4 kg/m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.73 kg/m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.25\u0026plusmn;0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.56\u0026plusmn;2.27\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e72.67\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.07\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: Different letters indicate the level of significance (Duncan\u0026apos;s multiple comparisons in analysis of variance, where the significance level is marked with \u0026quot;a, b, c\u0026quot; values indicating statistically significant differences)\u003c/p\u003e\n\u003cp\u003eSurvival rate (SR)=100% (N\u003csub\u003e0\u003c/sub\u003e - N\u003csub\u003et\u003c/sub\u003e)/N\u003csub\u003e0\u003c/sub\u003e\u003c/p\u003e\n\u003cp\u003eWeight gain rate (WGR)=100% (W\u003csub\u003et\u003c/sub\u003e - W\u003csub\u003e0\u003c/sub\u003e)/W\u003csub\u003e0\u003c/sub\u003e\u003c/p\u003e\n\u003cp\u003eSpecific Growth Rate (SGR)=100% (lnW\u003csub\u003et\u003c/sub\u003e - lnW\u003csub\u003e0\u003c/sub\u003e)/t\u003c/p\u003e\n\u003cp\u003eParameters such as initial average body weight (W₀), final average body weight (Wt), the number of feeding days (N), food intake (FI), and survival rate were calculated. Body weight (Wb) and body length (L) were also recorded, and the standard deviation of body mass (Ws) was calculated.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.\u0026nbsp;\u003c/em\u003e\u003cem\u003eCollection and Processing of Intestinal and Water Samples\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAfter 60 days, sampling was conducted. Water samples (500 mL) were collected from a depth of 0.3 m in the breeding tanks and stored at low temperature before being transported to the laboratory. The samples were filtered using a PVDF membrane (0.22 \u0026mu;m pore size, GenClone Biotechnology Co., Ltd.) and the filtrate was collected in sterile centrifuge tubes, with three replicates for each group. For intestinal samples, three triploid rainbow trout were randomly selected from different density aquaculture ponds. The fish were anesthetized using MS-222 (100 mg/L, Beijing Green Hengxing Biotechnology Co., Ltd.) and dissected using sterile tools to remove the intestines. Intestinal contents were squeezed into 1 mL sterile centrifuge tubes, with every three samples combined into one tube and stored at -80\u0026deg;C for further analysis.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e4.\u0026nbsp;\u003c/em\u003e\u003cem\u003e16S rRNA High-Throughput Sequencing Analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSequencing was outsourced to Beijing Baimaike Biotechnology Co., Ltd. (Qingdao, China). PCR amplification targeted the V3-V4 regions of the bacterial 16S rRNA gene using primers 338F: 5\u0026apos;-ACTCCTACGGGAGGCAGCA-3\u0026apos; and 806R: 5\u0026apos;-GGACTACHVGGGTWTCTAAT-3\u0026apos;. Total genomic DNA was extracted from water and intestinal samples using the TGuide S96 magnetic soil/fecal DNA kit (Tiangen Biotechnology Co., Ltd.), and the PCR products were analyzed using agarose gel electrophoresis, purified with the Omega DNA purification kit (Omega Inc., Norcross, GA, USA), and sequenced on the Illumina Novaseq 6000 platform (2 \u0026times; 250 bp).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e5.\u0026nbsp;\u003c/em\u003e\u003cem\u003ePreparation of Liver, Gill, and Intestinal Tissue Slices\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e1.\u0026nbsp;\u003c/em\u003e\u003cem\u003eLiver and Gill Tissue Slicing Method\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFor histological analysis, liver and gill tissues were fixed with 4% paraformaldehyde, processed according to standard pathology procedures, and subjected to microscopic examination. Tissue sections were observed under a white light microscope at various magnifications (100x, 200x, and 400x) using a Nikon Eclipse Ci-L microscope, and images were captured with Media Cybernetics Image Pro Plus 6.0 software. Muscle layer thickness was measured using Image Pro Plus 6.0, with five muscle layers measured per section.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.\u0026nbsp;\u003c/em\u003e\u003cem\u003eMethodology for Measuring Intestinal Muscle Tissue Slices\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eUse Eclipse Ci-L photographic microscope to select the muscle layer area of intestinal tissue for 100x imaging. During imaging, try to fill the entire field of view with tissue to ensure consistent background light for each photo. After imaging is completed, use Image Pro Plus 6.0 analysis software to uniformly measure the thickness of 5 muscle layers in each slice using millimeters as the standard unit (red arrows), and calculate the average value.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e6.\u0026nbsp;\u003c/em\u003e\u003cem\u003eData Processing and Statistical Analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analysis was performed using R version 4.2.1 (https://cran.r-project.org, accessed on July 20, 2024). OTU data analysis was conducted using the dada2 package (https://benjjneb.github.io/dada2/, accessed on July 22, 2024). SPSS 21.0 software was used to analyze the growth parameters of fish and water physicochemical factors using one-way ANOVA. Alpha diversity indices and principal component analysis (PCA) were performed using the \u0026quot;vegan\u0026quot; package in R, including indices such as Shannon-Wiener diversity, Chao1, ACE, Simpson dominance, and Fisher. A heatmap of microbial community abundance was generated using R (https://blog.csdn.net/ByteNinja/article/details/132518709, accessed on July 23, 2024). A co-occurrence network of gut microbiota was constructed based on Spearman\u0026rsquo;s rank correlation coefficient and visualized using Gephi (version 0.10.1). Data visualization was performed using Origin2020 Campus Edition.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e1. Effects of Different Aquaculture Densities on Growth Performance Indicators of Triploid Rainbow Trout\u003c/p\u003e\n\u003cp\u003eIn a 60-day experiment, different breeding densities had a significant impact on the growth and survival rates of triploid rainbow trout fry (Table 1). On day 15, the medium-density (MD) group had the highest average weight at 3.21 g, followed closely by the high-density (HD) group at 3.18 g, while the low-density (LD) group had the lowest weight at 3.12 g (Table 1). By day 30, the LD group exhibited a significantly higher average weight compared to the HD group (P\u0026lt;0.001). During the periods from days 15 to 30 and from 45 to 60 (Figure 2), fish demonstrated the fastest weight gain, while the highest mortality occurred during the 30\u0026ndash;45-day period, with the HD group showing the highest mortality rate. In terms of survival rate, the LD group had the highest at 84.6%, significantly higher than the HD group at 72.67% (P\u0026lt;0.05). The LD group also outperformed the MD and HD groups in final body weight, weight gain rate, specific growth rate, and feed conversion ratio (P\u0026lt;0.05) (Table 1). These results suggest that breeding density significantly influences the growth performance and survival rate of triploid rainbow trout fry.\u003c/p\u003e\n\u003cp\u003e2. Effects of Different Stocking Densities on Histological Structures of Liver, Gill and Intestine Tissues in Triploid Rainbow Trout (Oncorhynchus mykiss) Juveniles\u003c/p\u003e\n\u003cp\u003eThe histological structure of the gill tissue in triploid rainbow trout fry was affected by density stress (Figure 3). The LD group showed thickening of the gill filaments and proliferation of columnar cells, with no infiltration of inflammatory cells. In contrast, the MD and HD groups exhibited thickening of gill filaments, proliferation of columnar cells, accompanied by the presence of small vacuoles and inflammatory cell infiltration, as well as separation of the gill epithelium. Liver histology revealed that the liver cells in the LD group were arranged neatly, with dilated hepatic sinusoids and mild steatosis. In the MD and HD groups, fatty degeneration and hepatic sinusoidal dilation were more pronounced, with occasional lymphocyte infiltration. There was no significant difference in the thickness of the intestinal muscle layer among the groups. These findings indicate that density stress significantly impacts the histological characteristics of the gills and liver in triploid rainbow trout fry, while the intestinal muscle layer thickness remains unaffected.\u003c/p\u003e\n\u003cp\u003e3. Significant Changes in Gut and Water Microbiota Diversity in Triploid Rainbow Trout Under Varying Aquaculture Densities\u003c/p\u003e\n\u003cp\u003eGut and aquatic microbiota diversity in triploid rainbow trout was analyzed under different stocking densities using 16S rRNA gene sequencing. In the gut microbiota, the MD group had the highest number of OTUs (10,723), followed by the LD group (10,407), while the HD group had the lowest number (9,616), suggesting that aquaculture density impacts gut microbiota diversity. For the aquatic microbiota, the HD group had the highest number of OTUs (4,341), followed by the MD group (4,057), and the LD group had the lowest (2,987) (Figure 6). Diversity indices for gut microbiota alpha (OTU count, Chao1, ACE index, Shannon, and Simpson index) showed differences among the groups (Figure 4). The alpha diversity index for aquatic microbiota exhibited a similar trend, further indicating that aquaculture density affects both the gut and aquatic microbiota diversity in triploid rainbow trout.\u003c/p\u003e\n\u003cp\u003eTo better understand the distribution and similarity of gut and aquatic microbiota in triploid rainbow trout at different breeding densities, principal component analysis (PCA) was conducted (Figure 5). The PCA of gut microbiota revealed that the explanatory power of axis 1 was 14.41%, and axis 2 explained 75.54%. For aquatic microbiota, axis 1 explained 23.94%, and axis 2 explained 36.72%. The PCA results showed both similarities and differences in the gut microbiota between groups. There were significant differences in the microbial community of water samples (P\u0026lt;0.05).\u003c/p\u003e\n\u003cp\u003e4. Alterations in Gut Microbiota Composition of Triploid Rainbow Trout Under Different Breeding Densities\u003c/p\u003e\n\u003cp\u003eAt the OTU level, the MD group identified the highest number of OTUs (10,723), followed by the LD group (10,407), and the HD group (9,616) (Figure 6). The composition and relative abundance of gut microbiota in triploid rainbow trout were significantly influenced by aquaculture density. The MD group exhibited 9,695 unique OTUs, with 330 OTUs more than the HD group, 140 more than the LD group, and 254 more than both. The HD group contained 8,516 unique OTUs, and had 193 OTUs more than the LD group. The LD group had 9,627 unique OTUs (Figure 6).\u003c/p\u003e\n\u003cp\u003e5. Shifts in the Genus Composition of Intestinal Microbiota in Triploid Rainbow Trout at Various Breeding Densities\u003c/p\u003e\n\u003cp\u003eThe analysis of gut microbiota at the genus level showed that the proportion of Pseudomonas (Figure 7). At the phylum level, the dominant phyla were Proteobacteria, Firmicutes, and Bacteroidetes (Figure 6). Although the phylum composition was similar across groups, the abundance of each phylum varied, indicating differences in the gut microbiota composition among the groups. At the genus level, the proportion of Pseudomonas in the gut microbiota of triploid rainbow trout was higher in the MD and HD groups than in the LD group (Figure 7, Figure 1). Notably, the proportion of Pseudomonas in the HD group was twice that of the LD group. Studies suggest that Pseudomonas can cause intestinal inflammation in fish. In the water samples, the proportion of Pseudomonas was higher in the LD and MD groups than in the HD group, which was the opposite of the gut microbiota composition. However, no adverse effects on growth performance or survival rate were observed in the LD and MD groups (Figure 7). The survival rate and body weight of the LD group were significantly higher than those of the HD group (P\u0026lt;0.05), suggesting that the microbial composition in the LD group was more stable.\u003c/p\u003e\n\u003cp\u003e6. Network Analysis of Co-Occurrence of Gut and Water Microbiota Under Different Aquaculture Densities\u003c/p\u003e\n\u003cp\u003eTo further explore the interrelationships between gut and water microbiota at different aquaculture densities, co-occurrence network analysis was performed on the distribution of core gut microbiota (Table 4). The coefficients of the three modular co-occurrence networks were all greater than 0.2, indicating a significant modular structure. Network indicators, such as the number of nodes and edges, and the proportion of microbial communities, varied across the three breeding density methods. The network displayed a high modularity coefficient for the LD and MD groups (LD = 0.52, MD = 0.41), while the HD group had a lower coefficient (0.28). This suggests that the interactions between gut microbiota in the LD and MD groups were more complex than in the HD group. Further analysis revealed that the top three phyla\u0026mdash;Proteobacteria, Firmicutes, and Bacteroidetes\u0026mdash;exhibited a higher proportion of interactions with other taxa in the LD and MD groups, indicating that Proteobacteria plays a crucial role in the network structure at these densities (Figure 8). The network analysis aligned with growth data, which showed that the LD and MD groups had higher survival rates, and the LD group exhibited the most connections with the MD group. This indicates that the gut and aquatic microbiota environments of the LD and MD groups are more stable, which correlates with the higher survival rate observed in these groups.\u003c/p\u003e\n\u003cp\u003eFinally, co-occurrence network analysis of the water microbiota revealed that the modular coefficients for the three groups were greater than 0.4, indicating significant modular structures. The LD and MD groups showed high modularity coefficients (LD = 0.62, MD = 0.42), while the HD group had a slightly lower coefficient (0.49). This suggests that the interactions between gut and water microbiota in the LD and HD groups were more complex compared to the MD group (Figure 8).\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003e\u003cem\u003e1.\u0026nbsp;\u003c/em\u003e\u003cem\u003eImpact of Density Stress on Growth Indicators in Triploid Rainbow Trout\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eBreeding density is a key factor that influences the growth, survival, and yield of artificially farmed fish(Dediu et al., 2021; Ezhilmathi et al., 2022; G\u0026ouml;relşahin et al., 2018; Li et al., 2024). Research indicates that increasing breeding density inhibits the growth performance of fish, with the negative effects of high density becoming more pronounced\u0026nbsp;(Jia et al., 2022). In this experiment, from the third sampling onward, the growth performance of the low-density (LD) group was significantly better than that of the medium-density (MD) and high-density (HD) groups(Figure 2), a trend that persisted throughout the study\u0026nbsp;(Diao et al., 2023; Refaey et al., 2018). Weight and body length increased with breeding time, but significantly decreased with increasing density (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05). Different breeding densities had a significant effect on the final body weight, weight gain rate, specific growth rate, and feed conversion ratio of triploid rainbow trout fry (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05). These findings align with previous research showing that under appropriate density conditions, the specific growth rate (SGR) of fish significantly improves\u0026nbsp;(Hassan et al., n.d.; Zaki et al., 2020). Initially, there was no significant difference between the groups, but after 15 days, the weight of the MD group was significantly higher than that of the HD and LD groups (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05), likely due to the stress caused by high density affecting appetite\u0026nbsp;(Tian et al., 2025). By day 30, there was a significant difference in body weight between the LD group and the MD and HD groups (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05), possibly due to increased competition for space, dissolved oxygen, and food in high-density conditions, resulting in higher energy expenditure and lower growth rates\u0026nbsp;(Nong et al., 2024). These results are consistent with the findings of other scholars\u0026nbsp;(Dewi et al., 2020; Puspaningsih et al., 2021; Syah et al., 2020).\u003c/p\u003e\n\u003cp\u003eThis study demonstrated that density stress significantly affected the histology of gills and liver in triploid rainbow trout fry. The LD group showed gill filament thickening and columnar cell proliferation without inflammation, while MD/HD groups exhibited gill epithelial separation, vacuolation, inflammatory infiltration, and severe hepatic steatosis(Zhao et al., 2024). Intestinal muscle layer was unaffected, providing a basis for optimizing stocking density.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.\u0026nbsp;\u003c/em\u003e\u003cem\u003eEffect of Density Stress on the Characteristics of Gut and Aquatic Microbial Communities in Triploid Rainbow Trout\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn aquaculture, the impact of stocking density on water quality and gut microbiota is critical, as it directly affects the health of aquatic animals\u0026nbsp;(Clols-Fuentes et al., 2023; Xie et al., 2025). This study analyzed gut and water samples from triploid rainbow trout fry under different densities using OTUs. A total of 27,838 OTUs were identified in the gut microbiota \u003cstrong\u003e(Figure 6)\u003c/strong\u003e. The HD, MD, and LD groups had 8,516, 9,695, and 9,627 unique OTUs, respectively, indicating that the gut microbiota in the HD group contained fewer OTUs compared to the MD and LD groups. The water samples contained a total of 8,955 OTUs, with 3,462, 3,362, and 2,131 unique OTUs found in the HD, MD, and LD groups, respectively. As aquaculture density increased, the number of OTUs in the water gradually increased\u0026nbsp;(Song et al., 2024). Additionally, with higher breeding density, the Alpha diversity index of both the aquatic and gut microbiota of triploid rainbow trout fry showed an upward trend. Notably, the Chao, ACE, and Shannon indices in the MD and HD intestinal groups were higher, reflecting greater species richness compared to the LD group(Figure 4)\u0026nbsp;(Xie et al., 2025). The Simpson index for the HD gut microbiota was high, with differences observed when compared to the other groups\u0026nbsp;(Song et al., 2024; Yuan et al., 2023). The dominant microbial community showed an increase in the proportion of Proteobacteria with rising density. The relative abundance of Proteobacteria significantly increased in high-density aquaculture, suggesting an elevated risk of disease and decreased stability in the gut microbiota\u0026nbsp;(Chang et al., 2023; Du et al., 2024; Ma et al., 2019; Zhang et al., 2019; Zoccarato et al., 1994). The phyla Firmicutes and Bacteroidetes were most abundant in the LD group, followed by the HD group, and least abundant in the MD group. The trends for Actinobacteria and Proteobacteria were opposite, with their populations declining as density increased. These changes indicate significant differences in the gut microbiota of triploid rainbow trout under different stocking densities\u0026nbsp;(Domingo-Bret\u0026oacute;n et al., 2025; Zhang et al., 2025).\u003c/p\u003e\n\u003cp\u003ePrincipal component analysis (PCA) was used to investigate the effects of different stocking densities on the gut and aquatic microbiota of triploid rainbow trout fry(Figure 5).The results showed that the composition and structure of gut microbiota were similar across all groups, with significant ecological niche overlap and stable competitive relationships\u0026nbsp;(Wong et al., 2013). However, significant differences (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05) were observed in the microbial community of water samples between the LD, HD, and MD groups, indicating that water microbiota are more sensitive to density changes. The stability of gut microbiota may be influenced by the host\u0026rsquo;s physiological status and immune system regulation\u0026nbsp;(Bao et al., 2023; Irwin et al., 1999; Pease et al., 2023), while the aquatic microbiota appears to be more susceptible to density stress. In both positive and negative ion modes, the LD group water samples displayed distinct distributions, possibly indicating the presence of specific microbial communities or metabolites. In contrast, the water samples in the HD group were more similar in both ion modes, while the MD group showed a more scattered distribution, primarily clustered in the positive ion mode, suggesting higher microbial diversity in the MD group water samples, likely reflecting differences in microbial metabolic activity under density stress.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.\u0026nbsp;\u003c/em\u003e\u003cem\u003eNetwork Analysis of Gut and Water Microbial Community Composition at Different Stocking Densities\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eBased on growth data and survival rates, breeding density likely contributes to the increased proportion of Pseudomonas species. Pseudomonas, an opportunistic pathogen, can cause intestinal infections in fish, leading to disease and mortality\u0026nbsp;(Barros et al., 2024; Wheatley et al., 2022). Overgrowth of Pseudomonas can disrupt the balance of gut microbiota in fish, impairing their digestion and absorption functions(Figure 7), which ultimately affects their intestinal health and growth performance\u0026nbsp;(Bordignon et al., 2020). Moreover, studies suggest that changes in gut microbiota in farmed rainbow trout depend on the degree of infection\u0026nbsp;(Wong and Rawls, 2012), which may be linked to inflammatory enteritis in triploid rainbow trout fry. Research has shown that Pseudomonas is more abundant in the intestines of IBD patients and plays a role in disrupting microbial communities, which can influence disease progression\u0026nbsp;(Dehler et al., 2017; Li et al., 2017). Thus, excessive proliferation of Pseudomonas may exacerbate intestinal inflammation, impair intestinal barrier function, and ultimately affect the intestinal health of triploid rainbow trout fry\u0026nbsp;(Roeselers et al., 2011).\u003c/p\u003e\n\u003cp\u003eThe high similarity in species composition between the LD and HD groups may be attributed to density stress, offering insights into the ecological adaptability and evolution of microbial communities. The LD group exhibited a higher total number of nodes and modularity coefficient within the aquatic microbial community, suggesting a more complex interaction network(Figure 8)\u0026nbsp;(Wang et al., 2024). These results emphasize that breeding density significantly impacts the intestinal health of triploid rainbow trout fry, with the LD and MD groups showing superior stability in their gut microbiota\u003cstrong\u003e(Figure 8)\u003c/strong\u003e.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn summary, density stress has a profound impact on the intestinal health of triploid rainbow trout fry. According to the data presented in this study, low-density farming conditions are more suitable for breeding triploid rainbow trout fry in high-altitude environments. This study underscores the importance of balancing microbial communities in aquaculture management to ensure the health and growth performance of triploid rainbow trout. Future research could further investigate the specific mechanisms by which Pseudomonas influences the intestinal health of triploid rainbow trout, providing more scientific support for aquaculture practices. Through a comprehensive analysis of growth data and survival rates, this study offers new perspectives for understanding the complexity of microbiota-host interactions and paves the way for future research and applications. This work highlights the critical significance of considering the impact of aquaculture density on microbial stability and host health, offering a solid scientific foundation for optimizing aquaculture management strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch5\u003e\n \u003cp\u003eThe Tibetan Autonomous Region Academy of Agricultural and Animal Sciences, Institute of Aquatic Sciences, Ethical Approval for Experimental Animals Number (No): 2025002 The paper \"Impact of Different Aquaculture Densities on the Growth Performance and Intestinal Health of Triploid Rainbow Trout (Oncorhynchus mykiss) Fry in High-Altitude Environments\" authored by Wang Wanliang and others from the Institute of Aquatic Sciences, Tibetan Autonomous Region Academy of Agricultural and Animal Sciences. After review by the Ethical Committee for Experimental Animals of the Institute of Aquatic Sciences, Tibetan Autonomous Region Academy of Agricultural and Animal Sciences, it is deemed that the design and implementation of this paper are scientific and feasible, with appropriate measures taken to reduce animal suffering, injury, and mortality. All processes comply with the ethical requirements for experimental animals. The Ethical Committee for Aquatic Sciences Research of the Tibetan Autonomous Region Academy of Agricultural and Animal Sciences will regulate aspects such as research protocols and informed consent to ensure the ethical and rational use of experimental animals.\u003c/p\u003e\n \u003cp\u003eEthical Committee for Aquatic Sciences Research Tibetan Autonomous Region Academy of Agricultural and Animal Sciences\u003c/p\u003e\n\u003c/h5\u003e\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest, financial or otherwise.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData will be made available on request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Aquatic Science Research Institute of the Tibet Autonomous Region Academy of Agriculture and Animal Husbandry.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e-Zhang Yaoqiong: Conceptualization, Methodology, Writing – Original Draft, Investigation, Data Curation, Writing – Review \u0026amp; Editing, Software, Formal Analysis, Visualization.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Wu Mengyu: Investigation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Wang Zhuangzhuang: Review \u0026amp; Editing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Zhou Jianshe: Funding Acquisition.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e-Zhang ning：Supervise\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- Wang Wanliang (Corresponding author): Supervision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe experiments in this study were conducted in strict adherence to the guidelines outlined in Institute of Fisheries Science, Xizang Academy of Agriculture andAnimal Husbandry Sciences, Ethical Approval for Experimental Animals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProject Support\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis project is supported by the National System for the Technology of Characteristic Freshwater Fish Industry (Project No. CARS-46).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBao, S., Zhuo, L., Qi, D., Tian, H., Wang, D., Zhu, B., Meng, Y., Ma, R., 2023. 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Aquaculture, Cutting Edge Science in Aquaculture 2015 467, 149\u0026ndash;157. https://doi.org/10.1016/j.aquaculture.2016.07.017\u003c/li\u003e\n \u003cli\u003eDewi, R.R.S.P.S., Krettiawan, H., Anggraeni, F., Kusnendar, E., Kusdiarti, 2020. Productivity and profitability of rice-freshwater prawn culture in different stocking density of prawn. IOP Conf. Ser.: Earth Environ. Sci. 521, 012021. https://doi.org/10.1088/1755-1315/521/1/012021\u003c/li\u003e\n \u003cli\u003eDiao, W., Jia, R., Hou, Y., Dong, Y., Li, B., Zhu, J., 2023. Effects of Stocking Density on the Growth Performance, Physiological Parameters, Antioxidant Status and Lipid Metabolism of Pelteobagrus fulvidraco in the Integrated Rice-Fish Farming System. 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Aquaculture (in Chinese).39: 13-17. https://doi.org/10.3969/j.issn.1004-2091.2018.01.003.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Stocking density, Oncorhynchus mykiss, growth indicators, water sample, intestinal microbiota","lastPublishedDoi":"10.21203/rs.3.rs-6937259/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6937259/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e[\u003cstrong\u003eIntroduction\u003c/strong\u003e] To investigate the optimal stocking density for triploid rainbow trout seedlings in high-altitude environments, this study examined three different stocking densities:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e[\u003cstrong\u003eMethod\u003c/strong\u003e] Low-Density group (LD) with 100 fish per barrel, Medium-Density group (MD) with 200 fish per barrel, and High-Density group (HD) with 300 fish per barrel, over a 60-day cultivation period.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e[\u003cstrong\u003eResult\u003c/strong\u003e] The results revealed that the final body weight (Wt) and specific growth rate (SGR) of the LD group were significantly higher than those of the HD group (\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05). Additionally, the survival rate of the LD group was significantly greater than that of the HD group (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).\u003cem\u003e \u003c/em\u003eVariations are observed in the α-diversity of both water and gut microbial communities at different stocking densities. PCA analysis indicated significant differences \u003cem\u003e(P \u0026lt; 0.05) \u003c/em\u003ein both the water and gut microbial communities among the different aquaculture densities. The dominant phyla in both aquatic and intestinal microbiomes were Proteobacteria, Firmicutes, and Bacteroidetes. Notably, the proportion of Pseudomonas genera was significantly higher in the HD and MD groups compared to the LD group. Co-occurrence network analysis revealed a higher average degree in the LD and MD groups across all densities, suggesting that the microbiota environments of both the intestinal and aquatic ecosystems in these groups were more stable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e[\u003cstrong\u003eConclusion\u003c/strong\u003e] In conclusion, low-density aquaculture is more suitable for plateau environments. These findings provide valuable insights for the ecological, efficient, and healthy aquaculture of triploid rainbow trout in high-altitude regions.\u003c/p\u003e","manuscriptTitle":"Impact of Different Aquaculture Densities on the Growth Performance and Intestinal Health of Triploid Rainbow Trout (Oncorhynchus mykiss) Fry in High-Altitude Environments","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-03 06:30:02","doi":"10.21203/rs.3.rs-6937259/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9d54a4c4-313d-4d99-bb96-4732fdfe22fc","owner":[],"postedDate":"July 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-23T02:23:08+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-03 06:30:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6937259","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6937259","identity":"rs-6937259","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}