Microbial drivers of high aquaculture productivity: Decoupling of taxonomic and functional profiles enables efficient nitrogen cycling in greenhouse shrimp farming

Microbial drivers of high aquaculture productivity: Decoupling of taxonomic and functional profiles enables efficient nitrogen cycling in greenhouse shrimp farming | 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 Microbial drivers of high aquaculture productivity: Decoupling of taxonomic and functional profiles enables efficient nitrogen cycling in greenhouse shrimp farming Hongye Shen, Liangxiao Ma, Jinshan Li, Yongmei Hu, Nan Peng, Shumiao Zhao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9114727/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 9 You are reading this latest preprint version Abstract As a global cornerstone of animal protein, farmed shrimp production in China has been revolutionized by the Greenhouse Shrimp Farming Model (GSFM). However, the microbial mechanisms underpinning the productivity and biosafety of this intensive paradigm remain poorly understood. Through a nationwide metagenomic survey of 90 standardized aquaculture units across a 15-degree latitudinal gradient, we deciphered the functional architecture of the GSFM microbiome. Our results reveal a striking functional convergence despite geographic taxonomic variation: while microbial compositions shifted across regions, the core metabolic pathways—particularly those governing nutrient cycling—remained conserved. High-productivity systems, notably in southern provinces (Guangxi and Guangdong), were characterized by a specialized microbial repertoire that optimized water chemistry through efficient nitrogen processing. We identified a sophisticated spatial division of labor, where the gut microbiota acts as a specialized nitrite detoxification unit, while the water column microbiota serves as the primary engine for ammonia assimilation. Furthermore, we characterized a diverse antibiotic resistome and utilized host-tracking to evaluate the ecological costs of intensification. These findings shift the focus from microbial identity to functional niche partitioning, providing a mechanistic blueprint for the sustainable intensification of aquaculture through targeted microbiome management. Greenhouse shrimp farming model Litopenaeus vannamei microbial community succession antibiotic-resistance genes nitrogen cycle Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Highlights λ Characterized the microbial diversity within GSFM and highlighted the multifaceted contributions and potential value of these communities to the overall aquaculture system. λ Mapped the distribution of antibiotic-resistance genes in GSFM, identifying aminoglycoside, macrolide, and tetracycline resistance genes as the most prevalent, primarily associated with Proteobacteria, Bacteroidetes, and Actinobacteria. λ Annotated nitrogen-cycle gene to show that gut microbiota are enriched in nitrite-metabolizing genes, while water microbiota possess great ammonium-metabolizing capacity-highlighting a complementary metabolic division of labor that underpins GSFM’s adaptation to high nitrogen loads. λ The division of labour between aquatic and intestinal microbiota in nitrogen metabolism may be the key to promoting high GSFM production. Introduction Global aquaculture, expanding at an annual rate of 6.7%, has become a cornerstone of animal protein production to meet escalating food security demands 1 , 2 . Within this sector, the Greenhouse Shrimp Farming Model (GSFM) has emerged as a high-performance intensive system 3 . However, as an emerging paradigm, its sustainable development is hindered by a lack of cohesive ecological understanding. Current research often treats GSFM as a collection of isolated, descriptive cases, failing to explain how this model maintains consistent high productivity across diverse geographic scales or how it manages the inherent trade-offs between intensive growth and biosafety risks 3 , 4 . The Greenhouse Shrimp Farming Model (GSFM), originating in Jiangsu Province, China, has rapidly expanded nationwide as an intensive aquaculture system for Litopenaeus vannamei . Characterized by high productivity and environmental control, this model achieved a 450,000-ton output by 2023, reflecting a 22% annual growth rate 5 . However, as the GSFM is adopted across diverse climatic zones, it remains unclear whether its performance is sustained by a universal microbial mechanism or if regional environmental filters (e.g., source water chemistry, temperature) override management-driven microbial assembly 6 , 7 , 8 , 9 . A nationwide, cross-regional survey is therefore essential as a "natural experiment" to decouple these factors 10 . Currently, research predominantly focuses on isolated cultivation zones, neglecting systematic comparisons across geographically distinct systems 11 , 12 . "Microbial communities serve as the primary functional engines in aquaculture, regulating productivity through complex nutrient cycling and host-environment interactions. In the GSFM, despite continuous high-intensity feeding, the ecosystem maintains a remarkable metabolic balance, driven by microbiomes that efficiently decompose residual feed and fecal matter. Within this system, the gut microbiota aids in nutrient breakdown and host growth, while a diverse microbial community provides a critical biotic barrier against pathogens, thereby enhancing host immunity and survival 13 , 14 , 15 . The permeable nature of the aquatic medium further facilitates a dynamic exchange, where waterborne microbes directly shape the intestinal flora through filter-feeding—exemplified by L. vannamei ingesting bioflocs equivalent to 5% of its body weight dail 16 , 17 , 18 , 19 . Poor gut health can significantly reduce aquaculture production. However, this high-efficiency resource cycling under hyper-intensive conditions does not come without ecological trade-offs 20 . The same selective pressures that optimize metabolic performance also drive the accumulation and persistence of the antibiotic resistome genes(ARGs). Consequently, ARGs emerge as a critical indicator of system stability and ecological cost, representing the biosafety threshold that defines the limits of sustainable intensification in these managed ecosystems. Metagenomics-based approaches have been successfully used to characterize the composition and function of microorganisms in various ecological environments 21 , 22 , 23 . While substantial research has focused on vertebrate gut microbiomes (e.g., humans), the anatomical and physiological distinctions of invertebrate digestive systems (e.g., crustaceans) may impose unique selective pressures on microbial community assembly 24 , 25 . To address these limitations, we propose a conceptual framework centered on functional niche partitioning and metabolic resilience. We hypothesize that the GSFM’s success is underpinned by functional convergence—a process wherein standardized management pressures across regions select for distinct microbial assemblages that perform identical ecological roles, particularly in high-efficiency nitrogen detoxification. To test this hypothesis, we employed a nationwide cross-regional sampling strategy spanning four major provinces(Jiangsu-JS, Guangdong-GD, Fujian-FJ, Guangxi-GX). This design serves as a 'natural experiment' to decouple the influences of broad-scale environmental filtering from local, management-driven selective pressures. Crucially, we posit that these same selective pressures do not only optimize nutrient cycling but also shape the microbial ARGs. In this context, ARGs serve as a critical indicator of the system's ecological cost and biosafety threshold, reflecting the inherent trade-offs between metabolic performance and long-term stability in such hyper-intensive ecosystems. This study systematically investigated 90 standardized aquaculture units from 18 entities across four major provinces. During the peak harvest period, we collected water and intestinal samples for metagenomic sequencing to characterize multi-kingdom microbiota. By integrating water quality parameters and host physiological indices, we aimed to: (1) quantify the biogeographic variation in microbial functional potential—specifically nutrient cycling and nitrogen transformation genes—to establish the linkage between functional convergence and regional environmental efficiency; (2) elucidate the compartment-specific (water column vs. gut) functional specialization in nitrogen metabolism, resolving the key microbial drivers that enable efficient nutrient recycling and waste reduction; and (3) assess the environmental biosafety risks by characterizing the diversity, abundance, and host associations of the antibiotic resistome within these intensive production systems. Collectively, this work establishes a pioneer functional database for the GSFM, providing a mechanistic foundation for optimizing management strategies and advancing sustainable biosecurity policies. Results Geographic heterogeneity in environmental parameters and shrimp growth performance Growth performance of Litopenaeus vannameivaried significantly among provinces, with individuals from Guangxi (GX) exhibiting superior outcomes in both body length and weight compared to those from Guangdong (GD), Fujian (FJ), and Jiangsu (JS) (p < 0.05, Fig. 1 A, B). To decipher the environmental drivers behind this disparity, we profiled a suite of physicochemical parameters. Principal component analysis (PCA) and permutational multivariate analysis of variance (PERMANOVA; F = 3.98, p = 0.002) confirmed significant biogeographic differentiation in water quality (Fig. 2 A). The first three PCA axes cumulatively explained 75.6% of the total variance. PC1 (38.5%) was predominantly loaded by nutrients (NH₄⁺-N, NO₃⁻-N) and chemical oxygen demand (COD), while PC2 (21.9%) correlated strongly with dissolved oxygen (DO), NO₂⁻-N, and salinity (Fig. 2 C).Notably, water bodies in GX were characterized by the highest concentrations of total phosphorus (TP), phosphate (PO₄³⁻), ammonium (NH₄⁺-N), nitrate (NO₃⁻-N), and COD, yet the lowest concentration of nitrite (NO₂⁻-N) among all regions (p < 0.01, Fig. 1 ). A strong negative correlation was observed between nitrite concentration and the crustacean sclerotization index. Convergence of Microbial Community Structure Despite Geographic Dispersion We next assessed whether the differential shrimp growth performance across provinces (Fig. 1 A, B) was driven by underlying differences in microbial community structure. Alpha diversity analysis revealed no significant differences in the richness and diversity (Shannon index) of gut microbial communities across all four provinces. In water microbiota, overall diversity was also comparable; however, a significant reduction in the Shannon index was observed in GX compared to JS (p < 0.05, Fig. 3 A, B). Principal coordinates analysis (PCoA) based on Bray-Curtis dissimilarity further demonstrated this structural convergence. Gut microbiota from GD and GX clustered closely, while those from FJ and JS exhibited significantly higher within-group variation (Fig. 3 C). Water microbial communities from all provinces showed considerable overlap, indicating a shared core microbiota in GSFM systems (Fig. 3 D). However, a separation of GX water samples was observed along PC1 (Explanation 46.53%). Fitting of environmental vectors to the ordination revealed that this separation was significantly correlated with higher ammonium (NH₄⁺-N) and nitrate (NO 3 ⁻-N) concentrations (p < 0.05), directly linking the unique water chemistry of GX to its divergent microbial community structure. Functional Shifts in Core Microbiota across GSFM Provinces We characterized the microbial taxonomic composition to identify taxa potentially contributing to the differential nitrogen metabolism observed across provinces. Bacterial communities were dominated by genera with established roles in nutrient cycling: Streptomyces (7.2%, known for complex organic matter decomposition), Pseudomonas (5.2%, capable of denitrification and polyphosphate accumulation), and Shewanella (0.9%, involved in anaerobic respiration) were consistently present across all samples (Fig. 4 A, B).Archaeal communities were particularly relevant to nitrogen transformation, featuring ammonia-oxidizing Nitrosopumilus (3.70%) and methanogenic Methanosarcina (3.69%) and Methanobacterium (3.43%) (Fig. S1 A). The co-occurrence of these taxa suggests complete nitrogen cycling pathways within GSFM systems.Eukaryotic annotations were limited, with most sequences belonging to uncharacterized protists. Some sequences received database annotations matching human pathogens (e.g., Plasmodium -like), which likely represent database misannotations of uncharacterized aquatic microeukaryotes rather than actual pathogens (Fig. 4 C, D). Viral communities showed no consistently dominant genera across provinces (Fig. S1 C), indicating high variability in viral dynamics. Crucially, the abundance of key nitrogen-cycling taxa differed significantly among provinces. GX samples showed decreased abundance of Nitrosopumilus , Pseudomonas and other ammonia-oxidizing microorganisms, consistent with their superior nitrite conversion capacity observed in water chemistry profiles. Functional Gene Profiling and Resistome Characteristics in GSFM Microbiota To delineate the functional potential of the GSFM microbiota, we annotated metagenomic data against KEGG, CAZy, CARD, and BacMet databases. Global functional profiling revealed that metabolic pathways, particularly those for amino acid, carbohydrate, and energy metabolism, constituted the most abundant gene categories (Fig. S2 A). While functional profiles between gut and water samples showed broad similarity (Fig. S2 B), a core set of 20 high-abundance genes (e.g., K07497, K03088, K12845), predominantly involved in core cellular processes, accounted for over 51% of the total annotated gene abundance. Carbohydrate-Active Enzymes (CAZy), especially from the GT, GH, and CBM families, were highly represented (Fig. 5 A, B). Notably, significant geographic variation was evident. Jiangsu (JS) samples exhibited the most distinct pattern, characterized by higher functional gene abundance in water and lower abundance in gut microbiota compared to other provinces. In contrast, the functional profiles of Guangdong (GD) and Guangxi (GX) samples clustered together, suggesting shared functional traits. Analysis of the antibiotic resistome identified a total of 34 antibiotic resistance gene (ARG) types. Aminoglycoside (AMG), macrolide (MAC), and tetracycline (TET) resistance genes were dominant, collectively representing 87% of the total ARG abundance. Host prediction indicated that Actinobacteria were the primary carriers of AMG genes, Bacteroidetes for TET, while MAC genes were associated with both Proteobacteria and Bacteroidetes. Metal resistance genes, as annotated by BacMet, were more diverse, with over 22 types identified, primarily conferring resistance to nickel, copper, and molybdenum. In contrast, only two herbicide resistance genes (against Ticlosan and Plumbagin) were detected. These results demonstrate that GSFM systems harbor a considerable diversity of antibiotic and metal resistance genes, while herbicide resistance is minimal. Geographic and Compartment-Specific Patterns in Nutrient Cycling Genes The functional potential for material cycling was assessed through the abundance of key genes involved in carbon, nitrogen, and sulfur metabolism. Carbon degradation capabilities varied geographically (Fig. 6 A). Samples from Guangxi (GX) exhibited the highest relative abundance of genes for hemicellulose, cellulose, pectin, and aromatic compound degradation. In contrast, genes for starch degradation (e.g., alpha-amylase, glucoamylase) were most abundant in Fujian (FJ) samples. Nitrogen cycling genes were detected across all sites (Fig. 6 B). Genes encoding enzymes for the complete nitrogen cycle—including nitrogen fixation ( nifHDK ), nitrification ( amoABC , hao ), denitrification ( narAG , nirKS , norBC , nosZ ), and assimilatory pathways ( nasA , nirA , gdh , ureC )—showed distinct abundance variations among provinces. Guangdong (GD) samples showed higher relative abundances of genes for assimilatory nitrate reduction ( nasA , nirA ), nitrogen fixation ( nifH ), and organic nitrogen metabolism ( gdh , glsA , ureAC ). Sulfur cycling genes were primarily enriched in surface sediments (Fig. 56C). These included genes for sulfur oxidation ( soxABCXYZ ), sulfate reduction ( cysCDHJN , aprAB ), thiosulfate cleavage ( phsABC ), and sulfur reduction ( dsrAB ).A clear functional specialization was observed between the gut and water microbiota (Fig. S3). Gut microbial communities showed a higher relative abundance of genes involved in assimilatory nitrate reduction. Conversely, water-borne microbial communities were enriched in genes for ammonia assimilation. Linking Environmental Factors, Microbiota, and Shrimp Growth through Multivariate Modeling The relationships between environmental parameters, microbial communities, and shrimp growth were quantified using multivariate statistical models. Redundancy analysis (RDA) revealed significant structuring of water microbial communities by province (Fig. 7 A). Vectors for physical factors (pH, dissolved oxygen, temperature) and chemical factors (TP, NH₄⁺, COD, NO₃⁻, PO₄³⁻, salinity) showed distinct clustering, indicating correlated patterns within each group (Fig. 7 B). Shrimp length and weight were positively correlated with the aggregate of chemical factors, particularly with TP, NO₃⁻, and PO₄³⁻.A Partial Least Squares Path Model (PLS-PM) was constructed to test the hypothesized causal pathways (Fig. 7 C). The model exhibited adequate goodness-of-fit (GoF = 0.695). The latent variable ‘Physical Factors’ (defined by temperature, pH, dissolved oxygen, and salinity) had a significant positive effect on ‘Microbial Community Metrics’ (p < 0.05), explaining 21.6% of its variance. The latent variable ‘Chemical Factors’ (defined by COD, NH₄⁺-N, NO₃⁻-N, TP, PO₄³⁻) had a stronger direct effect on ‘Shrimp Growth’ (p < 0.01) than ‘Physical Factors’ did. ‘Spatial Characteristics’ (province) was also a significant predictor of both ‘Chemical Factors’ and ‘Shrimp Growth’. Discussion Interplay between Microbial Functional Redundancy and Niche Specialization Drives GSFM Productivity Our large-scale metagenomic survey of China’s Greenhouse Shrimp Farming Model (GSFM) reveals a core ecological principle underpinning its high productivity and regional adaptability: a decoupling of microbial community structure from function, governed by the interplay between functional redundancy and niche specialization. Contrary to the expectation that superior shrimp growth in Guangxi (GX) would be driven by a unique microbial taxonomy, we observed remarkable structural convergence in both alpha and beta diversity across provinces (Fig. 3 ). This suggests that the GSFM ecosystem exerts strong functional selection, where functional redundancy ensures core processes are maintained despite taxonomic variation. However, the key to regional success lies in specialization: the quantitative enrichment of specific pathways—such as efficient denitrification and carbon degradation in GX (Fig. 6 A, B)—tailors microbial function to local environmental conditions and management practices. This functional model challenges the terrestrial-animal-centric view of the microbiome, where host genetics predominantly shapes a stable gut community. In GSFM, the gut microbiome is an open ecosystem, continuously inoculated and influenced by the water column. We discovered a sophisticated division of labor between these compartments: water microbes primarily act as ammonia assimilators, incorporating inorganic nitrogen into biomass, while gut microbes function as nitrite reducers, directly detoxifying the critical intermediate NO₂⁻ (Fig. S3). This niche partitioning optimizes system-level nitrogen metabolism, explaining the paradoxical combination of high nutrient loading and low nitrite toxicity observed in high-performing systems 26 , 27 . High Nutrient Retention and Cycling Efficiency Underpin High Shrimp Yield in GSFM Our results revealed pronounced geographic heterogeneity in water quality, with Guangxi (GX) systems exhibiting a distinct profile characterized by significantly elevated concentrations of total phosphorus, phosphate, ammonium, nitrate, and COD—collectively indicative of a high nutrient load—yet maintaining the lowest nitrite (NO₂⁻-N) levels (Fig. 1 C). This combination is paradoxical under classical aquaculture models, where high nutrient input typically leads to nitrite accumulation and toxicity 28 , 29 . Elevated nitrite concentrations reflect inhibited nitrification or enhanced denitrification, potentially leading to ecosystem imbalance 30 . In shrimp culture systems, NO₂⁻-N levels correlate directly with shrimp carapace hardness and vigor, and nitrite concentration is routinely used as an indicator of system health in commercial production 31 , 32 . We propose that the superior shrimp yield in GX is not a direct result of high nutrient concentrations per se, but rather a consequence of a more efficient microbial nutrient cycling pipeline that effectively converts feed-derived nutrients into shrimp biomass while minimizing the accumulation of toxic intermediates. This is supported by the metagenomic evidence showing a functional enrichment in GX for complete denitrification pathways (nirS, nirK) and versatile carbon degradation capabilities (Fig. 5 A, B). The low nitrite concentration, which strongly correlated with shrimp sclerotization and yield, is a key indicator of this efficient nitrogen transformation chain. Therefore, the water quality pattern in GX reflects a high-throughput, low-intermediate system where nutrients are rapidly assimilated into the productive food web (microbial flocs and shrimp) rather than accumulating as waste or toxins 33 , 34 . In contrast to natural ecosystems where elevated N and P signify eutrophication and degradation, in the managed GSFM system, these parameters—when coupled with the specific microbial functional profile observed in GX—are indicators of high system productivity and efficient nutrient retention. Structural Convergence Suggests a Functionally Robust Core Microbiome in GSFM The most striking feature of the GSFM microbiome is its structural convergence across vast geographic distances. Unlike the distinct functional gene profiles, both gut and water microbial communities showed negligible differences in alpha diversity and considerable overlap in beta diversity among the four provinces. This indicates that the GSFM environment, characterized by high biomass loading and consistent management practices, exerts strong selective pressures that shape a characteristic core microbiome regardless of location. This structural consistency suggests a high degree of functional redundancy, where different taxonomic members can perform similar ecosystem services, thereby ensuring system stability. Within this core community, we observed a co-occurrence of taxa with contrasting roles—including potential pathogens (e.g., Vibrio) and beneficial organisms (e.g., Bacillus, nitrite-detoxifying Shewanella). This is not indicative of dysfunction but rather a hallmark of a complex and mature ecosystem. The persistence of potential pathogens at low abundances may be controlled by the surrounding microbial community through resource competition and antagonism, a phenomenon known as the “lottery hypothesis” where the diverse microbiota fills all available ecological niches, leaving fewer opportunities for any single pathogen to erupt 35 , 36 . The presence of beneficial bacteriophages further contributes to this top-down control. Beyond the direct causative links between specific bacteria and disease, studies have shown that changes in gut bacterial diversity correlate with host health and gastrointestinal disorder incidence 37 . Intestinal bacteria contribute to host defenses by occupying limited adhesion sites (colonization resistance) 38 , producing antimicrobial peptides to antagonize pathogens 39 , and stimulating the host immune system to modulate tolerance to other microbes. A comprehensive survey of bacteria, eukaryotes, viruses, and archaea in the GSFM system revealed the functional diversity conferred by microbial richness. Both beneficial and harmful microbial taxa exhibited appreciable abundances in this complex system. Thus, the key to GSFM's success may not lie in a unique, region-specific microbial signature, but in the system's ability to reliably assemble a functionally versatile and resilient core microbiome. This structural foundation provides the stability upon which the more nuanced, quantitative differences in metabolic pathways can build to drive high productivity. The functional robustness provided by a diverse and convergent core microbiome highlights the importance of managing the 'whole-community' health. This suggests that instead of adding single-strain probiotics, management should aim to foster the assembly of a mature and resilient microbial consortium—potentially through biofloc technology or standardized inoculation during the early culture stages—to preemptively fill ecological niches and suppress pathogen eruptions through competitive exclusion. Functional Gene Geography Reveals Specialized Nutrient Cycling Adaptations Recent studies have emphasized the pivotal role of the gut microbiome in host health and disease 40 , 41 . However, most gut microbes are unculturable in the laboratory, limiting our understanding of their functions, including those related to nutrient metabolism and antibiotic resistance 22 , 26 , 27 . Our metagenomic analysis reveals that while microbial community structures are convergent, functional gene profiles exhibit striking geographic specialization (Fig. 5 ). This divergence underscores that the ecological success of GSFM is driven by quantitative shifts in core metabolic pathways rather than the presence or absence of specific taxa. The distinct functional landscape in Guangxi (GX)—characterized by enhanced genes for complex carbon degradation (e.g., hemicellulose, cellulose) and complete denitrification pathways (e.g., nirS, nosZ)—provides a mechanistic explanation for its superior performance. This genetic repertoire enables a more efficient conversion of feed-derived organic matter and a robust nitrogen-removal pipeline that minimizes the accumulation of toxic nitrite, a key constraint in intensive aquaculture. In contrast, the functional profile in Jiangsu (JS), though distinct, may represent a less optimized state for high-density production. Therefore, the functional gene composition, not merely the microbial taxonomy, serves as a predictive biomarker for the performance of a GSFM system. The composition of ARGs in both ecosystems and gut microbiota has been a subject of considerable interest 42 , 43 . despite the reported absence of antibiotic use, highlights the persistent environmental reservoir of resistance determinants in aquaculture systems. Rovira et al. found that discontinuation of tetracycline use on farms does not automatically reduce resistance levels 44 . Consequently, a substantial reservoir of resistance genes remains in the GSFM system. Critically, host-tracking analysis revealed that Proteobacteria, Bacteroidetes, and Actinobacteria constituted the primary hosts of ARGs, with Proteobacteria harboring most high-abundance resistance genes (TMP resistance genes almost exclusively), and Bacteroidetes serving as the main hosts for MAC, GLY, and TET resistance genes—a pattern also observed in the chicken gut microbiome. This finding signals a tangible risk for the horizontal transfer of these genes into potential pathogens, a crucial aspect for the biosafety assessment of GSFM. Our host-tracking analysis provides a diagnostic blueprint for biosecurity. By identifying Proteobacteria as the primary reservoirs of high-abundance ARGs, we provide evidence that biosecurity measures should target specific microbial lineages rather than applying broad-spectrum disinfectants, which often exacerbate resistance. Spatial Partitioning of Nitrogen Metabolism between Gut and Water Microbiota Enhances System Efficiency The biogeochemical nitrogen cycle is typically partitioned into six key processes: assimilation, ammonification, nitrification, denitrification, anaerobic ammonium oxidation (anammox), and nitrogen fixation (N₂ fixation) 45 , 46 . Our results reveal a sophisticated spatial partitioning of nitrogen metabolic labor between the shrimp gut and the rearing water, a key mechanism underpinning the efficiency of the GSFM system. Contrary to the traditional view of the culture environment as a homogeneous reactor, we found a clear functional specialization: the gut microbiota primarily functions as a nitrite transformation hotspot, while the water microbiota serves as an ammonia assimilation engine. This division of labor is critical for system stability. The shrimp gut, enriched with genes for nitrite oxidation (nxr) and both pathways of nitrite reduction (to nitrate via nirK/S; to ammonium via nrfAH), acts as a primary internal detoxification organ, directly mitigating the toxicity of nitrite absorbed from the water. This explains the strong negative correlation between gut nrfAHgene abundance and nitrite concentration observed in high-performing systems like GX. Conversely, the water column, enriched with genes for ammonia assimilation (e.g., glutamate synthase), efficiently converts toxic ammonium—derived from shrimp excretion and feed waste—into microbial biomass. This microbial protein can subsequently be consumed by shrimp, forming a productive loop. Nitrite oxidation, catalyzed by nitrite oxidoreductase (NXR), is the principal biochemical route converting nitrite to nitrate 45 , 47 . Nitrite reduction to ammonium serves both dissimilatory and assimilatory functions 48 . In most bacteria, dissimilatory nitrite reduction to ammonium is the periplasmic cytochrome c nitrite reductase (ccNIR) encoded by NrfAH 49 . This reaction is catalyzed by either a heme-containing cd₁ nitrite reductase (cd₁-NIR; encoded by nirS ) or a copper-containing nitrite reductase (Cu-NIR; encoded by nirK ) 50 , both of which are widespread in GSFM systems. The high productivity and low nitrite stress characteristic of GX systems can be attributed to the optimal performance of this coupled system. The efficient assimilation of ammonia in the water reduces the overall nitrogen load, while the robust nitrite transformation capacity within the shrimp gut provides a critical safety net, preventing the accumulation of this key toxin. Conclusion This nationwide metagenomic survey of China’s GSFM reveals that its exceptional productivity and regional adaptability are underpinned by functional niche partitioning within the microbiome, rather than geographic shifts in microbial taxonomy. We demonstrate that the superior system performance observed in high-performing regions, such as Guangxi, is characterized by a "high-nutrient, low-nitrite" water quality profile, sustained by a highly efficient and spatially organized microbial consortium. Key ecological mechanisms identified include: (1) a sophisticated spatial division of labor in nitrogen metabolism, wherein the shrimp gut microbiota functions as a specialized nitrite detoxification unit, while the water column microbiota acts as the primary engine for ammonia assimilation; (2) regional-scale functional gene specialization, enabling the localized optimization of nutrient cycling despite structural taxonomic convergence; and (3) the persistence of a diverse resistome, the ecological costs and biosecurity risks of which can now be more precisely evaluated through host-tracking analysis. In conclusion, the GSFM serves as a prime example of a managed ecosystem where productivity is maximized through microbially mediated resource cycling. Our findings shift the focus from microbial identity to microbial function, providing a mechanistic blueprint for the optimization of intensive aquaculture. This work suggests that future management should transition from broad-spectrum interventions to fostering the specific microbial processes identified herein—such as through targeted probiotic colonization to enhance in situ nitrite detoxification—thereby paving the way for more sustainable and high-yield aquaculture development. Materials and Methods Sample collection In this study, aquaculture water and shrimp intestinal content samples were collected from 90 independent greenhouse shrimp farming systems operated across four provinces in China, where greenhouse shrimp farming is most extensively practiced: Jiangsu (JS), Guangdong (GD), Guangxi (GX), and Fujian (FJ). "GSFM represents a distinctive intensive shrimp farming model in China. In our sampled small-shed management system, the stocking density was approximately 55,000 juveniles per shed, with seedstock sourced from industrial hatcheries. The feeding regimen utilized refined pellets with 43% protein content. Water was typically sourced from local groundwater with minimal exchange throughout the cycle; meanwhile, continuous aeration was maintained, and disinfectants were primarily applied during the early and late rearing phases. Detailed sampling maps and schematic of the GSFM pattern are shown in Fig. S4. The GSFM represents an emerging, intensive aquaculture system in China, characterized by high-density farming, controlled environmental conditions, and efficient resource utilization, aimed at achieving high yield and productivity. This model has been widely adopted in many coastal regions. Specifically, shrimp intestine and aquaculture water samples were collected from Jiangsu (n = 25), Guangdong (n = 25), Guangxi (n = 20), and Fujian (n = 20). The selected systems were randomly sampled from hundreds of independent greenhouses maintained by local shrimp farmers to ensure representative coverage. All sampled shrimp were healthy, active, and in good physiological condition at the time of collection, which occurred uniformly in September, shortly before harvest. Water samples were collected in sterile 1 L polypropylene containers, while shrimp were aseptically dissected on-site to obtain intestinal contents using sterilized instruments. All samples were immediately placed on ice and transported to the laboratory within 24 hours. Upon arrival, samples were either processed immediately or stored at − 80°C until further analysis to preserve DNA integrity. Sample collection and experimental procedures were conducted between April and May 2023 and were reviewed and approved by the Ethics Committee of Huazhong Agricultural University (Approval No. HZAUFI-2025-0075). Water Quality Analysis Following sample thawing, concentrations of total phosphorus (TP), total nitrogen (TN), and permanganate index (CODₘₙ) were measured using unfiltered water samples, whereas filtered samples were employed to determine the concentrations of nitrate nitrogen (NO₃⁻-N), nitrite nitrogen (NO₂⁻-N), ammonium nitrogen (NH₄⁺-N), and orthophosphate (PO₄³⁻-P). TP and TN were analyzed using the potassium persulfate digestion–ammonium molybdate spectrophotometric method (GB11893-89) and the alkaline potassium persulfate digestion–ultraviolet spectrophotometric method (GB11894-89), respectively. Soluble orthophosphate concentrations were measured spectrophotometrically according to the "Water and Wastewater Monitoring and Analysis Methods, Fourth Edition," and CODₘₙ was determined via titration (GB11892-89). For nitrogen species, NO₃⁻-N and NO₂⁻-N concentrations were assessed by ultraviolet spectrophotometry (HJ/T 346–2007) and standard spectrophotometry (GB/T 7493 − 1987), respectively, while NH₄⁺-N was quantified using Nessler's reagent spectrophotometric method (HJ535-2009). Chlorophyll-a levels were determined following the hot ethanol extraction method (SL88-2012). Metagenome DNA Extraction and Shotgun Sequencing The microbial genomic DNA samples were extracted according to the manufacturer’s Magnetic Soil And Stool DNA Kit (TINGGEN) and stored at − 80 ℃. To ensure the purity and quality of the extracted DNA, a NanoPhotometer and a Qubit 3.0 were used, respectively. The integrity of the DNA was assessed by agarose electrophoresis. To prepare the DNA for sequencing, 0.5 µg genomic DNA was randomly fragmented using Bioruptor Pico and then filtered with magnetic beads. The Adaptor was added and the DNA was repaired, followed by magnetic bead purification. PCR was performed to amplify and enrich the products. The double-stranded PCR library was then purified to unchain and loop to form a single-stranded circular DNA. Rolling ring amplification (RCA) technology was used to form the DNA nanosphere (DNB), which was loaded into the chip and fixed through a fully automatic sample loading system. Metagenomics Analysis Raw sequencing reads were processed to obtain quality-filtered reads for further analysis. First, sequencing adapters were removed from sequencing reads using Cutadapt (v1.2.1). Secondly, low-quality reads were trimmed using a sliding-window algorithm in Fastp 51 . Once quality-filtered reads were obtained, taxonomical classifications of metagenomics sequencing read from each sample were performed using Kraken2 52 , which included genomes from archaea, bacteria, viruses, fungi, protozoans, metazoans, and viridiplantae. Reads assigned to metazoans or viridiplantae were removed for downstream analysis. Megahit (v1.1.2) 53 assembled each sample using the meta-large presetted parameters. The generated contigs (longer than 300bp) were then pooled together and clustered using mmseqs2 (Steinegger and SöDing 2017) with “easy-linclust” mode, setting the sequence identity threshold to 0.95 and covering residues of the shorter contig to 90%. The lowest common ancestor taxonomy of the non-redundant contigs was obtained by aligning them against the NCBI-nt database by mmseqs2 54 with “taxonomy” mode, and contigs assigned to Viridiplantae or Metazoa were dropped in the following analysis. MetaGeneMark was used to predict the genes in the contigs. CDS sequences of all samples were clustered by mmseqs2 with “easy-cluster” mode, setting the protein sequence identity threshold to 0.90 and covering residues of the shorter contig to 90%. To assess the abundance of these genes, the high-quality reads from each sample were mapped onto the predicted gene sequences using salmon in the quasi-mapping-based mode with “--meta --minScoreFraction = 0.55”, and the copy per kilobase per million mapped reads (CPM) was used to normalize abundance values in metagenomes. The non-redundant genes' functionality was obtained by annotating using mmseqs with the “search” mode against the KEGG EggNOG and CAZy databases' protein databases, respectively. EggNOG and GO were obtained using EggNOG-mapper (v2) 55 . GO ontology was obtained using map2slim ( www.metacpan.org ). KO was obtained using KOBAS 56 . Statistical analysis Data manipulation and visualization were performed through the R meta package tidyverse (2.0.0) 57 . T-test, Kruskal-Wallis rank sum test, and Wilcoxon rank sum test were performed through functions ‘t.test’, ‘kruskal.test’, and ‘wilcox.test’ in package stats (4.2.1). Before analysis, the samples were rarefied to uniform depth based on the lowest sample sequence to eliminate the influence of different sequencing depths. Alpha diversity indices (Shannon, pielou’s eveness, observed species, and faith’s pd) were calculated using functions diversity in vegan (2.7). Beta diversity metrics (Jaccard dissimilarity, Bray-Curtis dissimilarity), as well as PCA, were conducted using the function ‘rda’ in vegan (2.7) 58 followed by an ADNOIS test to measure the changes related to sampling sites 59 . Heatmaps were plotted using the complexheatmap (2.16.0) and pheatmap (1.012) packages. A Partial Least Squares Path Model (PLS-PM) analysis was also implemented in R using the plspm package. The raup-Crick dissimilarity index was calculated using a custom function provided by Chase et al. All visualizations are done in R, mainly based on ggplot2 (3.6.1). Declarations CONFLICT OF INTEREST STATEMENT The authors declare that they have no competing interests. ETHICS STATEMENT This study has an ethical clearance number of HZAUFI-2025-0075. Author Contribution Hongye Shen : Writing—original draft; writing—review and editing; conceptualization; supervision. Liangxiao Ma : Writing—original draft; writing—review and editing; conceptualization. Jinshan Li : Writing—original draft. Yongmei Hu : Writing—original draft. Nan Peng : Writing—original draft. Shumiao Zhao: Conceptualization; project administration; writing—review and editing; supervision; funding acquisition. ACKNOWLEDGMENTS We thank the BaiChuan fellowship of the College of Life Science and Technology, Huazhong Agricultural University, for funding support. This study was financially supported by Data Availability We have uploaded the raw data. All the metagenomic sequencing raw data have been deposited in the NCBI Sequence Read Archive database under BioProject number PRJNA1256668. References Naylor RL, et al. Blue food demand across geographic and temporal scales. Nat Commun. 2021;12:5413. Tigchelaar M, et al. Compound climate risks threaten aquatic food system benefits. Nat Food. 2021;2:673–82. Guo H et al. Sucrose addition directionally enhances bacterial community convergence and network stability of the shrimp culture system. npj Biofilms Microbiomes 8, (2022). Nguyen TAT, Nguyen KAT, Jolly C. Is Super-Intensification the Solution to Shrimp Production and Export Sustainability? Sustainability. 2019;11:22. Fisheries Bureau of the Ministry of Agriculture. China Fisheries Statistical Yearbook 2013M. Beijing: China Agriculture; 2023. Akdeniz HB, Yalpir S, Inam S. Assessment of suitable shrimp farming site selection using geographical information system based Analytical Hierarchy Process in Turkey. Ocean Coast Manage. 2023;235:106468. Huang Y, Jiang KY, Liu M, Wang BJ, Wang L. Microbial community dynamics and nitrogen transformation associated with turbid-turned culture stage of Pacific white shrimp, Litopenaeus vannamei in small greenhouse farms. Aquaculture. 2024;593:741285. Chen BY, Huang PL, Hou YL, Lan HY, Huang CT, Nan FH. The economic feasibility of aquavoltaics in Taiwan- A case study of whiteleg shrimp culture. Aquaculture. 2024;581:740454. Barreto MK, Oliveira CTA, Henry-Silva GG. Greenhouse gas emission flux (CO, CH, NO) from marine shrimp (Litopenaeus vannamei) monoculture cultures in the Brazilian semi-arid region. Aquaculture. 2024;582:740536. Tong C, et al. Annual CO2 and CH4 fluxes in coastal earthen ponds with Litopenaeus vannamei in southeastern China. Aquaculture. 2021;545:737229. Kring NA, Fleckenstein LJ, Tierney TW, Fisk JC, Lawson BC, Ray AJ. The effects of stocking density and artifical substrate on production of pacific white shrimp and water quality dynamics in greenhouse-based biofloc systems. Aquacult Eng. 2023;101:102322. Holt CC, van der Giezen M, Daniels CL, Stentiford GD, Bass D. Spatial and temporal axes impact ecology of the gut microbiome in juvenile European lobster (Homarus gammarus). ISME J. 2020;14:531–43. Xiong JB, Dai WF, Zhu JY, Liu KS, Dong CM, Qiu QF. The Underlying Ecological Processes of Gut Microbiota Among Cohabitating Retarded, Overgrown and Normal Shrimp. Microb Ecol. 2017;73:988–99. Zokaeifar H, et al. Effects of Bacillus subtilis on the growth performance, digestive enzymes, immune gene expression and disease resistance of white shrimp. Fish Shellfish Immun. 2012;33:683–9. Perry WB, Lindsay E, Payne CJ, Brodie C, Kazlauskaite R. The role of the gut microbiome in sustainable teleost aquaculture. P Roy Soc B-Biol Sci. 2020;287:1926. Bernhardt JR, O'Connor MI. Aquatic biodiversity enhances multiple nutritional benefits to humans. Proc. Natl. Acad. Sci. U.S.A 118, 15 (2021). Stentiford GD, et al. New Paradigms to Help Solve the Global Aquaculture Disease Crisis. Plos Pathog. 2017;13(2):1006160. Stentiford GD, Bass D, Williams BAP. Ultimate opportunists-The emergent group Microsporidia. Plos Pathog. 2019;15:1007668. Trevathan-Tackett SM, et al. A horizon scan of priorities for coastal marine microbiome research. Nat Ecol Evol. 2019;3:1509–20. Copetti F, Gregoracci GB, Vadstein O, Schveitzer R. Management of biofloc concentrations as an ecological strategy for microbial control in intensive shrimp culture. Aquaculture. 2021;543:736969. Shen H, et al. Native synthetic microbial communities enhance zha-chili by boosting the fermentation capacity of indigenous microorganisms. iMetaOmics. 2025;2:2. Shen H, et al. Metagenome-assembled genome reveals species and functional composition of Jianghan chicken gut microbiota and isolation of Pediococcus acidilactic with probiotic properties. Microbiome. 2024;12:25. Zhou Q, et al. Intestinal microbiome-mediated resistance against vibriosis for Cynoglossus semilaevis. Microbiome. 2022;10:153. Petersen JM, Osvatic J, Microbiomes. Importance of Invertebrates in Understanding the Natural Variety of Animal-Microbe Interactions. Msystems. 2018;3:13. Kanehisa M. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 2000;28(1):27–30. Feng YQ, Wang YA, Zhu BL, Gao GF, Guo YM, Hu YF. Metagenome-assembled genomes and gene catalog from the chicken gut microbiome aid in deciphering antibiotic resistomes. Commun Biology. 2021;4:1305. Li CY, et al. Expanded catalogue of metagenome-assembled genomes reveals resistome characteristics and athletic performance-associated microbes in horse. Microbiome. 2023;11:7. Cao YH, et al. The multi-kingdom microbiome of the goat gastrointestinal tract. Microbiome. 2023;11:219. Killberg-Thoreson L, Mulholland MR, Heil CA, Sanderson MP, O'Neil JM, Bronk DA. Nitrogen uptake kinetics in field populations and cultured strains of Karenia brevis. Harmful Algae. 2014;38:73–85. Kieley CM, et al. Concentration of total microcystins associates with nitrate and nitrite, and may disrupt the nitrogen cycle, in warm-monomictic lakes of the southcentral United States. Harmful Algae. 2023;130:102542. Hou DW, Li HY, Wang S, Weng SP, He JG. Nitrite nitrogen stress disrupts the intestine bacterial community by altering host-community interactions in shrimp. Sci Total Environ. 2024;925:171536. Lin LT, et al. Integrated histological, physiological, and transcriptome analysis reveals the post-exposure recovery mechanism of nitrite in Litopenaeus vannamei. Ecotox Environ Safe. 2024;281:116673. Li ZJ, et al. Insight into aerobic phosphorus removal from wastewater in algal-bacterial aerobic granular sludge system. Bioresour Technol. 2022;352:127104. Wang ZK, et al. Effect of acetochlor on the symbiotic relationship between microalgae and bacteria. J Hazard Mater. 2024;463:132848. Campos-Pardos E, Uranga S, Picó A, Gómez AB, Gonzalo-Asensio J. Dependency on host vitamin B12 has shaped Complex evolution. Nat Commun. 2024;15:2161. Verster AJ, Borenstein E. Competitive lottery-based assembly of selected clades in the human gut microbiome. Microbiome. 2018;6:186. Donaldson GP, Lee SM, Mazmanian SK. Gut biogeography of the bacterial microbiota. Nat Rev Microbiol. 2016;14:20–32. Lawley TD, Walker AW. Intestinal colonization resistance. Immunology. 2013;138:1–11. Garcia-Gutierrez E, Mayer MJ, Cotter PD, Narbad A. Gut microbiota as a source of novel antimicrobials. Gut Microbes. 2019;10:1–21. Huang Y, Lv HJ, Song YC, Sun CJ, Zhang ZF, Chen SR. Community composition of cecal microbiota in commercial yellow broilers with high and low feed efficiencies. Poult Sci. 2021;100:100996. Wen CL, et al. The gut microbiota is largely independent of host genetics in regulating fat deposition in chickens. ISME J. 2019;13:1422–36. Imperial ICVJ, Ibana JA. Addressing the Antibiotic Resistance Problem with Probiotics: Reducing the Risk of Its Double-Edged Sword Effect. Front Microbiol. 2016;7:1983. Zhao J, et al. Intestinal toxicity and resistance gene threat assessment of multidrug-resistant Shigella: A novel biotype pollutant. Environ Pollut. 2023;316:120551. Rovira P, et al. Characterization of the Microbial Resistome in Conventional and Raised Without Antibiotics Beef and Dairy Production Systems. Front Microbiol. 2019;10:1980. Kuypers MMM, Marchant HK, Kartal B. The microbial nitrogen-cycling network. Nat Rev Microbiol. 2018;16:263–76. Zehr JP, Jenkins BD, Short SM, Steward GF. Nitrogenase gene diversity and microbial community structure: a cross-system comparison. Environ Microbiol. 2003;5:539–54. Fang FC. Antimicrobial reactive oxygen and nitrogen species: Concepts and controversies. Nat Rev Microbiol. 2004;2:820–32. Maia LB, Moura JJG. How Biology Handles Nitrite. Chem Rev. 2014;114:5273–357. Tikhonova TV, et al. Molecular and catalytic properties of a novel cytochrome nitrite reductase from nitrate-reducing haloalkaliphilic sulfur-oxidizing bacterium. Bba-Proteins Proteom. 2006;1764:715–23. Zumft WG. Cell biology and molecular basis of denitrification. Microbiol Mol Biol Rev. 1997;61:533–49. Chen SF, Zhou YQ, Chen YR, Gu J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics. 2018;34:884–90. Wood DE, Lu J, Langmead B. Improved metagenomic analysis with Kraken 2. Genome Biol. 2019;20:257. Li DH, Liu CM, Luo RB, Sadakane K, Lam TW. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct graph. Bioinformatics. 2015;31:1674–6. Steinegger M, Söding J. MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nat Biotechnol. 2017;35:1026–8. Cantalapiedra CP, Hernández-Plaza A, Letunic I, Bork P, Huerta-Cepas J. eggNOG-mapper v2: Functional Annotation, Orthology Assignments, and Domain Prediction at the Metagenomic Scale. Mol Biol Evol. 2021;38:5825–9. Bu DC, et al. KOBAS-i: intelligent prioritization and exploratory visualization of biological functions for gene enrichment analysis. Nucleic Acids Res. 2021;49:W317–25. Wickham H, et al. Welcome to the Tidyverse. J Open Source Softw. 2019;4:43. Faith DP, Minchin PR, Belbin L. Compositional dissimilarity as a robust measure of ecological distance. Vegetatio. 1987;69:57–68. Anderson MJ. A new method for non-parametric multivariate analysis of variance. Austral Ecol. 2001;26:32–46. Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterialforcomposting.pdf Graphabstrct.pdf Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 04 May, 2026 Reviews received at journal 27 Apr, 2026 Reviews received at journal 09 Apr, 2026 Reviewers agreed at journal 08 Apr, 2026 Reviewers agreed at journal 07 Apr, 2026 Reviewers invited by journal 07 Apr, 2026 Editor assigned by journal 30 Mar, 2026 Submission checks completed at journal 19 Mar, 2026 First submitted to journal 13 Mar, 2026 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-9114727","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":620270542,"identity":"4a7b8796-8225-4bb5-9b02-1d9c9dc075e7","order_by":0,"name":"Hongye Shen","email":"","orcid":"","institution":"Huazhong Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Hongye","middleName":"","lastName":"Shen","suffix":""},{"id":620270543,"identity":"a7f4ff57-866f-4e87-aad6-7bbc4267bc59","order_by":1,"name":"Liangxiao Ma","email":"","orcid":"","institution":"Huazhong Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Liangxiao","middleName":"","lastName":"Ma","suffix":""},{"id":620270548,"identity":"32eaf543-a0d7-454a-b294-d83e3f9048cb","order_by":2,"name":"Jinshan Li","email":"","orcid":"","institution":"Huazhong Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Jinshan","middleName":"","lastName":"Li","suffix":""},{"id":620270549,"identity":"f523a3ed-814f-4389-a178-491fa2771521","order_by":3,"name":"Yongmei Hu","email":"","orcid":"","institution":"Huazhong Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Yongmei","middleName":"","lastName":"Hu","suffix":""},{"id":620270550,"identity":"af4a1d85-51fd-4d6b-b0ee-82a3cbdfdc57","order_by":4,"name":"Nan Peng","email":"","orcid":"","institution":"Huazhong Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Nan","middleName":"","lastName":"Peng","suffix":""},{"id":620270551,"identity":"1aaf6d20-2744-46d1-b462-13d2a5952722","order_by":5,"name":"Shumiao Zhao","email":"data:image/png;base64,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","orcid":"","institution":"Huazhong Agricultural University","correspondingAuthor":true,"prefix":"","firstName":"Shumiao","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2026-03-13 12:24:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9114727/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9114727/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106961439,"identity":"beb5b195-e0de-4172-891f-748be89e5fd7","added_by":"auto","created_at":"2026-04-15 09:25:34","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":448158,"visible":true,"origin":"","legend":"\u003cp\u003eShrimp size and water‐quality parameters across regional GSFM systems.\u003cbr\u003e\n(A) Shrimp length; (B) shrimp weight. Physicochemical indicators of culture water: (C) temperature; (D) pH; (E) dissolved oxygen; (F) salinity; (G) oxidation–reduction potential (ORP); (H) total phosphorus (TP); (I) ammonium nitrogen (NH₄⁺–N); (J) orthophosphate (PO₄³⁻–P); (K) nitrite nitrogen (NO₂⁻–N); (L) nitrate nitrogen (NO₃⁻–N); (M) chlorophyll a (Chl a); (N) chemical oxygen demand (COD).\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9114727/v1/3bb83c90809eb1d0a84a8c2e.jpeg"},{"id":106961931,"identity":"9d3bb13b-4f6f-442f-847b-f7ff3e143dd8","added_by":"auto","created_at":"2026-04-15 09:27:52","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":232784,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of multifunctionality across regional GSFM systems. (A) Principal component analysis (PCA) of eleven ecosystem functions, with each color representing a different province (Guangdong, Guangxi, Fujian, and Jiangsu). A permutational multivariate analysis of variance (PERMANOVA) based on Bray–Curtis distances was employed to test for overall functional differences among regions. (B) Average multifunctionality by region. (C) Multidimensional functional indices (eigenvalues for dimensions 1–3; numbers in parentheses indicate the percentage of variance explained). Different lowercase letters denote statistically significant differences among regions (p \u0026lt; 0.05). Values are mean ± SE (n = 6).\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9114727/v1/3831e791802b0c61019b3cfe.jpeg"},{"id":106961754,"identity":"986a6e07-402f-446c-b4f0-d4934c53b6c7","added_by":"auto","created_at":"2026-04-15 09:26:51","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":312724,"visible":true,"origin":"","legend":"\u003cp\u003eRegional variations in alpha diversity. (A) Gut microbial communities, (B) Aquatic environmental microbial communities. Regional variations in beta diversity. (C) Gut microbial communities, (D) Aquatic environmental microbial communities.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9114727/v1/9775ee82563658ae98af85c7.jpeg"},{"id":106961106,"identity":"cd2809d1-f8b6-4f14-af97-1be1a8fe1251","added_by":"auto","created_at":"2026-04-15 09:24:19","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":410661,"visible":true,"origin":"","legend":"\u003cp\u003eComposition and dynamics of the microbial community at the genus level (top 30 bacterial genera). Numbers indicate replicate samples for each region. (A) Temporal dynamics of bacterial communities across four sites. (B) Average bacterial community composition for each site. (C) Temporal dynamics of fungal communities across four sites. (D) Average fungal community composition for each site.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9114727/v1/01e0edd9db5fc2dd2eb91816.jpeg"},{"id":106960345,"identity":"5c27371b-41db-4ed0-bf7b-5b30ac9331b0","added_by":"auto","created_at":"2026-04-15 09:20:21","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":488341,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional annotation of metagenome‐assembled genomes (MAGs) in GSFM. Annotation results obtained from (A) KEGG, (B) COG, and (C) CAZy databases. (D) Phylum‐level host‐tracking analysis of antibiotic‐resistance genes.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9114727/v1/fe08c6c676eb645ffb58d5a7.jpeg"},{"id":106960908,"identity":"b5ba7760-75e8-49be-96bd-c40f63b60980","added_by":"auto","created_at":"2026-04-15 09:23:35","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":306287,"visible":true,"origin":"","legend":"\u003cp\u003eDepth‐wise variations in microbiome functional potential in GSFM. Heatmaps show enrichment of genes involved in (A) carbon degradation, (B) nitrogen cycling, and (C) sulfur‐cycling pathways across four provinces. Gene‐abundance changes were evaluated by generalized linear models with a negative binomial distribution via the edgeR package. P values were derived from two‐sided likelihood‐ratio tests (LRTs) and adjusted for multiple comparisons using the Benjamini–Hochberg false‐discovery‐rate (FDR) correction. Genes with significant abundance changes (p \u0026lt; 0.05) are marked with an asterisk (*). LogFC denotes log₂‐fold change.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9114727/v1/9561901b0c210ce6d9938eb5.jpeg"},{"id":106961749,"identity":"798bd124-ae97-443e-ab8d-b68c67ac8d50","added_by":"auto","created_at":"2026-04-15 09:26:50","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":266234,"visible":true,"origin":"","legend":"\u003cp\u003eLinking physicochemical factors and microbial composition via redundancy analysis (RDA) and partial least squares path modeling (PLS‐PM). (A) RDA of water‐column microbial communities; (B) RDA of GSFM microbial communities, grouped by sample type (gut vs. water); (C) PLS‐PM results (*p ≤ 0.05; **p ≤ 0.01; ***p ≤ 0.001). Each group contains factors: Space: sampling location; Water1: temperature, pH, dissolved oxygen, salinity, ORP; Water2: CODₘₙ, NH₄⁺–N, NO₃⁻–N, TP, PO₄³⁻–P, NO₂⁻–N, Chl a; Community: alpha‐diversity metrics per sample; Type: sample type (gut or water); Growth: shrimp length and weight.\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9114727/v1/1a88ea31461a8a545caa4bc6.jpeg"},{"id":106994388,"identity":"e8282e84-26f1-451c-bf19-40dd43f750b6","added_by":"auto","created_at":"2026-04-15 15:08:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3439006,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9114727/v1/1ecc0bf6-8faf-4390-b903-ae42f5fe1538.pdf"},{"id":106920839,"identity":"09e4f03a-cd68-42fc-844f-7184e97bf2bd","added_by":"auto","created_at":"2026-04-14 19:44:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1698322,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterialforcomposting.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9114727/v1/820e5acfb7a460cf1974247e.pdf"},{"id":106960907,"identity":"9f77b616-0afa-4ad5-9310-77aaa63fd74e","added_by":"auto","created_at":"2026-04-15 09:23:35","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16429399,"visible":true,"origin":"","legend":"","description":"","filename":"Graphabstrct.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9114727/v1/da6e9a8ead6c7b5c7792472d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Microbial drivers of high aquaculture productivity: Decoupling of taxonomic and functional profiles enables efficient nitrogen cycling in greenhouse shrimp farming","fulltext":[{"header":"Highlights","content":"\u003cp\u003eλ Characterized the microbial diversity within GSFM and highlighted the multifaceted contributions and potential value of these communities to the overall aquaculture system.\u003c/p\u003e\u003cp\u003eλ Mapped the distribution of antibiotic-resistance genes in GSFM, identifying aminoglycoside, macrolide, and tetracycline resistance genes as the most prevalent, primarily associated with Proteobacteria, Bacteroidetes, and Actinobacteria.\u003c/p\u003e\u003cp\u003eλ Annotated nitrogen-cycle gene to show that gut microbiota are enriched in nitrite-metabolizing genes, while water microbiota possess great ammonium-metabolizing capacity-highlighting a complementary metabolic division of labor that underpins GSFM\u0026rsquo;s adaptation to high nitrogen loads.\u003c/p\u003e\u003cp\u003eλ The division of labour between aquatic and intestinal microbiota in nitrogen metabolism may be the key to promoting high GSFM production.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eGlobal aquaculture, expanding at an annual rate of 6.7%, has become a cornerstone of animal protein production to meet escalating food security demands\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Within this sector, the Greenhouse Shrimp Farming Model (GSFM) has emerged as a high-performance intensive system\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. However, as an emerging paradigm, its sustainable development is hindered by a lack of cohesive ecological understanding. Current research often treats GSFM as a collection of isolated, descriptive cases, failing to explain how this model maintains consistent high productivity across diverse geographic scales or how it manages the inherent trade-offs between intensive growth and biosafety risks\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe Greenhouse Shrimp Farming Model (GSFM), originating in Jiangsu Province, China, has rapidly expanded nationwide as an intensive aquaculture system for \u003cem\u003eLitopenaeus vannamei\u003c/em\u003e. Characterized by high productivity and environmental control, this model achieved a 450,000-ton output by 2023, reflecting a 22% annual growth rate\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. However, as the GSFM is adopted across diverse climatic zones, it remains unclear whether its performance is sustained by a universal microbial mechanism or if regional environmental filters (e.g., source water chemistry, temperature) override management-driven microbial assembly\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. A nationwide, cross-regional survey is therefore essential as a \"natural experiment\" to decouple these factors\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Currently, research predominantly focuses on isolated cultivation zones, neglecting systematic comparisons across geographically distinct systems\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e\"Microbial communities serve as the primary functional engines in aquaculture, regulating productivity through complex nutrient cycling and host-environment interactions. In the GSFM, despite continuous high-intensity feeding, the ecosystem maintains a remarkable metabolic balance, driven by microbiomes that efficiently decompose residual feed and fecal matter. Within this system, the gut microbiota aids in nutrient breakdown and host growth, while a diverse microbial community provides a critical biotic barrier against pathogens, thereby enhancing host immunity and survival\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. The permeable nature of the aquatic medium further facilitates a dynamic exchange, where waterborne microbes directly shape the intestinal flora through filter-feeding\u0026mdash;exemplified by \u003cem\u003eL. vannamei\u003c/em\u003e ingesting bioflocs equivalent to 5% of its body weight dail\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Poor gut health can significantly reduce aquaculture production. However, this high-efficiency resource cycling under hyper-intensive conditions does not come without ecological trade-offs\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. The same selective pressures that optimize metabolic performance also drive the accumulation and persistence of the antibiotic resistome genes(ARGs). Consequently, ARGs emerge as a critical indicator of system stability and ecological cost, representing the biosafety threshold that defines the limits of sustainable intensification in these managed ecosystems.\u003c/p\u003e \u003cp\u003eMetagenomics-based approaches have been successfully used to characterize the composition and function of microorganisms in various ecological environments\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. While substantial research has focused on vertebrate gut microbiomes (e.g., humans), the anatomical and physiological distinctions of invertebrate digestive systems (e.g., crustaceans) may impose unique selective pressures on microbial community assembly\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. To address these limitations, we propose a conceptual framework centered on functional niche partitioning and metabolic resilience. We hypothesize that the GSFM\u0026rsquo;s success is underpinned by functional convergence\u0026mdash;a process wherein standardized management pressures across regions select for distinct microbial assemblages that perform identical ecological roles, particularly in high-efficiency nitrogen detoxification. To test this hypothesis, we employed a nationwide cross-regional sampling strategy spanning four major provinces(Jiangsu-JS, Guangdong-GD, Fujian-FJ, Guangxi-GX). This design serves as a 'natural experiment' to decouple the influences of broad-scale environmental filtering from local, management-driven selective pressures. Crucially, we posit that these same selective pressures do not only optimize nutrient cycling but also shape the microbial ARGs. In this context, ARGs serve as a critical indicator of the system's ecological cost and biosafety threshold, reflecting the inherent trade-offs between metabolic performance and long-term stability in such hyper-intensive ecosystems.\u003c/p\u003e \u003cp\u003eThis study systematically investigated 90 standardized aquaculture units from 18 entities across four major provinces. During the peak harvest period, we collected water and intestinal samples for metagenomic sequencing to characterize multi-kingdom microbiota. By integrating water quality parameters and host physiological indices, we aimed to: (1) quantify the biogeographic variation in microbial functional potential\u0026mdash;specifically nutrient cycling and nitrogen transformation genes\u0026mdash;to establish the linkage between functional convergence and regional environmental efficiency; (2) elucidate the compartment-specific (water column vs. gut) functional specialization in nitrogen metabolism, resolving the key microbial drivers that enable efficient nutrient recycling and waste reduction; and (3) assess the environmental biosafety risks by characterizing the diversity, abundance, and host associations of the antibiotic resistome within these intensive production systems. Collectively, this work establishes a pioneer functional database for the GSFM, providing a mechanistic foundation for optimizing management strategies and advancing sustainable biosecurity policies.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eGeographic heterogeneity in environmental parameters and shrimp growth performance\u003c/h2\u003e \u003cp\u003eGrowth performance of \u003cem\u003eLitopenaeus vannameivaried\u003c/em\u003e significantly among provinces, with individuals from Guangxi (GX) exhibiting superior outcomes in both body length and weight compared to those from Guangdong (GD), Fujian (FJ), and Jiangsu (JS) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, B).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo decipher the environmental drivers behind this disparity, we profiled a suite of physicochemical parameters. Principal component analysis (PCA) and permutational multivariate analysis of variance (PERMANOVA; F\u0026thinsp;=\u0026thinsp;3.98, p\u0026thinsp;=\u0026thinsp;0.002) confirmed significant biogeographic differentiation in water quality (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The first three PCA axes cumulatively explained 75.6% of the total variance. PC1 (38.5%) was predominantly loaded by nutrients (NH₄⁺-N, NO₃⁻-N) and chemical oxygen demand (COD), while PC2 (21.9%) correlated strongly with dissolved oxygen (DO), NO₂⁻-N, and salinity (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).Notably, water bodies in GX were characterized by the highest concentrations of total phosphorus (TP), phosphate (PO₄\u0026sup3;⁻), ammonium (NH₄⁺-N), nitrate (NO₃⁻-N), and COD, yet the lowest concentration of nitrite (NO₂⁻-N) among all regions (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). A strong negative correlation was observed between nitrite concentration and the crustacean sclerotization index.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eConvergence of Microbial Community Structure Despite Geographic Dispersion\u003c/h3\u003e\n\u003cp\u003eWe next assessed whether the differential shrimp growth performance across provinces (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, B) was driven by underlying differences in microbial community structure. Alpha diversity analysis revealed no significant differences in the richness and diversity (Shannon index) of gut microbial communities across all four provinces. In water microbiota, overall diversity was also comparable; however, a significant reduction in the Shannon index was observed in GX compared to JS (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, B).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePrincipal coordinates analysis (PCoA) based on Bray-Curtis dissimilarity further demonstrated this structural convergence. Gut microbiota from GD and GX clustered closely, while those from FJ and JS exhibited significantly higher within-group variation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Water microbial communities from all provinces showed considerable overlap, indicating a shared core microbiota in GSFM systems (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). However, a separation of GX water samples was observed along PC1 (Explanation 46.53%). Fitting of environmental vectors to the ordination revealed that this separation was significantly correlated with higher ammonium (NH₄⁺-N) and nitrate (NO\u003csub\u003e3\u003c/sub\u003e⁻-N) concentrations (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), directly linking the unique water chemistry of GX to its divergent microbial community structure.\u003c/p\u003e\n\u003ch3\u003eFunctional Shifts in Core Microbiota across GSFM Provinces\u003c/h3\u003e\n\u003cp\u003eWe characterized the microbial taxonomic composition to identify taxa potentially contributing to the differential nitrogen metabolism observed across provinces. Bacterial communities were dominated by genera with established roles in nutrient cycling: \u003cem\u003eStreptomyces\u003c/em\u003e(7.2%, known for complex organic matter decomposition), \u003cem\u003ePseudomonas\u003c/em\u003e(5.2%, capable of denitrification and polyphosphate accumulation), and \u003cem\u003eShewanella\u003c/em\u003e(0.9%, involved in anaerobic respiration) were consistently present across all samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, B).Archaeal communities were particularly relevant to nitrogen transformation, featuring ammonia-oxidizing \u003cem\u003eNitrosopumilus\u003c/em\u003e(3.70%) and methanogenic \u003cem\u003eMethanosarcina\u003c/em\u003e(3.69%) and \u003cem\u003eMethanobacterium\u003c/em\u003e(3.43%) (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA). The co-occurrence of these taxa suggests complete nitrogen cycling pathways within GSFM systems.Eukaryotic annotations were limited, with most sequences belonging to uncharacterized protists. Some sequences received database annotations matching human pathogens (e.g., \u003cem\u003ePlasmodium\u003c/em\u003e-like), which likely represent database misannotations of uncharacterized aquatic microeukaryotes rather than actual pathogens (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC, D). Viral communities showed no consistently dominant genera across provinces (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eC), indicating high variability in viral dynamics.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCrucially, the abundance of key nitrogen-cycling taxa differed significantly among provinces. GX samples showed decreased abundance of \u003cem\u003eNitrosopumilus\u003c/em\u003e, \u003cem\u003ePseudomonas\u003c/em\u003e and other ammonia-oxidizing microorganisms, consistent with their superior nitrite conversion capacity observed in water chemistry profiles.\u003c/p\u003e\n\u003ch3\u003eFunctional Gene Profiling and Resistome Characteristics in GSFM Microbiota\u003c/h3\u003e\n\u003cp\u003eTo delineate the functional potential of the GSFM microbiota, we annotated metagenomic data against KEGG, CAZy, CARD, and BacMet databases. Global functional profiling revealed that metabolic pathways, particularly those for amino acid, carbohydrate, and energy metabolism, constituted the most abundant gene categories (Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eA). While functional profiles between gut and water samples showed broad similarity (Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eB), a core set of 20 high-abundance genes (e.g., K07497, K03088, K12845), predominantly involved in core cellular processes, accounted for over 51% of the total annotated gene abundance. Carbohydrate-Active Enzymes (CAZy), especially from the GT, GH, and CBM families, were highly represented (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, B). Notably, significant geographic variation was evident. Jiangsu (JS) samples exhibited the most distinct pattern, characterized by higher functional gene abundance in water and lower abundance in gut microbiota compared to other provinces. In contrast, the functional profiles of Guangdong (GD) and Guangxi (GX) samples clustered together, suggesting shared functional traits.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAnalysis of the antibiotic resistome identified a total of 34 antibiotic resistance gene (ARG) types. Aminoglycoside (AMG), macrolide (MAC), and tetracycline (TET) resistance genes were dominant, collectively representing 87% of the total ARG abundance. Host prediction indicated that Actinobacteria were the primary carriers of AMG genes, Bacteroidetes for TET, while MAC genes were associated with both Proteobacteria and Bacteroidetes. Metal resistance genes, as annotated by BacMet, were more diverse, with over 22 types identified, primarily conferring resistance to nickel, copper, and molybdenum. In contrast, only two herbicide resistance genes (against Ticlosan and Plumbagin) were detected. These results demonstrate that GSFM systems harbor a considerable diversity of antibiotic and metal resistance genes, while herbicide resistance is minimal.\u003c/p\u003e\n\u003ch3\u003eGeographic and Compartment-Specific Patterns in Nutrient Cycling Genes\u003c/h3\u003e\n\u003cp\u003eThe functional potential for material cycling was assessed through the abundance of key genes involved in carbon, nitrogen, and sulfur metabolism. Carbon degradation capabilities varied geographically (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Samples from Guangxi (GX) exhibited the highest relative abundance of genes for hemicellulose, cellulose, pectin, and aromatic compound degradation. In contrast, genes for starch degradation (e.g., alpha-amylase, glucoamylase) were most abundant in Fujian (FJ) samples.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNitrogen cycling genes were detected across all sites (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Genes encoding enzymes for the complete nitrogen cycle\u0026mdash;including nitrogen fixation (\u003cem\u003enifHDK\u003c/em\u003e), nitrification (\u003cem\u003eamoABC\u003c/em\u003e, \u003cem\u003ehao\u003c/em\u003e), denitrification (\u003cem\u003enarAG\u003c/em\u003e, \u003cem\u003enirKS\u003c/em\u003e, \u003cem\u003enorBC\u003c/em\u003e, \u003cem\u003enosZ\u003c/em\u003e), and assimilatory pathways (\u003cem\u003enasA\u003c/em\u003e, \u003cem\u003enirA\u003c/em\u003e, \u003cem\u003egdh\u003c/em\u003e, \u003cem\u003eureC\u003c/em\u003e)\u0026mdash;showed distinct abundance variations among provinces. Guangdong (GD) samples showed higher relative abundances of genes for assimilatory nitrate reduction (\u003cem\u003enasA\u003c/em\u003e, \u003cem\u003enirA\u003c/em\u003e), nitrogen fixation (\u003cem\u003enifH\u003c/em\u003e), and organic nitrogen metabolism (\u003cem\u003egdh\u003c/em\u003e, \u003cem\u003eglsA\u003c/em\u003e, \u003cem\u003eureAC\u003c/em\u003e).\u003c/p\u003e \u003cp\u003eSulfur cycling genes were primarily enriched in surface sediments (Fig.\u0026nbsp;56C). These included genes for sulfur oxidation (\u003cem\u003esoxABCXYZ\u003c/em\u003e), sulfate reduction (\u003cem\u003ecysCDHJN\u003c/em\u003e, \u003cem\u003eaprAB\u003c/em\u003e), thiosulfate cleavage (\u003cem\u003ephsABC\u003c/em\u003e), and sulfur reduction (\u003cem\u003edsrAB\u003c/em\u003e).A clear functional specialization was observed between the gut and water microbiota (Fig. S3). Gut microbial communities showed a higher relative abundance of genes involved in assimilatory nitrate reduction. Conversely, water-borne microbial communities were enriched in genes for ammonia assimilation.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eLinking Environmental Factors, Microbiota, and Shrimp Growth through Multivariate Modeling\u003c/h2\u003e \u003cp\u003eThe relationships between environmental parameters, microbial communities, and shrimp growth were quantified using multivariate statistical models. Redundancy analysis (RDA) revealed significant structuring of water microbial communities by province (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). Vectors for physical factors (pH, dissolved oxygen, temperature) and chemical factors (TP, NH₄⁺, COD, NO₃⁻, PO₄\u0026sup3;⁻, salinity) showed distinct clustering, indicating correlated patterns within each group (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). Shrimp length and weight were positively correlated with the aggregate of chemical factors, particularly with TP, NO₃⁻, and PO₄\u0026sup3;⁻.A Partial Least Squares Path Model (PLS-PM) was constructed to test the hypothesized causal pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). The model exhibited adequate goodness-of-fit (GoF\u0026thinsp;=\u0026thinsp;0.695). The latent variable \u0026lsquo;Physical Factors\u0026rsquo; (defined by temperature, pH, dissolved oxygen, and salinity) had a significant positive effect on \u0026lsquo;Microbial Community Metrics\u0026rsquo; (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), explaining 21.6% of its variance. The latent variable \u0026lsquo;Chemical Factors\u0026rsquo; (defined by COD, NH₄⁺-N, NO₃⁻-N, TP, PO₄\u0026sup3;⁻) had a stronger direct effect on \u0026lsquo;Shrimp Growth\u0026rsquo; (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) than \u0026lsquo;Physical Factors\u0026rsquo; did. \u0026lsquo;Spatial Characteristics\u0026rsquo; (province) was also a significant predictor of both \u0026lsquo;Chemical Factors\u0026rsquo; and \u0026lsquo;Shrimp Growth\u0026rsquo;.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eInterplay between Microbial Functional Redundancy and Niche Specialization Drives GSFM Productivity\u003c/h2\u003e \u003cp\u003eOur large-scale metagenomic survey of China\u0026rsquo;s Greenhouse Shrimp Farming Model (GSFM) reveals a core ecological principle underpinning its high productivity and regional adaptability: a decoupling of microbial community structure from function, governed by the interplay between functional redundancy and niche specialization. Contrary to the expectation that superior shrimp growth in Guangxi (GX) would be driven by a unique microbial taxonomy, we observed remarkable structural convergence in both alpha and beta diversity across provinces (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This suggests that the GSFM ecosystem exerts strong functional selection, where functional redundancy ensures core processes are maintained despite taxonomic variation. However, the key to regional success lies in specialization: the quantitative enrichment of specific pathways\u0026mdash;such as efficient denitrification and carbon degradation in GX (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, B)\u0026mdash;tailors microbial function to local environmental conditions and management practices.\u003c/p\u003e \u003cp\u003eThis functional model challenges the terrestrial-animal-centric view of the microbiome, where host genetics predominantly shapes a stable gut community. In GSFM, the gut microbiome is an open ecosystem, continuously inoculated and influenced by the water column. We discovered a sophisticated division of labor between these compartments: water microbes primarily act as ammonia assimilators, incorporating inorganic nitrogen into biomass, while gut microbes function as nitrite reducers, directly detoxifying the critical intermediate NO₂⁻ (Fig. S3). This niche partitioning optimizes system-level nitrogen metabolism, explaining the paradoxical combination of high nutrient loading and low nitrite toxicity observed in high-performing systems\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eHigh Nutrient Retention and Cycling Efficiency Underpin High Shrimp Yield in GSFM\u003c/h2\u003e \u003cp\u003eOur results revealed pronounced geographic heterogeneity in water quality, with Guangxi (GX) systems exhibiting a distinct profile characterized by significantly elevated concentrations of total phosphorus, phosphate, ammonium, nitrate, and COD\u0026mdash;collectively indicative of a high nutrient load\u0026mdash;yet maintaining the lowest nitrite (NO₂⁻-N) levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). This combination is paradoxical under classical aquaculture models, where high nutrient input typically leads to nitrite accumulation and toxicity\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Elevated nitrite concentrations reflect inhibited nitrification or enhanced denitrification, potentially leading to ecosystem imbalance\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. In shrimp culture systems, NO₂⁻-N levels correlate directly with shrimp carapace hardness and vigor, and nitrite concentration is routinely used as an indicator of system health in commercial production\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe propose that the superior shrimp yield in GX is not a direct result of high nutrient concentrations per se, but rather a consequence of a more efficient microbial nutrient cycling pipeline that effectively converts feed-derived nutrients into shrimp biomass while minimizing the accumulation of toxic intermediates. This is supported by the metagenomic evidence showing a functional enrichment in GX for complete denitrification pathways (nirS, nirK) and versatile carbon degradation capabilities (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, B). The low nitrite concentration, which strongly correlated with shrimp sclerotization and yield, is a key indicator of this efficient nitrogen transformation chain. Therefore, the water quality pattern in GX reflects a high-throughput, low-intermediate system where nutrients are rapidly assimilated into the productive food web (microbial flocs and shrimp) rather than accumulating as waste or toxins\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn contrast to natural ecosystems where elevated N and P signify eutrophication and degradation, in the managed GSFM system, these parameters\u0026mdash;when coupled with the specific microbial functional profile observed in GX\u0026mdash;are indicators of high system productivity and efficient nutrient retention.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStructural Convergence Suggests a Functionally Robust Core Microbiome in GSFM\u003c/h2\u003e \u003cp\u003eThe most striking feature of the GSFM microbiome is its structural convergence across vast geographic distances. Unlike the distinct functional gene profiles, both gut and water microbial communities showed negligible differences in alpha diversity and considerable overlap in beta diversity among the four provinces. This indicates that the GSFM environment, characterized by high biomass loading and consistent management practices, exerts strong selective pressures that shape a characteristic core microbiome regardless of location.\u003c/p\u003e \u003cp\u003eThis structural consistency suggests a high degree of functional redundancy, where different taxonomic members can perform similar ecosystem services, thereby ensuring system stability. Within this core community, we observed a co-occurrence of taxa with contrasting roles\u0026mdash;including potential pathogens (e.g., Vibrio) and beneficial organisms (e.g., Bacillus, nitrite-detoxifying Shewanella). This is not indicative of dysfunction but rather a hallmark of a complex and mature ecosystem. The persistence of potential pathogens at low abundances may be controlled by the surrounding microbial community through resource competition and antagonism, a phenomenon known as the \u0026ldquo;lottery hypothesis\u0026rdquo; where the diverse microbiota fills all available ecological niches, leaving fewer opportunities for any single pathogen to erupt\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. The presence of beneficial bacteriophages further contributes to this top-down control.\u003c/p\u003e \u003cp\u003eBeyond the direct causative links between specific bacteria and disease, studies have shown that changes in gut bacterial diversity correlate with host health and gastrointestinal disorder incidence\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Intestinal bacteria contribute to host defenses by occupying limited adhesion sites (colonization resistance)\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e, producing antimicrobial peptides to antagonize pathogens\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e, and stimulating the host immune system to modulate tolerance to other microbes. A comprehensive survey of bacteria, eukaryotes, viruses, and archaea in the GSFM system revealed the functional diversity conferred by microbial richness. Both beneficial and harmful microbial taxa exhibited appreciable abundances in this complex system. Thus, the key to GSFM's success may not lie in a unique, region-specific microbial signature, but in the system's ability to reliably assemble a functionally versatile and resilient core microbiome. This structural foundation provides the stability upon which the more nuanced, quantitative differences in metabolic pathways can build to drive high productivity.\u003c/p\u003e \u003cp\u003eThe functional robustness provided by a diverse and convergent core microbiome highlights the importance of managing the 'whole-community' health. This suggests that instead of adding single-strain probiotics, management should aim to foster the assembly of a mature and resilient microbial consortium\u0026mdash;potentially through biofloc technology or standardized inoculation during the early culture stages\u0026mdash;to preemptively fill ecological niches and suppress pathogen eruptions through competitive exclusion.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eFunctional Gene Geography Reveals Specialized Nutrient Cycling Adaptations\u003c/h2\u003e \u003cp\u003eRecent studies have emphasized the pivotal role of the gut microbiome in host health and disease\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. However, most gut microbes are unculturable in the laboratory, limiting our understanding of their functions, including those related to nutrient metabolism and antibiotic resistance\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Our metagenomic analysis reveals that while microbial community structures are convergent, functional gene profiles exhibit striking geographic specialization (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). This divergence underscores that the ecological success of GSFM is driven by quantitative shifts in core metabolic pathways rather than the presence or absence of specific taxa.\u003c/p\u003e \u003cp\u003eThe distinct functional landscape in Guangxi (GX)\u0026mdash;characterized by enhanced genes for complex carbon degradation (e.g., hemicellulose, cellulose) and complete denitrification pathways (e.g., nirS, nosZ)\u0026mdash;provides a mechanistic explanation for its superior performance. This genetic repertoire enables a more efficient conversion of feed-derived organic matter and a robust nitrogen-removal pipeline that minimizes the accumulation of toxic nitrite, a key constraint in intensive aquaculture. In contrast, the functional profile in Jiangsu (JS), though distinct, may represent a less optimized state for high-density production. Therefore, the functional gene composition, not merely the microbial taxonomy, serves as a predictive biomarker for the performance of a GSFM system.\u003c/p\u003e \u003cp\u003eThe composition of ARGs in both ecosystems and gut microbiota has been a subject of considerable interest\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. despite the reported absence of antibiotic use, highlights the persistent environmental reservoir of resistance determinants in aquaculture systems. Rovira et al. found that discontinuation of tetracycline use on farms does not automatically reduce resistance levels\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Consequently, a substantial reservoir of resistance genes remains in the GSFM system. Critically, host-tracking analysis revealed that Proteobacteria, Bacteroidetes, and Actinobacteria constituted the primary hosts of ARGs, with Proteobacteria harboring most high-abundance resistance genes (TMP resistance genes almost exclusively), and Bacteroidetes serving as the main hosts for MAC, GLY, and TET resistance genes\u0026mdash;a pattern also observed in the chicken gut microbiome. This finding signals a tangible risk for the horizontal transfer of these genes into potential pathogens, a crucial aspect for the biosafety assessment of GSFM. Our host-tracking analysis provides a diagnostic blueprint for biosecurity. By identifying Proteobacteria as the primary reservoirs of high-abundance ARGs, we provide evidence that biosecurity measures should target specific microbial lineages rather than applying broad-spectrum disinfectants, which often exacerbate resistance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSpatial Partitioning of Nitrogen Metabolism between Gut and Water Microbiota Enhances System Efficiency\u003c/h2\u003e \u003cp\u003eThe biogeochemical nitrogen cycle is typically partitioned into six key processes: assimilation, ammonification, nitrification, denitrification, anaerobic ammonium oxidation (anammox), and nitrogen fixation (N₂ fixation)\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Our results reveal a sophisticated spatial partitioning of nitrogen metabolic labor between the shrimp gut and the rearing water, a key mechanism underpinning the efficiency of the GSFM system. Contrary to the traditional view of the culture environment as a homogeneous reactor, we found a clear functional specialization: the gut microbiota primarily functions as a nitrite transformation hotspot, while the water microbiota serves as an ammonia assimilation engine.\u003c/p\u003e \u003cp\u003eThis division of labor is critical for system stability. The shrimp gut, enriched with genes for nitrite oxidation (nxr) and both pathways of nitrite reduction (to nitrate via nirK/S; to ammonium via nrfAH), acts as a primary internal detoxification organ, directly mitigating the toxicity of nitrite absorbed from the water. This explains the strong negative correlation between gut nrfAHgene abundance and nitrite concentration observed in high-performing systems like GX. Conversely, the water column, enriched with genes for ammonia assimilation (e.g., glutamate synthase), efficiently converts toxic ammonium\u0026mdash;derived from shrimp excretion and feed waste\u0026mdash;into microbial biomass. This microbial protein can subsequently be consumed by shrimp, forming a productive loop.\u003c/p\u003e \u003cp\u003eNitrite oxidation, catalyzed by nitrite oxidoreductase (NXR), is the principal biochemical route converting nitrite to nitrate\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Nitrite reduction to ammonium serves both dissimilatory and assimilatory functions\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. In most bacteria, dissimilatory nitrite reduction to ammonium is the periplasmic cytochrome c nitrite reductase (ccNIR) encoded by NrfAH\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. This reaction is catalyzed by either a heme-containing cd₁ nitrite reductase (cd₁-NIR; encoded by \u003cem\u003enirS\u003c/em\u003e) or a copper-containing nitrite reductase (Cu-NIR; encoded by \u003cem\u003enirK\u003c/em\u003e)\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e, both of which are widespread in GSFM systems. The high productivity and low nitrite stress characteristic of GX systems can be attributed to the optimal performance of this coupled system. The efficient assimilation of ammonia in the water reduces the overall nitrogen load, while the robust nitrite transformation capacity within the shrimp gut provides a critical safety net, preventing the accumulation of this key toxin.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis nationwide metagenomic survey of China\u0026rsquo;s GSFM reveals that its exceptional productivity and regional adaptability are underpinned by functional niche partitioning within the microbiome, rather than geographic shifts in microbial taxonomy. We demonstrate that the superior system performance observed in high-performing regions, such as Guangxi, is characterized by a \"high-nutrient, low-nitrite\" water quality profile, sustained by a highly efficient and spatially organized microbial consortium.\u003c/p\u003e \u003cp\u003eKey ecological mechanisms identified include: (1) a sophisticated spatial division of labor in nitrogen metabolism, wherein the shrimp gut microbiota functions as a specialized nitrite detoxification unit, while the water column microbiota acts as the primary engine for ammonia assimilation; (2) regional-scale functional gene specialization, enabling the localized optimization of nutrient cycling despite structural taxonomic convergence; and (3) the persistence of a diverse resistome, the ecological costs and biosecurity risks of which can now be more precisely evaluated through host-tracking analysis.\u003c/p\u003e \u003cp\u003eIn conclusion, the GSFM serves as a prime example of a managed ecosystem where productivity is maximized through microbially mediated resource cycling. Our findings shift the focus from microbial identity to microbial function, providing a mechanistic blueprint for the optimization of intensive aquaculture. This work suggests that future management should transition from broad-spectrum interventions to fostering the specific microbial processes identified herein\u0026mdash;such as through targeted probiotic colonization to enhance in situ nitrite detoxification\u0026mdash;thereby paving the way for more sustainable and high-yield aquaculture development.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eSample collection\u003c/h2\u003e \u003cp\u003eIn this study, aquaculture water and shrimp intestinal content samples were collected from 90 independent greenhouse shrimp farming systems operated across four provinces in China, where greenhouse shrimp farming is most extensively practiced: Jiangsu (JS), Guangdong (GD), Guangxi (GX), and Fujian (FJ). \"GSFM represents a distinctive intensive shrimp farming model in China. In our sampled small-shed management system, the stocking density was approximately 55,000 juveniles per shed, with seedstock sourced from industrial hatcheries. The feeding regimen utilized refined pellets with 43% protein content. Water was typically sourced from local groundwater with minimal exchange throughout the cycle; meanwhile, continuous aeration was maintained, and disinfectants were primarily applied during the early and late rearing phases.\u003c/p\u003e \u003cp\u003eDetailed sampling maps and schematic of the GSFM pattern are shown in Fig. S4. The GSFM represents an emerging, intensive aquaculture system in China, characterized by high-density farming, controlled environmental conditions, and efficient resource utilization, aimed at achieving high yield and productivity. This model has been widely adopted in many coastal regions. Specifically, shrimp intestine and aquaculture water samples were collected from Jiangsu (n\u0026thinsp;=\u0026thinsp;25), Guangdong (n\u0026thinsp;=\u0026thinsp;25), Guangxi (n\u0026thinsp;=\u0026thinsp;20), and Fujian (n\u0026thinsp;=\u0026thinsp;20). The selected systems were randomly sampled from hundreds of independent greenhouses maintained by local shrimp farmers to ensure representative coverage. All sampled shrimp were healthy, active, and in good physiological condition at the time of collection, which occurred uniformly in September, shortly before harvest. Water samples were collected in sterile 1 L polypropylene containers, while shrimp were aseptically dissected on-site to obtain intestinal contents using sterilized instruments. All samples were immediately placed on ice and transported to the laboratory within 24 hours. Upon arrival, samples were either processed immediately or stored at \u0026minus;\u0026thinsp;80\u0026deg;C until further analysis to preserve DNA integrity.\u003c/p\u003e \u003cp\u003eSample collection and experimental procedures were conducted between April and May 2023 and were reviewed and approved by the Ethics Committee of Huazhong Agricultural University (Approval No. HZAUFI-2025-0075).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eWater Quality Analysis\u003c/h2\u003e \u003cp\u003eFollowing sample thawing, concentrations of total phosphorus (TP), total nitrogen (TN), and permanganate index (CODₘₙ) were measured using unfiltered water samples, whereas filtered samples were employed to determine the concentrations of nitrate nitrogen (NO₃⁻-N), nitrite nitrogen (NO₂⁻-N), ammonium nitrogen (NH₄⁺-N), and orthophosphate (PO₄\u0026sup3;⁻-P).\u003c/p\u003e \u003cp\u003eTP and TN were analyzed using the potassium persulfate digestion\u0026ndash;ammonium molybdate spectrophotometric method (GB11893-89) and the alkaline potassium persulfate digestion\u0026ndash;ultraviolet spectrophotometric method (GB11894-89), respectively. Soluble orthophosphate concentrations were measured spectrophotometrically according to the \"Water and Wastewater Monitoring and Analysis Methods, Fourth Edition,\" and CODₘₙ was determined via titration (GB11892-89).\u003c/p\u003e \u003cp\u003eFor nitrogen species, NO₃⁻-N and NO₂⁻-N concentrations were assessed by ultraviolet spectrophotometry (HJ/T 346\u0026ndash;2007) and standard spectrophotometry (GB/T 7493\u0026thinsp;\u0026minus;\u0026thinsp;1987), respectively, while NH₄⁺-N was quantified using Nessler's reagent spectrophotometric method (HJ535-2009). Chlorophyll-a levels were determined following the hot ethanol extraction method (SL88-2012).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eMetagenome DNA Extraction and Shotgun Sequencing\u003c/h2\u003e \u003cp\u003eThe microbial genomic DNA samples were extracted according to the manufacturer\u0026rsquo;s Magnetic Soil And Stool DNA Kit (TINGGEN) and stored at \u0026minus;\u0026thinsp;80 ℃. To ensure the purity and quality of the extracted DNA, a NanoPhotometer and a Qubit 3.0 were used, respectively. The integrity of the DNA was assessed by agarose electrophoresis.\u003c/p\u003e \u003cp\u003eTo prepare the DNA for sequencing, 0.5 \u0026micro;g genomic DNA was randomly fragmented using Bioruptor Pico and then filtered with magnetic beads. The Adaptor was added and the DNA was repaired, followed by magnetic bead purification. PCR was performed to amplify and enrich the products. The double-stranded PCR library was then purified to unchain and loop to form a single-stranded circular DNA. Rolling ring amplification (RCA) technology was used to form the DNA nanosphere (DNB), which was loaded into the chip and fixed through a fully automatic sample loading system.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eMetagenomics Analysis\u003c/h2\u003e \u003cp\u003eRaw sequencing reads were processed to obtain quality-filtered reads for further analysis. First, sequencing adapters were removed from sequencing reads using Cutadapt (v1.2.1). Secondly, low-quality reads were trimmed using a sliding-window algorithm in Fastp\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. Once quality-filtered reads were obtained, taxonomical classifications of metagenomics sequencing read from each sample were performed using Kraken2\u003csup\u003e52\u003c/sup\u003e, which included genomes from archaea, bacteria, viruses, fungi, protozoans, metazoans, and viridiplantae. Reads assigned to metazoans or viridiplantae were removed for downstream analysis. Megahit (v1.1.2)\u003csup\u003e53\u003c/sup\u003e assembled each sample using the meta-large presetted parameters. The generated contigs (longer than 300bp) were then pooled together and clustered using mmseqs2 (Steinegger and S\u0026ouml;Ding 2017) with \u0026ldquo;easy-linclust\u0026rdquo; mode, setting the sequence identity threshold to 0.95 and covering residues of the shorter contig to 90%. The lowest common ancestor taxonomy of the non-redundant contigs was obtained by aligning them against the NCBI-nt database by mmseqs2\u003csup\u003e54\u003c/sup\u003e with \u0026ldquo;taxonomy\u0026rdquo; mode, and contigs assigned to Viridiplantae or Metazoa were dropped in the following analysis. MetaGeneMark was used to predict the genes in the contigs. CDS sequences of all samples were clustered by mmseqs2 with \u0026ldquo;easy-cluster\u0026rdquo; mode, setting the protein sequence identity threshold to 0.90 and covering residues of the shorter contig to 90%. To assess the abundance of these genes, the high-quality reads from each sample were mapped onto the predicted gene sequences using salmon in the quasi-mapping-based mode with \u0026ldquo;--meta --minScoreFraction\u0026thinsp;=\u0026thinsp;0.55\u0026rdquo;, and the copy per kilobase per million mapped reads (CPM) was used to normalize abundance values in metagenomes. The non-redundant genes' functionality was obtained by annotating using mmseqs with the \u0026ldquo;search\u0026rdquo; mode against the KEGG EggNOG and CAZy databases' protein databases, respectively. EggNOG and GO were obtained using EggNOG-mapper (v2)\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. GO ontology was obtained using map2slim (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.metacpan.org\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.metacpan.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). KO was obtained using KOBAS\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eData manipulation and visualization were performed through the R meta package tidyverse (2.0.0)\u003csup\u003e57\u003c/sup\u003e. T-test, Kruskal-Wallis rank sum test, and Wilcoxon rank sum test were performed through functions \u0026lsquo;t.test\u0026rsquo;, \u0026lsquo;kruskal.test\u0026rsquo;, and \u0026lsquo;wilcox.test\u0026rsquo; in package stats (4.2.1). Before analysis, the samples were rarefied to uniform depth based on the lowest sample sequence to eliminate the influence of different sequencing depths. Alpha diversity indices (Shannon, pielou\u0026rsquo;s eveness, observed species, and faith\u0026rsquo;s pd) were calculated using functions diversity in vegan (2.7). Beta diversity metrics (Jaccard dissimilarity, Bray-Curtis dissimilarity), as well as PCA, were conducted using the function \u0026lsquo;rda\u0026rsquo; in vegan (2.7)\u003csup\u003e58\u003c/sup\u003e followed by an ADNOIS test to measure the changes related to sampling sites\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. Heatmaps were plotted using the complexheatmap (2.16.0) and pheatmap (1.012) packages. A Partial Least Squares Path Model (PLS-PM) analysis was also implemented in R using the plspm package. The raup-Crick dissimilarity index was calculated using a custom function provided by Chase et al. All visualizations are done in R, mainly based on ggplot2 (3.6.1).\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCONFLICT OF INTEREST STATEMENT\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eETHICS STATEMENT\u003c/h2\u003e \u003cp\u003eThis study has an ethical clearance number of HZAUFI-2025-0075.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eHongye Shen : Writing\u0026mdash;original draft; writing\u0026mdash;review and editing; conceptualization; supervision. Liangxiao Ma : Writing\u0026mdash;original draft; writing\u0026mdash;review and editing; conceptualization. Jinshan Li : Writing\u0026mdash;original draft. Yongmei Hu : Writing\u0026mdash;original draft. Nan Peng : Writing\u0026mdash;original draft. Shumiao Zhao: Conceptualization; project administration; writing\u0026mdash;review and editing; supervision; funding acquisition.\u003c/p\u003e\u003ch2\u003eACKNOWLEDGMENTS\u003c/h2\u003e \u003cp\u003eWe thank the BaiChuan fellowship of the College of Life Science and Technology, Huazhong Agricultural University, for funding support. This study was financially supported by\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eWe have uploaded the raw data. All the metagenomic sequencing raw data have been deposited in the NCBI Sequence Read Archive database under BioProject number PRJNA1256668.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNaylor RL, et al. Blue food demand across geographic and temporal scales. Nat Commun. 2021;12:5413.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTigchelaar M, et al. Compound climate risks threaten aquatic food system benefits. Nat Food. 2021;2:673\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuo H et al. Sucrose addition directionally enhances bacterial community convergence and network stability of the shrimp culture system. npj Biofilms Microbiomes 8, (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNguyen TAT, Nguyen KAT, Jolly C. Is Super-Intensification the Solution to Shrimp Production and Export Sustainability? Sustainability. 2019;11:22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFisheries Bureau of the Ministry of Agriculture. China Fisheries Statistical Yearbook 2013M. Beijing: China Agriculture; 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkdeniz HB, Yalpir S, Inam S. Assessment of suitable shrimp farming site selection using geographical information system based Analytical Hierarchy Process in Turkey. Ocean Coast Manage. 2023;235:106468.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang Y, Jiang KY, Liu M, Wang BJ, Wang L. Microbial community dynamics and nitrogen transformation associated with turbid-turned culture stage of Pacific white shrimp, Litopenaeus vannamei in small greenhouse farms. Aquaculture. 2024;593:741285.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen BY, Huang PL, Hou YL, Lan HY, Huang CT, Nan FH. The economic feasibility of aquavoltaics in Taiwan- A case study of whiteleg shrimp culture. Aquaculture. 2024;581:740454.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarreto MK, Oliveira CTA, Henry-Silva GG. Greenhouse gas emission flux (CO, CH, NO) from marine shrimp (Litopenaeus vannamei) monoculture cultures in the Brazilian semi-arid region. Aquaculture. 2024;582:740536.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTong C, et al. Annual CO2 and CH4 fluxes in coastal earthen ponds with Litopenaeus vannamei in southeastern China. Aquaculture. 2021;545:737229.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKring NA, Fleckenstein LJ, Tierney TW, Fisk JC, Lawson BC, Ray AJ. The effects of stocking density and artifical substrate on production of pacific white shrimp and water quality dynamics in greenhouse-based biofloc systems. Aquacult Eng. 2023;101:102322.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHolt CC, van der Giezen M, Daniels CL, Stentiford GD, Bass D. Spatial and temporal axes impact ecology of the gut microbiome in juvenile European lobster (Homarus gammarus). ISME J. 2020;14:531\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiong JB, Dai WF, Zhu JY, Liu KS, Dong CM, Qiu QF. The Underlying Ecological Processes of Gut Microbiota Among Cohabitating Retarded, Overgrown and Normal Shrimp. Microb Ecol. 2017;73:988\u0026ndash;99.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZokaeifar H, et al. Effects of Bacillus subtilis on the growth performance, digestive enzymes, immune gene expression and disease resistance of white shrimp. Fish Shellfish Immun. 2012;33:683\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePerry WB, Lindsay E, Payne CJ, Brodie C, Kazlauskaite R. The role of the gut microbiome in sustainable teleost aquaculture. P Roy Soc B-Biol Sci. 2020;287:1926.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBernhardt JR, O'Connor MI. Aquatic biodiversity enhances multiple nutritional benefits to humans. Proc. Natl. Acad. Sci. U.S.A 118, 15 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStentiford GD, et al. New Paradigms to Help Solve the Global Aquaculture Disease Crisis. Plos Pathog. 2017;13(2):1006160.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStentiford GD, Bass D, Williams BAP. Ultimate opportunists-The emergent group Microsporidia. Plos Pathog. 2019;15:1007668.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTrevathan-Tackett SM, et al. A horizon scan of priorities for coastal marine microbiome research. Nat Ecol Evol. 2019;3:1509\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCopetti F, Gregoracci GB, Vadstein O, Schveitzer R. Management of biofloc concentrations as an ecological strategy for microbial control in intensive shrimp culture. Aquaculture. 2021;543:736969.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShen H, et al. Native synthetic microbial communities enhance zha-chili by boosting the fermentation capacity of indigenous microorganisms. iMetaOmics. 2025;2:2.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShen H, et al. Metagenome-assembled genome reveals species and functional composition of Jianghan chicken gut microbiota and isolation of Pediococcus acidilactic with probiotic properties. Microbiome. 2024;12:25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou Q, et al. Intestinal microbiome-mediated resistance against vibriosis for Cynoglossus semilaevis. Microbiome. 2022;10:153.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePetersen JM, Osvatic J, Microbiomes. Importance of Invertebrates in Understanding the Natural Variety of Animal-Microbe Interactions. Msystems. 2018;3:13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKanehisa M. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 2000;28(1):27\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeng YQ, Wang YA, Zhu BL, Gao GF, Guo YM, Hu YF. Metagenome-assembled genomes and gene catalog from the chicken gut microbiome aid in deciphering antibiotic resistomes. Commun Biology. 2021;4:1305.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi CY, et al. Expanded catalogue of metagenome-assembled genomes reveals resistome characteristics and athletic performance-associated microbes in horse. Microbiome. 2023;11:7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCao YH, et al. The multi-kingdom microbiome of the goat gastrointestinal tract. Microbiome. 2023;11:219.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKillberg-Thoreson L, Mulholland MR, Heil CA, Sanderson MP, O'Neil JM, Bronk DA. Nitrogen uptake kinetics in field populations and cultured strains of Karenia brevis. Harmful Algae. 2014;38:73\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKieley CM, et al. Concentration of total microcystins associates with nitrate and nitrite, and may disrupt the nitrogen cycle, in warm-monomictic lakes of the southcentral United States. Harmful Algae. 2023;130:102542.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHou DW, Li HY, Wang S, Weng SP, He JG. Nitrite nitrogen stress disrupts the intestine bacterial community by altering host-community interactions in shrimp. Sci Total Environ. 2024;925:171536.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin LT, et al. Integrated histological, physiological, and transcriptome analysis reveals the post-exposure recovery mechanism of nitrite in Litopenaeus vannamei. Ecotox Environ Safe. 2024;281:116673.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi ZJ, et al. Insight into aerobic phosphorus removal from wastewater in algal-bacterial aerobic granular sludge system. Bioresour Technol. 2022;352:127104.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang ZK, et al. Effect of acetochlor on the symbiotic relationship between microalgae and bacteria. J Hazard Mater. 2024;463:132848.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCampos-Pardos E, Uranga S, Pic\u0026oacute; A, G\u0026oacute;mez AB, Gonzalo-Asensio J. Dependency on host vitamin B12 has shaped Complex evolution. Nat Commun. 2024;15:2161.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVerster AJ, Borenstein E. Competitive lottery-based assembly of selected clades in the human gut microbiome. Microbiome. 2018;6:186.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDonaldson GP, Lee SM, Mazmanian SK. Gut biogeography of the bacterial microbiota. Nat Rev Microbiol. 2016;14:20\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLawley TD, Walker AW. Intestinal colonization resistance. Immunology. 2013;138:1\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarcia-Gutierrez E, Mayer MJ, Cotter PD, Narbad A. Gut microbiota as a source of novel antimicrobials. Gut Microbes. 2019;10:1\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang Y, Lv HJ, Song YC, Sun CJ, Zhang ZF, Chen SR. Community composition of cecal microbiota in commercial yellow broilers with high and low feed efficiencies. Poult Sci. 2021;100:100996.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWen CL, et al. The gut microbiota is largely independent of host genetics in regulating fat deposition in chickens. ISME J. 2019;13:1422\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eImperial ICVJ, Ibana JA. Addressing the Antibiotic Resistance Problem with Probiotics: Reducing the Risk of Its Double-Edged Sword Effect. Front Microbiol. 2016;7:1983.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao J, et al. Intestinal toxicity and resistance gene threat assessment of multidrug-resistant Shigella: A novel biotype pollutant. Environ Pollut. 2023;316:120551.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRovira P, et al. Characterization of the Microbial Resistome in Conventional and Raised Without Antibiotics Beef and Dairy Production Systems. Front Microbiol. 2019;10:1980.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKuypers MMM, Marchant HK, Kartal B. The microbial nitrogen-cycling network. Nat Rev Microbiol. 2018;16:263\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZehr JP, Jenkins BD, Short SM, Steward GF. Nitrogenase gene diversity and microbial community structure: a cross-system comparison. Environ Microbiol. 2003;5:539\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFang FC. Antimicrobial reactive oxygen and nitrogen species: Concepts and controversies. Nat Rev Microbiol. 2004;2:820\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaia LB, Moura JJG. How Biology Handles Nitrite. Chem Rev. 2014;114:5273\u0026ndash;357.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTikhonova TV, et al. Molecular and catalytic properties of a novel cytochrome nitrite reductase from nitrate-reducing haloalkaliphilic sulfur-oxidizing bacterium. Bba-Proteins Proteom. 2006;1764:715\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZumft WG. Cell biology and molecular basis of denitrification. Microbiol Mol Biol Rev. 1997;61:533\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen SF, Zhou YQ, Chen YR, Gu J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics. 2018;34:884\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWood DE, Lu J, Langmead B. Improved metagenomic analysis with Kraken 2. Genome Biol. 2019;20:257.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi DH, Liu CM, Luo RB, Sadakane K, Lam TW. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct graph. Bioinformatics. 2015;31:1674\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSteinegger M, S\u0026ouml;ding J. MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nat Biotechnol. 2017;35:1026\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCantalapiedra CP, Hern\u0026aacute;ndez-Plaza A, Letunic I, Bork P, Huerta-Cepas J. eggNOG-mapper v2: Functional Annotation, Orthology Assignments, and Domain Prediction at the Metagenomic Scale. Mol Biol Evol. 2021;38:5825\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBu DC, et al. KOBAS-i: intelligent prioritization and exploratory visualization of biological functions for gene enrichment analysis. Nucleic Acids Res. 2021;49:W317\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWickham H, et al. Welcome to the Tidyverse. J Open Source Softw. 2019;4:43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFaith DP, Minchin PR, Belbin L. Compositional dissimilarity as a robust measure of ecological distance. Vegetatio. 1987;69:57\u0026ndash;68.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnderson MJ. A new method for non-parametric multivariate analysis of variance. Austral Ecol. 2001;26:32\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"animal-microbiome","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"amic","sideBox":"Learn more about [Animal Microbiome](http://animalmicrobiome.biomedcentral.com)","snPcode":"42523","submissionUrl":"https://submission.nature.com/new-submission/42523/3","title":"Animal Microbiome","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Greenhouse shrimp farming model, Litopenaeus vannamei, microbial community succession, antibiotic-resistance genes, nitrogen cycle","lastPublishedDoi":"10.21203/rs.3.rs-9114727/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9114727/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAs a global cornerstone of animal protein, farmed shrimp production in China has been revolutionized by the Greenhouse Shrimp Farming Model (GSFM). However, the microbial mechanisms underpinning the productivity and biosafety of this intensive paradigm remain poorly understood. Through a nationwide metagenomic survey of 90 standardized aquaculture units across a 15-degree latitudinal gradient, we deciphered the functional architecture of the GSFM microbiome. Our results reveal a striking functional convergence despite geographic taxonomic variation: while microbial compositions shifted across regions, the core metabolic pathways\u0026mdash;particularly those governing nutrient cycling\u0026mdash;remained conserved. High-productivity systems, notably in southern provinces (Guangxi and Guangdong), were characterized by a specialized microbial repertoire that optimized water chemistry through efficient nitrogen processing. We identified a sophisticated spatial division of labor, where the gut microbiota acts as a specialized nitrite detoxification unit, while the water column microbiota serves as the primary engine for ammonia assimilation. Furthermore, we characterized a diverse antibiotic resistome and utilized host-tracking to evaluate the ecological costs of intensification. These findings shift the focus from microbial identity to functional niche partitioning, providing a mechanistic blueprint for the sustainable intensification of aquaculture through targeted microbiome management.\u003c/p\u003e","manuscriptTitle":"Microbial drivers of high aquaculture productivity: Decoupling of taxonomic and functional profiles enables efficient nitrogen cycling in greenhouse shrimp farming","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-14 19:44:37","doi":"10.21203/rs.3.rs-9114727/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-04T14:35:03+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-27T06:20:42+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-09T11:07:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"144242980357762263088658002598070062623","date":"2026-04-08T08:46:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"27517757981274688051425246737989026759","date":"2026-04-07T12:56:47+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-07T12:20:25+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-30T23:43:02+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-19T09:39:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"Animal Microbiome","date":"2026-03-13T12:13:09+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"animal-microbiome","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"amic","sideBox":"Learn more about [Animal Microbiome](http://animalmicrobiome.biomedcentral.com)","snPcode":"42523","submissionUrl":"https://submission.nature.com/new-submission/42523/3","title":"Animal Microbiome","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ec5ceb2e-006c-4738-ba68-bef5322435bb","owner":[],"postedDate":"April 14th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-04T14:35:03+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T14:40:23+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-14 19:44:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9114727","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9114727","identity":"rs-9114727","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}