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Scientific Reports volume 16, Article number: 4279 (2026)
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The photovoltaic (PV) fishery breeding model integrates the generation of solar energy with aquaculture, yet its impacts on aquatic organisms remain poorly understood. This study investigated how PV panel shading affects the intestinal microbial ecosystem of Litopenaeus vannamei. We conducted a controlled 80-day experiment comparing shrimp reared under PV panels (ZG group) versus those reared in traditional open ponds (CK group), with quadruplicate 800 m² ponds per group under standardized conditions (80 shrimp/m², salinity 15–18‰). High-throughput 16 S rRNA sequencing was employed to analyze microbial composition, diversity, and predicted functional profiles. The growth data were collected daily during the initial 20-day period and subsequently at five-day intervals thereafter. The results demonstrate that the ZG group exhibited significantly reduced body length compared to the CK group after 20 days of culture (P < 0.05), while body weight was significantly lower after 16 days (P < 0.05). The results of the intestinal microbiota analysis showed that Proteobacteria and Firmicutes were the main components of the intestinal microbiota in the CK and ZG groups, while Oceanobacillus and Candidatus_Electronema were present as indicator species in the CK and ZG groups, respectively. Analysis of the Chao1 index and Shannon index revealed no significant differences in either the diversity or evenness of the intestinal microbiota of L. vannamei among the experimental groups. In addition, significant differences between the groups were detected by the β-diversity analysis. A predicted bacterial function analysis also revealed significant differences in functional abundance between the two groups. This study provides critical insight into how PV shading alters shrimp microbiota and growth performance, offering practical guidance for optimizing sustainable PV-aquaculture integrated systems.
Integrating photovoltaic (PV) systems with aquaculture is a promising approach for sustainable development, as it simultaneously addresses energy production and food security challenges. The PV fishery breeding model achieves clean energy and aquatic products while improving land-use efficiency1,2,3. However, the shading effect of PV panels significantly changes the light conditions in the aquaculture environment4, which influences physiology5,6.
Current research has demonstrated the profound effects of light intensity on crustaceans. Studies on Jasus edwardsii and Scylla paramamosain have shown that low light levels during culture significantly enhance growth rate7,8. Wang et al. reported significant differences in the growth and exuviation of Fenneropenaeus chinensis under different light intensities, with optimal growth observed at light intensities ranging from 50 to 300 lx8. Gardner et al. showed that low light intensity promotes molting in Pseudocarcinus gigas larvae and reduces the rate of residual feeding9. In addition, Oreochromis niloticus and Paralichthys dentatus have lower plasma cortisol concentrations in darker environments10. Melanogrammus aeglefinus exhibits higher locomotor activity at 100 lx compared to 30 lx11. While existing research has not yet directly investigated the impact of light on the intestinal microbiota of shrimp, it is known that light can influence water temperature – a critical abiotic factor affecting shrimp environmental adaptation12. Environmental temperature fluctuations have been shown to alter the abundance of host gut microbial communities and metabolite concentrations, with distinct bacterial populations emerging under different conditions13. For instance, the dominant gut microbiota of Macrobrachium nipponense exhibits temperature-dependent shifts14. As temperature rises, beneficial bacteria such as Bifidobacterium and Lactobacillus increase in Litopenaeus vannamei intestines, suppressing pathogen colonization and thereby enhancing host digestion and nutrient absorption15.
Litopenaeus vannamei (phylum Arthropoda, family Penaeidae), a species native to the tropical Pacific coast of western Latin America16, has become a cornerstone of sustainable aquaculture due to its rapid growth, broad salinity tolerance, and strong disease resistance. Its high nutritional value – characterized by delicious meat, rich protein content, and low-fat levels – further enhances its economic and dietary appeal17. As a dominant species in China’s photovoltaic (PV) fishery breeding facilities, L. vannamei faces unique environmental challenges from PV shading, which may disrupt its intestinal microbiota – a key regulator of nutrient absorption, immune function, and pathogen resistance. The intestinal microbiome plays a pivotal role in host health, influencing growth performance and disease susceptibility through its interactions with environmental factors. Despite its importance, the impact of PV shading on the composition, diversity, and functional dynamics of L. vannamei’s intestinal microbiota remains unexplored, despite its potential implications for sustainable aquaculture practices.
The primary scientific question addressed in this study is whether PV-induced light modulation significantly alters the characteristics of L. vannamei’s intestinal microbiota, thereby influencing growth performance compared to traditional pond systems. This investigation is grounded in the growing recognition of environment-microbiota interactions in aquatic species. To address this question, we focus on two key objectives: first, analyzing the effect of PV shading on the composition and diversity of intestinal microbiota in L. vannamei; and second, evaluating the growth performance (body length/weight) of L. vannamei in PV versus traditional pond systems. By elucidating how PV shading drives microbial shifts and linking these changes to growth outcomes, this study aims to provide empirical evidence for optimizing PV-aquaculture practices and advancing our understanding of environment-microbiota interactions in commercially important species.
L. vannamei were collected from Wenchun Town, Taishan City, Guangdong Province, China. Four ponds each were allocated to the experimental group (ZG) and the control group (CK), respectively. All ponds were standardized cement pools, with a single pool area of 800 m2 (40 × 20 m), a water depth of 1.2 m, and equipped with an oxygen generator and a water-circulating system. The ZG was equipped with an overhead PV panel system covering 50% of the pond surface, whereas the CK was cultured in a traditional open-air aquaculture pond structure. The water source for the two groups was the same (salinity, 15–18‰, and 80 shrimp/m2 stocking density; body length 1.2 ± 0.3 cm). The water-circulating system maintained a partial water exchange rate of 10% daily, with complete water replacement every 10 days to ensure stability of the microbial community. This protocol was applied to the ZG and CK groups to eliminate water renewal frequency as a confounding variable. The water temperature was measured using a HOBO U22-001 high-precision temperature recorder (± 0.2℃). Fixed monitoring points were set at two depths: the surface layer (0.2 m) and the bottom layer (1.0 m) of the breeding pool (the ZG group was placed under the PV panels). The temperature was automatically recorded every 10 min. Continuous monitoring was conducted from 08:00 to 18:00 every day, and a YSI Pro2030 handheld water quality analyzer was used for manual calibration (3 times per week) simultaneously. All data were verified by the NIST standard temperature source and averaged daily for analysis.
A standardized commercial diet containing 30% crude protein was manually broadcast twice daily at 08:00 and 20:00 using automated feeders. The feeding rate was adjusted weekly based on calculated shrimp biomass, commencing at 5% of total biomass at trial initiation and progressively decreasing to 2% by experiment conclusion. Daily quantification of residual feed was performed through dedicated feeding observation logs, with subsequent adjustments made to the feeding regimen to ensure optimal consumption efficiency. Morphometric measurements, including body length and body weight, were systematically conducted throughout the culture period. Sampling frequency followed a two-phase protocol: daily measurements were taken during the initial 20-day period, subsequently transitioning to five-day intervals for the remainder of the experiment. Measurements were performed between 08:00 and 10:00 h. Five individuals were randomly selected from each culture pond. Body length (postorbital carapace to the telson tip) was measured using a digital caliper (precision: ±0.01 mm) and recorded. Body weight (wet weight) was determined using an electronic balance (precision: ±0.01 g) following a 12-hour postprandial fasting period. Due to the small size of the shrimp during the first 20 days, weight data were obtained via pooled weighing of ten shrimp per sample, with individual weights calculated subsequently. Shrimp were weighed separately after 20 days of culture. Five replicates per sampling event were performed for weight measurements within each pond. After the 80-day culture experiment, 16 L. vannamei with a healthy appearance, intact appendages, and consistent size were randomly selected from the two groups. Four L. vannamei were collected from each breeding pool to constitute one biological sample. Four such samples were collected from each culture model as experimental replicates. The intestinal contents were collected under sterile conditions and immediately flash-frozen in liquid nitrogen for the intestinal microbiota analysis.
DNA was extracted using the HiPure Stool DNA Kit(D5625-01) (Magen, Guangzhou, China) kit. Primers 338 F (5’- CCTACGGGNGGCWGCAG − 3’) and 806R (5’- GGACTACHVGGGTATCTAAT − 3’) were used to amplify the V3-V4 region of 16 S rRNA. Paired-end sequencing (2 × 250 bp) was performed on an Illumina NovaSeq 6000 platform using the NovaSeq 6000 SP Reagent Kit (500 cycles). The primers used were taken from Guo et al. (2017)2. The resulting amplicons were extracted from 2% agarose gels and purified using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA) according to the manufacturer’s instructions, and quantified using the ABI StepOnePlus Real-Time PCR System (Life Technologies, Foster City, CA, USA). The purified amplicons were pooled in equimolar quantities and pair-end sequenced (PE250) on the Illumina platform according to the standard protocol.
Statistical analysis of body length, body weight, and water temperature data was performed using GraphPad Prism 8.0.1 software. Comparative analysis between experimental groups was conducted for data collected during equivalent time periods. Additionally, the software was utilized to generate curves depicting temporal variations in body length, body weight, and water temperature parameters.
The operational taxonomic units (OTUs) were classified at the species level through taxonomic classification and annotation against the database. Stacked bar charts of the microbial species distribution for each group were generated at the phylum and genus levels18,19,20. The labdsv package in R (The R Foundation for Statistical Computing, Vienna, Austria) was used to calculate the indicator values for each species across the different groups. A 10-fold cross-validation approach was applied to assess species differentiation21,22. The Chao1 and Shannon indices were calculated in QIIME (version 1.9.1)23. The OTU rarefaction and rank abundance curves were plotted in R using the ggplot2 package (version 2.2.1). The alpha index comparison between groups was calculated using Welch’s t-test and the Wilcoxon rank test using the vegan package in R (version 2.5.3)24. The bacterial community compositions were ordinated by principal coordinate analysis (PCoA) based on the Bray-Curtis dissimilarity values. Permutational multivariate analysis of variance was performed to examine the differences in microbial community composition among the different groups based on the Bray-Curtis dissimilarities using the ‘adonis’ function in the vegan package24,25. The KEGG pathway analysis of the OTUs was inferred using Tax4Fun (version 1.0) or PICRUSt26,27. The analysis of functional differences between groups was calculated by Tukey’s HSD test in R using the vegan package (version 2.5.3)24. The network was constructed with a Pearson’s correlation coefficient exceeding 0.9 and a significance level of p-value less than 0.05. The network was visualized using igraph28 (Fig 1).
Experimental procedure. The L. vannamei were placed in the ponds of the photovoltaic fishery model and the ordinary pond culture model, and their intestinal tracts were excised for analysis after 80 days. L. vannamei growth performance and water temperature data were recorded during the culture period.
Water temperature monitoring revealed significantly higher mean values in the CK (average surface water temperature: 32.38 ± 1.83℃, average bottom water temperature: 30.06 ± 1.42℃) than the ZG (average surface water temperature: 31.52 ± 1.72℃, average bottom water temperature: 29.29 ± 1.34℃) group over the 80-day culture period. The CK group exhibited an average surface water temperature increase of 0.85 °C and an average bottom water temperature increase of 0.76 °C relative to the ZG group (Fig. S1).
Morphometric analysis of L. vannamei indicated significant growth differences between the groups. The body length of L. vannamei was significantly shorter in the ZG group (4.94 ± 0.06 cm) than that in the CK group (4.98 ± 0.06 cm) on day 20 of culture (Fig. 2A). Similarly, body weight measurements indicated significantly lower values in the ZG group (ZG: 0.781 ± 0.00 g; CK: 0.844 ± 0.00 g) starting on day 16 post-stocking (Fig. 2B). Specifically, for the microbiological research component, the body length and weight of L. vannamei in the ZG group were measured at (10.54 ± 0.37) cm and (20.00 ± 1.15) g, respectively, while the corresponding values for the CK group were (12.32 ± 0.54) cm and (22.98 ± 0.64) g.
Changes in body length and weight of L. vannamei after 80 days of culture. (A) Curve of the body length changes. (B) Curve of the body weight changes. * Indicates a significant difference between the two groups during this time period.
We obtained 1,033,325 raw tags and 1,032,822 clean tags from the intestines of eight biological samples. After removing the chimeric tags detected in the clustered analog pairs, 960,147 valid tags were obtained. Additionally, we calculated the number of OTUs and the coverage rate of each sample exceeded 99.94% (Table 1). Rank abundance curves were generated for all samples based on the OTU abundance and ranking to illustrate species richness and evenness. Figure 3 provides an overview of the species composition in each sample.
Dilution curves for the Sob index of the 16 S rRNA gene MiSeq sequences from the different light condition samples. Different colored lines represent different samples. The horizontal and vertical coordinates represent the number of tags extracted and the number of tags extracted corresponding to the calculation of the diversity index value, respectively. CK: control group, ZG: 24-hour shading treatment.
The cumulative abundance of the top ten bacterial phyla in the intestinal microbiota of the treatment groups exceeded 94%, and Proteobacteria was the most dominant. The abundance of Proteobacteria and Firmicutes decreased, whereas Verrucomicrobiota increased in the ZG group compared with the control group (Fig. 4A).
Aeromonas and Vibrio were the dominant genera in the treatment groups. The abundance of Aeromonas, Vibrio, Bacillus, and Staphylococcus decreased in the ZG, while LD29, Thioclava, and Rhodobacter increased (Fig. 4B). Additionally, 244 bacterial genera were shared between the two treatment groups (Fig. 4C).
The indicator species analysis, based on species abundance and occurrence frequency, revealed that Oceanobacillus had the highest indicator value in the CK, whereas C._Electronema had the highest indicator value in the ZG. This result indicates that Oceanobacillus and C._Electronema could serve as indicator species for the CK and ZG groups, respectively (Fig. 4D).
Changes in the intestinal microbiota composition under the different light treatments. (A) Distribution of intestinal bacterial phyla in each sample of the experimental and control groups. (B) Distribution of intestinal bacteria genera in the treatment and control groups. (C) Upset plot of species composition between the treatment groups. (D) Indicator analysis between the treatment groups.
The Chao1 and Shannon alpha diversity indices (Fig. 5A-B) increased in the ZG group, but no significant differences were observed. Closer samples had more similar microbiomes. The Adonis analysis revealed a significant overall difference in the intestinal microbiota between the ZG and CK groups (Fig. 5C). The PcoA based on OTU abundance revealed clear separation between the groups, while samples within each group clustered closely together (Fig. 5D).
Additionally, bacterial phylogenetic tree analysis of L. vannamei indicated that the abundance of Fimbriiglobus, Butyrivibrio, Desulfomicrobrio, Bdellovibrio, and Chryseolinea was significantly higher in the ZG than the CK. In contrast, the abundance of Mariniradius, Muribaculum, Parabacteroides, Nonomuraea, and Cloacibacillus was significantly lower in the ZG than in the CK (Fig. 5E).
Diversity of the gut bacteria in the treatment groups. (A) Box chart of the inter-group differences in the Chao 1 index. (B) Box chart of the inter-group differences in the Shannon index. (C) Adonis analyzed the explanatory power of the groups for sample differences and used the permutation test to detect differences between the groups. (D) Principal coordinates analysis between samples from the treatment groups. (E) Phylogenetic relationship among the genera of the horizontal species. A phylogenetic tree was constructed with representative sequences of the genera of the horizontal species. The colors of the branches and the fan-shaped branches represent the corresponding gates, and the stacking histogram outside the fan ring represents the abundance of the genus in the samples.
To further investigate the effect of light on the intestinal microbiota of L. vannamei, we predicted bacterial functions and assessed whether the light treatments affected the functionality of the intestinal microbiota. The predictive functional analysis revealed that metabolism and cellular processes were dominant across all groups. Using analysis of variance and Tukey’s HSD test, we detected the significant differences among the eight most abundant bacterial functions in the groups. The results indicated that the functional abundances of bacterial chemotaxis, the bacterial secretion system, cysteine and methionine metabolism, riboflavin metabolism, nitrogen metabolism, cyanoamino acid metabolism, the phosphotransferase system, and plant-pathogen interactions decreased significantly under the PV fishery breeding model (Fig. 6A). Furthermore, the bacterial interaction analysis revealed distinct topological architectures, including differences in node connectivity and edge distribution, between CK and ZG groups. The CK network consisted of 227 nodes and 2,497 edges, whereas the ZG network contained 192 nodes and 1,379 edges. These results indicated significantly higher connectivity in the CK network (Fig. 6B and C). Specifically, in the CK network, the strongest interactions were observed between Firmicutes and Proteobacteria, within Firmicutes (intra-phylum), and between Actinobacteriota and Firmicutes (Fig. 6B). In contrast, the strongest interactions in the ZG network occurred between Firmicutes and Proteobacteria, within Proteobacteria (intra-phylum), and between Actinobacteriota and Proteobacteria associations (Fig. 6C).
Functional prediction of the intestinal microbiota. A Tukey’s Honestly Significant Difference rank-sum test of the significance differences in functions between the groups. The eight functions predicted to have the highest abundance were compared for differences between the groups, with an asterisk indicating a difference. (B) Network diagram of the microbial community structure of Group CK. (C) Network diagram of the microbial community structure of Group ZG.
Photoperiod was first studied in plants, where it affects physiological activities such as flowering and fruiting29. However, light is an important environmental factor that cannot be ignored for the health and development of aquatic animals30. Wang et al. reported that the most suitable growth environment for the intensive farming of Marsupenaeus japonicas is total darkness31. Wu et al. showed that Cherax quadricarinatus shrimp have the lowest survival rate and the slowest growth rate under full darkness32. In this study, measurements of body length and body weight indicated that the growth rate of L. vannamei in the PV fishery model was slower than that in the traditional pond culture model, which may be attributed to the combined effects of light and temperature. Previous studies have showed that light environment variations can influence the growth of aquatic organism by modulating digestive enzymes activity33,34,35. For instance, protease facilitates proteins hydrolysis into amino acids, thereby promoting nutrient absorption in aquatic organisms33. Specifically, α-amylase (AMS) primarily catalyzes the hydrolysis of starch and glycogen34, and lipse (LPS) catalyzes lipid hydrolysis of aquatic organisms, converting it into energy substrates to support growth35. Reduced light conditions under PV panels may suppress the activity of these enzymes, thereby impairing digestive efficiency and overall growth in L. vannamei. Meanwhile, light exposure may also affect the growth of L. vannamei by influencing its feeding behavior, and future studies could incorporate continuous feeding monitoring to better characterize this relationship36.
For crustaceans, temperature is an especially crucial environmental factor. Due to their unique molting-based growth and developmental mechanism, crustaceans exhibit high sensitivity to temperature throughout their entire life cycle. Within the optimal thermal range, the growth and development rates of species, including L. vannamei and Penaeus japonicus, exhibit a positive correlation with water temperature37,38. In this study, recorded aquaculture water temperatures revealed that the average water temperature in the PV fishery model was lower than that in the traditional pond culture model, which may account for the observed differences in the growth performance of L. vannamei between the two models. However, when water temperature exceeds the physiological tolerance threshold of shrimp, their metabolic rate may exceed their assimilation rate. Under such conditions, energy is expended rapidly and cannot be stored, ultimately adversely affecting growth. Moreover, excessively high temperatures can cause tissue damage and disrupt normal physiological functions, leading to growth inhibition and reduced survival rates39. In addition, a large number of research indicates that water temperature affects the antioxidant defense systems of both fish and crustaceans. Adverse water temperature conditions and abrupt temperature fluctuations can induce oxidative stress responses40.
In the present study, the composition and structure of the L. vannamei intestinal microbiota were significantly different between the PV fishery model and the traditional pond culture model. Although the relative intestinal abundance of Proteobacteria, Actinobacteria, Firmicutes, and Bacteroidetes varied between the two culture modes, these phyla consistently dominated the bacterial community. This is similar to the results of studies in Scophthalmus maximus41, hybrid grouper42, Eriocheir sinensis43, Penaeus monodon44 and Macrobrachium rosenbergii45. Under the PV fishery model, reduced light availability caused by shading is likely to suppress primary production, thereby driving a compositional shift in the microbial community toward k-strategists (e.g., Verrucomicrobiota). These bacteria are characterized by slow growth rates and strong competitiveness, typically adapting to relatively stable environments. In contrast, frequent water disturbances of traditional aquaculture models may promote the proliferation of r-strategists (e.g., Proteobacteria), which exhibit high reproductive rates but lower competitive ability46. Our findings revealed a reduction in Aeromonas populations within the intestinal microbiota of L. vannamei under shaded conditions, with certain Aeromonas species known to facilitate host digestion, absorption, and metabolic processes47. Notably, the PV aquaculture system demonstrated decreased abundance of opportunistic pathogens including Aeromonas and Vibrio, consistent with established correlations between these bacterial taxa and environmental conditions in Macrobrachium rosenbergii culture systems48,49,50,51. The nitrogen cycling function of Thioclava and the photosynthetic oxygen production characteristics of Rhodobacter may together improve the microenvironment of the culture water, but further data on dissolved oxygen and ammonia nitrogen levels are needed for support52,53. Although the ecological functions of Vibrio and related genera have been previously documented54, this study is the first to report a negative correlation between their temporal dynamics of Vibrio and the abundance of photosynthetic bacteria (Rhodobacter) in water column under the PV model. This finding suggests that reduced light availability may influence microbial community interactions and implies a potential regulatory mechanism through which light conditions could be manipulated to suppress opportunistic pathogens.
The observed microbial community disparities between PV fishery and traditional pond models may stem from interactive effects of light and temperature parameters. Temperature serves as a principal regulator of microbial metabolic activity and community structure13. In the PV model, comparatively lower water temperatures likely decelerated microbial metabolic rates, thereby favoring k-selected taxa such as Verrucomicrobiota that thrive in stable, low-energy environments. In contrast, elevated temperatures in traditional ponds potentially accelerated microbial turnover, promoting r-selected groups including Proteobacteria. The light-temperature interaction may further manifest through mechanisms such as reduced photosynthetic activity under PV shading, which could suppress light-dependent bacterial populations including Rhodobacter55. Additionally, temperature fluctuations in traditional ponds may induce physiological stress in L. vannamei, indirectly modulating gut microbiota composition through host-mediated pathways.
The identification of Candidatus_Electronema as a key indicator species in the ZG group, coupled with significant β-diversity divergence and predicted functional shifts, suggests that PV shading induces specialized microbial adaptations. These adaptations may involve modifications in nutrient cycling pathways or energy utilization strategies within the intestinal environment56. As dominant phyla, the dynamic equilibrium between Proteobacteria and Firmicutes appears particularly influential in this adaptive process57. The concomitant suppression of Vibrio in ZG systems further implies a trade-off between pathogen defense mechanisms and nutrient acquisition efficiency. Thus, the observed growth retardation during the early culture phase may constitute a transient metabolic expenditure associated with the establishment of a PV-adapted gut microbiome, underscoring the imperative to reconcile microbial community strategies with growth performance optimization in integrated aquaculture systems. Subsequent investigations should prioritize quantifying mechanistic relationships between host growth rate, digestive enzyme activity, and the functional contributions of key microbial taxa to validate this proposed ecological trade-off.
This study demonstrates that the PV fishery model induces significant alterations in the intestinal microbiota of L. vannamei, characterized by increased α-diversity and altered community structure. Although these microbial changes are initially associated with reduced growth rates, they appear to promote the establishment of a more stable and pathogen-resistant gut ecosystem, as indicated by the suppression of Vibrio populations and the enrichment of functional taxa like Verrucomicrobiota58,59. Notably, the observed trade-off between transient growth performance and long-term microbial community stability necessitates a comprehensive evaluation of its implications for system-level productivity and host physiological integrity. The sustained growth attenuation observed in PV-integrated systems may culminate in reduced cumulative biomass production per unit area, thereby constituting a potential economic constraint for large-scale implementation. Furthermore, while microbial community stability may correlate with enhanced disease resilience, the chronic physiological stress associated with suboptimal growth conditions could potentially compromise critical health indicators including immune competence (e.g., antioxidative defense capacity or lysozyme activity) and overall stress responsiveness60. To address this ecological balancing act, prospective investigations should prioritize the development of optimized light regimens through methodologies such as intermittent illumination protocols. Concurrently, systematic evaluations must be conducted to assess the impact of these interventions on host immune function, environmental resilience, and final biomass productivity to ensure operational viability of integrated PV-aquaculture systems61. Collectively, these findings provide mechanistic insights for advancing ecologically sustainable aquaculture practices through strategic manipulation of light-microbiota-host interactions.
This 80-day aquaculture experiment systematically evaluated the effects of photovoltaic shading on L. vannamei. This results showed that the PV fishery breeding model (ZG) exhibited lower water temperatures compared to the traditional pond culture model (CK), leading to reduced growth in body length and weight. Microbial profiling demonstrated substantial declines in potential pathogens (Vibrio and Aeromonas), increased abundance of beneficial taxa (Thioclava and Verrucomicrobiota), and specific enrichment of C._Electronema as a biomarker in the PV model. While 244 core genera with essential ecological functions were shared between both models, the observed reductions in Proteobacteria and Firmicutes abundance under the PV model suggest that light-induced changes in water temperature may modulate shrimp energy allocation though temperature-microbiota interactions. These findings imply that a moderate trade-off growth performance may be compensated by enhanced microbial stability and disease resistance, providing a critical ecological rationale for the adoption of integrated PV fishery breeding model.
The raw sequence data reported in this paper have been deposited in the Genome Sequence Archive in National Genomics Data Center, China National Center for Bioinformation / Beijing Institute of Genomics, Chinese Academy of Sciences (CRA024106) that are publicly accessible at [https://ngdc.cncb.ac.cn/gsa](https:/ngdc.cncb.ac.cn/gsa) .
Jingwen, B. Integrating fishing and light to create a new model of industrial development. Solar Energy, 67 + 69 (2016).
Mengjiao, G. et al. Bacillus subtilis improves immunity and disease resistance in rabbits. Front. Immunol. 8, 354 (2017).
Google Scholar
Duo, H. Integration of fish and light to promote the transformation and upgrading of aquaculture. China Fish. News, 106–108 (2016).
Gilles, B. & Bail, P. Y. L. Does light have an influence on fish growth? Aquaculture 177, 129–152 (1999).
Blaxter, J. H. S. Visual thresholds and spectral sensitivity of herring larvae. J. Exp. Biol. 51, 39–53 (1968).
Article Google Scholar
Mcfarland, W. N. Light in the Sea-Correlations with behaviors of fishes and invertebrates. Am. Zool. 26, 389–401 (1986).
Article Google Scholar
Moss, G. A., Tong, L. J. & Illingworth, J. Effects of light intensity and food density on the growth and survival of early-stage phyllosoma larvae of the rock Lobster Jasus edwardsii. Mar. Freshw. Res. 50, 129–134 (1999).
Article Google Scholar
Wang, F. et al. The effect of light intensity on the growth of Chinese shrimp Fenneropenaeus chinensis. Aquaculture 234, 475–483 (2004).
Article Google Scholar
Gardner, C. & Maguire, G. B. Effect of photoperiod and light intensity on survival, development and cannibalism of larvae of the Australian giant crab Pseudocarcinus gigas (Lamarck). Aquaculture 165, 51–63 (1998).
Article Google Scholar
Mclean, E., Cotter, P. & Thain, C. King, N. Tank color impacts performance of cultured fish. Ribarstvo 2, 43–54 (2008).
Google Scholar
Wei Hui et al. Effects of light intensity on phototaxis, growth, antioxidant and stress of juvenile Gibel carp (Carassius auratus Gibelio). Aquaculture 501, 39–47 (2019).
Article Google Scholar
Sepulveda, J. & Moeller, A. H. The effects of temperature on animal gut microbiomes. Front. Microbiol. 11 https://doi.org/10.3389/fmicb.2020.00384 (2020).
Osadchiy, V., Martin, C. R. & Mayer, E. A. The gut brain axis and the microbimoe: mechanisms and Chinical implications. Clin. Gastroenterol. Hepatol. 17 https://doi.org/10.1016/j.cgh.2018.10.002 (2019).
Li, Y., Bao, N., Kan, Z. & Kang, X. in The 20th anniversary of the Invertebrate Zoology Society and the 55th anniversary of Academician Liu Ruiyu’s engagement in marine science education and research academic symposium.
Ying, J., Shen, W. & Li, P. Study on the influence of water temperature on the intestinal microflora of Penaeus vannamei. Mar. Scienes. 28, 33–36 (2004).
Google Scholar
Kr, M., Sunar, M. C., Topuz, M. & Saripek, M. Thermal acclimation capacity and standard metabolism of the Pacific white shrimp Litopenaeus vannamei (Boone, 1931) at different temperature and salinity combinations. J. Therm. Biol. 112, 103429 (2023).
Article Google Scholar
Dong, X., Wang, J. & Raghavan, V. Impact of microwave processing on the secondary structure, in-vitro protein digestibility and allergenicity of shrimp (Litopenaeus vannamei) proteins-ScienceDirect. Food Chem. 337, 127811 (2021).
Article CAS PubMed Google Scholar
Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naive bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267 (2007).
Article ADS CAS PubMed PubMed Central Google Scholar
Elmar, P. et al. SILVA: a comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB. Nucleic Acids Res. 35, 7188–7196 (2007).
Article Google Scholar
Ondov, B. D., Bergman, N. H. & Phillippy, A. M. Interactive metagenomic visualization in a web browser. BMC Bioinform. 12, 385 (2011).
Article Google Scholar
Chen, H. B. & Boutros, P. C. VennDiagram: a package for the generation of highly-customizable Venn and Euler diagrams in R. BMC Bioinform. 12, 35 (2011).
Article Google Scholar
Conway, J. R., Lex, A. & Gehlenborg, N. UpSetR: an R package for the visualization of intersecting sets and their properties. Bioinformatics 33, 2938–2940 (2017).
Article CAS PubMed PubMed Central Google Scholar
Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods. 7, 335–336 (2010).
Article CAS PubMed PubMed Central Google Scholar
Oksanen, J., Blanchet, F. G., Kindt, R., Legendre, P. & Stevens, W. H. H. : Vegan: Community Ecology Package. R package, version 1.17-4, (2010).
Wickham, H. ggplot2. Wiley Interdisciplinary Reviews: Computational Statistics 3, 180–185 (2011).
Aßhauer, K. P., Wemheuer, B., Daniel, R. & Meinicke, P. Tax4Fun: predicting functional profiles from metagenomic 16S rRNA data. Bioinformatics 31, 2882–2884 (2015).
Article PubMed PubMed Central Google Scholar
Langille et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat. Biotechnol. 31, 814–821 (2013).
Article CAS PubMed PubMed Central Google Scholar
Shen, W. et al. The impact of microplastic and sulfanilamide co-exposure on soil microbiota. Ecotoxicol. Environ. Saf. 292, 117968 (2025).
Article CAS PubMed Google Scholar
Liu, Li & Pengfang, Z. Relationship between photoperiod and plant floral induction. Liaoning Agri. Sci., 26–27 (2004).
Adair, K. L. & Douglas, A. E. Making a microbiome: the many determinants of host-associated microbial community composition. Curr. Opin. Microbiol. 35, 23–29 (2017).
Article PubMed Google Scholar
Wang Xiang, R., Xianyun, S., Xiuzhen, Li, C. & Jian, L. Effects of different photoperiods on growth, molting and glucose metabolism of Marsupenaeus japonicus. Progress Fish. Sci. 41, 66–73. https://doi.org/10.19663/j.issn2095-9869.20190902001 (2020).
Article Google Scholar
Wu Zhixin, C., Xiaoxuan, L., Xiaoling, M. X. & Xiqun, C. Effects of different photoperiods on reproduction and growth of Cherax quadricarinatus. Freshw. Fisheries. 30, 4–5 (2000).
Google Scholar
Shukun, Y. The latest research and application progress of feed protease. Feed Ind. 40, 22–26. https://doi.org/10.13302/j.cnki.fi.2019.10.005 (2019).
Article Google Scholar
Chunlei;, L. & Yutuo, W. Research progress on classi fication and application of amylase. Guangxi Sci. 25, 248–252. https://doi.org/10.13656/j.cnki.gxkx.20180531.002 (2018).
Article Google Scholar
Zishe;, W., Ji;, Z., Junyi;, M. & Rui, S. Research and application progress of lipase. J. Anhui Agri Cult. Sci. 39, 3798–3800. https://doi.org/10.13989/j.cnki.0517-6611.2011.07.004 (2011).
Article Google Scholar
Yanling, T. et al. The influence of light intensity and shelter on habitat and feeding behavior of the spiny Lobster (Panulirus homarus). J. Dalian Ocean. Univ. 40, 382–389. https://doi.org/10.16535/j.cnki.dlhyxb.2024-285 (2025).
Article Google Scholar
Jesus;, P. P. A., M.-P. C., and & G., R. L. The effects of salinity and temperature on the growth and survival rates of juvenile white shrimp, Penaeus vannamei, Boone, 1931. Aquaculture 157, 107–115 (1997).
Coman, G. J., Crocos, P. J. & P, P. N. The effects of temperature on the growth, survival and biomass of different families of juvenile Penaeus japonicus Bate. Aquaculture 214, 185–199 (2002).
Article Google Scholar
Zhiguo;, L., Chengsong;, Z., Yan;, Z., Fuhua;, L. & Jianhai, X. Effects of temperature on embryonic and larval development of the Exopalaemon carinicauda (Holthuis). Repopts 37, 9–16 (2013).
Google Scholar
Bowden, T. J. Modulation of the immune system of fish by their environment. Fish. Shellfish Immunol. 25, 373–383 (2008).
Article CAS PubMed Google Scholar
Li, Y. et al. Effects of dietary glycinin on the growth performance, digestion, intestinal morphology and bacterial community of juvenile turbot, Scophthalmus Maximus L. Aquacultur, 125–133 (2017).
He, Y. et al. Glutamine improves growth and intestinal health in juvenile hybrid groupers fed high-dose glycinin. Fish Shellfish Immunology, 141 (2023).
A, F. H. et al. Sodium butyrate can improve intestinal integrity and immunity in juvenile Chinese mitten crab (Eriocheir sinensis) fed glycinin-ScienceDirect. Fish Shellfish Immunology, 400–411 (2020).
Rungrassamee, W., Klanchui, A., Maibunkaew, S., Chaiyapechara, S. & Karoonuthaisiri, N. Characterization of intestinal bacteria in wild and domesticated adult black tiger shrimp (Penaeus monodon). PLoS ONE. 9, e91853 (2014).
Article ADS PubMed PubMed Central Google Scholar
Jingfeng, Y. The Study on Immune effects, Feeding Physiology and Intestinal Health of Soybean Antigen Protein on Giant Freshwater Prawn (Macrobrachium rosenbergii) (Shanghai Ocean University, 2018).
Chaiyapechara, S., Uengwetwanit, T., Arayamethakorn, S., Bunphimpapha, P. & Rungrassamee, W. Understanding the host-microbe-environment interactions: intestinal microbiota and transcriptomes of black tiger shrimp Penaeus monodon at different salinity levels. Aquaculture 546, 737371 (2021).
Article Google Scholar
Koca, S. B., Yigit, N. Ö. & I, D. B. Effects of enzyme-producing probiotic bacteria isolated from the Gastrointestinal tract of trout on the growth performance, survival, and digestive enzyme activity of rainbow trout fry (Oncorhynchus mykiss). Israeli J. Aquaculture-Bamidgeh. 352, 1–9 (2015).
Google Scholar
Sai, B. V. Antibacterial activity of Allium sativum against multidrug-resistant Vibrio harveyi isolated from black gill–diseased Fenneropenaeus indicus. Aquacult. Int. 19, 531–539 (2011).
Article Google Scholar
Kuebutornye, F. K. A., Abarike, E. D. & Lu, Y. A review on the application of Bacillus as probiotics in aquaculture. Fish Shellfish Immunol. 87, 820–828 (2019).
Article CAS PubMed Google Scholar
Huttenhower, C., Gevers, D., Knight, R., Abubucker, S. & Badger, J. H. Structure, function and diversity of the healthy human Microbiome. Prog. Mol. Biol. Transl. Sci. 191, 53–82 (2012).
Google Scholar
Green, T. J. et al. Simulated marine heat wave alters abundance and structure of Vibrio populations associated with the Pacific oyster resulting in a mass mortality event. Microb. Ecol. 77, 736–747 (2018).
Article ADS PubMed Google Scholar
Sorokin, D. Y. Thioclava Pacifica gen. nov., sp. nov., a novel facultatively autotrophic, marine, sulfur-oxidizing bacterium from a near-shore sulfidic hydrothermal area. Int. J. Syst. Evol. MicroBiol. 55, 1069–1075 (2005).
Article CAS PubMed Google Scholar
Cao Haipeng, Z., Shuying, Y. J. & Jian, A. Isolation, identification and detoxification of a trichlorphon-tolerant Rhodobacter sphaeroides XR12. Acta Hydrobiol. Sin. 44, 59–66 (2020).
Google Scholar
Dai, L. et al. Pathogenicity and transcriptomic exploration of Vibrio fortis in Penaeus monodon. Fish. Shellfish Immunol. 142, 109097 (2023).
Article CAS PubMed Google Scholar
Kolda, A., Gavrilovi, A., Jug-Dujakovi, J., Ljubei, Z. & Kapetanovi, D. Profiling of bacterial assemblages in the marine cage farm environment, with implications on fish, human and ecosystem health. Ecol. Ind. 118, 106785 (2020).
Article Google Scholar
Li, X. M. et al. Gut microbiota contributes to the growth of fast-growing Transgenic common carp (Cyprinus Carpio L). Plos One. 8, 7. https://doi.org/10.1371/journal.pone.0064577 (2013).
Article CAS Google Scholar
Llewellyn et al. Teleost microbiomes: the state of the art in their characterization, manipulation and importance in aquaculture and fisheries. (2014).
Qu Biao et al. Effect offishing light complementaryon plankton in catfish pond. J. Aquaculture. 36, 6–9 (2015).
Google Scholar
Kyle, F., Edwards, M. K., Thomas, Christopher, A. & Elena, K. Phytoplankton growth and the interaction of light and temperature: A synthesis at the species and community level. Limnol. Oceanogr. 61, 1232–1244 (2016).
Article Google Scholar
Liu, X., Sha, Y. Z., Dingkao, R. Q., Zhang, W. & Luo, Y. Z. Interactions between rumen microbes, VFAs, and host genes regulate nutrient absorption and epithelial barrier function during cold season nutritional stress in Tibetan sheep. Front. Microbiol. 11 https://doi.org/10.3389/fmicb.2020.593062 (2020).
Wang, Y. B., Li, J. R. & Lin, J. Probiotics in aquaculture: challenges and outlook. Aquaculture 281, 1–4 (2008).
Article Google Scholar
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This research was supported by Innovation of High Quality Fish Breeding Materials and Methods and Selection of New Varieties (Breeding Research Project) (2021YFYZ0015) and Sichuan Freshwater Fish Innovation Team of the National Modern Agricultural Industrial Technology System (SCCXTD-2025-15). In addition, We would like to thank Tongwei New Energy Co., Ltd. For their financial support in this study.
Fisheries Research Institute, Sichuan Academy of Agricultural Sciences (Sichuan Fisheries Research Institute), Chengdu, Sichuan, China
Zhongmeng Zhao, Han Zhao, Huadong Li, Yuanliang Duan, Zhipeng Huang, Jian Zhou & Qiang Li
Tongwei New Energy Co., Ltd, Chengdu, Sichuan, China
Xingyu Chen & Yongshuang Wang
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Z.Z.M., and L.Q. conceived and designed research. Z.Z.M., Z.H., W.Y.S, and C.X.Y. conducted experiments. Z.Z.M., Z.H., L.H.D., D.Y.L., H.Z.P., Z.L., and Z.J. analyzed data. Z.Z.M., C.X.Y., and L.Q. wrote the manuscript. All authors read and approved the manuscript.
Correspondence to Xingyu Chen or Qiang Li.
The authors declare no competing interests.
All animal handling procedures were approved by the Animal Care and Use Committee of the Fisheries Research Institute, Sichuan Academy of Agricultural Sciences (20220323002 A), following the recommendations in the U.K. Animals (Scientific Procedures) Act, 1986. At the same time, all methods were carried out by relevant guidelines and regulations.
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Zhao, Z., Chen, X., Wang, Y. et al. Effects of the photovoltaic fishery breeding model on intestinal microbiota structure and diversity in Litopenaeus vannamei. Sci Rep 16, 4279 (2026). https://doi.org/10.1038/s41598-025-34429-x
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