Dynamic Succession and Origin of Gut Microbiota During Early-life in White King Pigeon

Dynamic Succession and Origin of Gut Microbiota During Early-life in White King Pigeon | 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 Dynamic Succession and Origin of Gut Microbiota During Early-life in White King Pigeon Yue He, Jie Deng, Jundong He, Bangyuan Wu, Long Zhang, Zihan Wang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7144754/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Oct, 2025 Read the published version in BMC Microbiology → Version 1 posted 10 You are reading this latest preprint version Abstract Background The gut microbiota plays a critical role in host health, yet the dynamic establishment and key influencing factors of the early-life gut microbiota in pigeons remain poorly understood. Methods This study employed 16S rRNA gene sequencing to characterize the spatiotemporal succession of gut microbiota in White King pigeon squabs (Within one week after birth) and quantify contributions from potential sources, including pigeon milk, cloaca, egg components, feed, and environment. Results Revealed a dramatic transition from prenatal to postnatal microbiota: meconium (G0) was dominated by Pseudomonas (19.6%), Enterococcus 14.5%), and Escherichia-Shigella (8.7%), whereas postnatal communities rapidly shifted to a stable composition dominated by Lactobacillus and Limosilactobacillus (Firmicutes) by day 2. Source tracking analysis demonstrated that prenatal colonization primarily originated from albumen (DB) and female cloaca (XZ), contributing 58.5% of G0 microbiota, while pigeon milk (M0) drove 72% of the microbiota in 1-day-old squabs (G1), outcompeting prenatal microbes. Postnatally, microbiota assembly was increasingly driven by previous-stage communities (65.9-9.1%), with minimal environmental input (10.9% by day 7). Conclusions These findings establish the first 48 hours as a critical developmental window for gut microbiota maturation and highlight pigeon milk as the primary driver of early microbial assembly. The study provides a scientific basis for microbial modulation strategies in pigeon farming, including probiotic-supplemented artificial pigeon milk formulation and biosecurity measures to mitigate prenatal pathogen transmission. Squabs Gut microbiota Early establishment Source tracking 16S rRNA sequencing Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background A large number of diverse microbial communities are colonized in the animal gut, which is regarded as the "second genome" of the animal and has an inseparable symbiotic relationship with the host [1]. Gut microbiota profoundly participate in multiple host physiological processes, including nutrient metabolism (e.g., fermentation of dietary fibers into short-chain fatty acids)[2, 3], development and regulation of the immune system (e.g., promoting immune cell differentiation and maturation, maintaining intestinal barrier integrity)[4-6], and defense against pathogen colonization through mechanisms such as competitive exclusion[7, 8]. Additionally, microbiota-derived metabolites serve as key messengers in the gut-brain axis, underpinning neuromodulatory functions [9]. Consequently, the gut microbiota serves as a critical determinant of host health, growth, development, environmental adaptation. Maintaining structural and functional stability of this microbial community is essential for host health, whereas dysbiosis is closely associated with the pathogenesis of various diseases, including inflammatory bowel disease, metabolic syndromes (obesity, diabetes), autoimmune disorders, neuropsychiatric conditions, and increased infection susceptibility[10, 11]. Thus, deciphering the dynamic processes governing the establishment, succession, and ultimate stabilization of gut microbiota during early life, along with identifying key factors shaping this trajectory, has emerged as a cutting-edge research frontier in life sciences [12]. Extensive research indicates that the establishment of the avian gut microbiota commences during the hatching process. Post-hatching, a phase of rapid colonization ensues, with the microbiota tending to reach relative stability within days to weeks. Utilizing high-throughput sequencing, Ding et al. [13]detected substantial microbial communities within chicken embryos at 4 and 19 days of incubation. Our previous research identified abundant microbes present in the meconium of hatchling pigeons[14], from which viable bacteria were successfully isolated and cultured (Unpublished data). Studies in broilers demonstrate that 0-4 days post-hatch represent a period of rapid microbial colonization, 4-10 days are characterized by the rapid proliferation of dominant bacterial groups, and after 10 days old, microbial growth rate declines, leading to stabilization of the microbial composition [15, 16]. In contrast, the gut microbiota of layer hens matures more slowly, typically stabilizing during peak or late lay periods [17]. Research on arctic shorebirds similarly indicates stabilization of the gut microbial structure by 3 days of age [18]. This early rapid colonization phase of the avian gut microbiota likely represents the most plastic period for microbial community assembly, exerting profound implications for subsequent physiological functions, growth performance, and disease resistance in hatchlings [19]. Early gut microbiota colonization is a complex process, where microbes from parents, the environment, and diet are transmitted to the gut of newborn individuals through direct or indirect means. Various intrinsic factors (such as species, genotype, and sex) and extrinsic factors (such as food type, climate conditions, and behavior) may selectively shape the acquired microbiota, resulting in a unique gut microbiota profile for each population or individual [19-22]. Microbiota residing in the maternal oviduct and cloaca can be indirectly transmitted to the embryo via the egg albumen or eggshell [23]. Following hatching, Microbiota from the parental oral cavity, feathers, and skin can be directly or indirectly transferred to the offspring. Research showed that a mere 24-hour-long contact between a hen and newly hatched chickens was long enough for transfer of hen gut microbiota to chickens [24]. The diet is the main driving force in shaping the gut microbiota. The composition of gut microbiota in animals with different feeding habits varies significantly [25], and the microbes in food can also directly colonize the gut [14]. Furthermore, the living or incubation environment continuously introduces new microbial members to the developing bird [26]. Although the sources of early gut microbiota colonization are well understood, the contribution of these factors to early gut microbiota establishment remains unclear, especially in the case of pigeons, where relevant studies are still lacking. Previous research by our team revealed that the gut microbial community structure in squabs stabilizes within the first week after birth. Pigeon milk, serving as the sole nutritional source for squabs, plays a crucial role in establishing their gut microbiota [14]. However, the critical window for the maturation of their gut microbial composition remains unclear, and the influence of other key factors affecting microbial colonization is yet to be elucidated. Therefore, this study employs 16S rRNA gene sequencing to characterize the dynamic changes in the gut microbiota of squabs during the first week post-hatching and to investigate the influence of the microbiota in the cloaca of parent pigeons, pigeon milk, feed, eggshell, albumen, etc. on the early development process of the squab gut microbiota. Through this research, we aim to identify the critical time window for gut microbiota development in pigeons, evaluate the contribution of various factors on the gut microbial community structure in squabs, and thus provide novel data to support theoretical research in pigeon gut microbiota, alongside a scientific basis for modulating microbial communities in practical pigeon farming. Methods Animal model and sampling The samples for this study were obtained from Yingshan Fucheng Meat Pigeon Breeding Professional Cooperative (Nanchong, Sichuan, China). Parent pigeons (2-year-old, peak-laying period) were housed in an individual cage under standardized conditions and provided with three meals per day, along with constant access to water and natural light. Their egg-laying cycles remained stable throughout the study. In this study, fecal samples were collected from squabs on day 0, day 1, day 2, day 3, day 4, day 5, and day 7 post-hatch, designated as G0, G1, G2, G3, G4, G5, and G7, respectively, with seven replicates for each group. The sample collection method for all fecal samples followed the procedure outlined in a previous study [14]. Before rearing squabs, one egg was collected from each breeding pigeon in every group. The egg shell and albumen were aseptically separated, and the samples were designated as DK and DB, respectively. Additionally, a sterile swab was gently rotated within the cloaca of the female pigeon and held for 5 seconds to collect a cloacal swab sample, designated as XZ. A total of 25 g of feed (including compound feed, corn, peas, sorghum, wheat, etc.) was mixed with 50 ml of drinking water from the cage. After shaking for 1 hour and standing for 15 minutes, the supernatant was collected and centrifuged at 12,000 rpm for 10 minutes. The enriched precipitate, representing microbiota from the feed and water, was designated as H. Health sand samples (T) were also collected. Previous studies have shown that microbiota in pigeon colostrum have a significant impact on the establishment of gut microbiota in squabs. Therefore, this experiment will reference colostrum-related data (designated as M0) from previous studies for subsequent analyses [14]. DNA extraction and 16S rRNA gene amplicon sequencing Microbial genomic DNA extraction was carried out using the OMEGA Soil DNA Extraction Kit (D5625 - 01; Omega Bio-tek, Norcross, GA, USA), strictly adhering to the manufacturer's provided protocols. Subsequently, the V3-V4 hypervariable region of the 16S rRNA gene was amplified via polymerase chain reaction (PCR) from the extracted microbial genomic DNA templates. The amplification employed the forward primer 338F (5'-ACTCCTACGGGAGGCAGCA-3') and the reverse primer 806R (5'-GGACTACHVGGGTWTCTAAT-3').Each PCR reaction was configured with a total volume of 25 μL, containing 5 μL of 5× buffer, 0.25 μL of Fast Pfu DNA polymerase (5 U/μL), 2 μL of dNTPs (2.5 mM), 1 μL each of the forward and reverse primers (10 μM), 1 μL of template DNA, and 14.75 μL of nuclease-free water (ddH2O). The thermal cycling protocol started with an initial denaturation at 98°C for 5 minutes, followed by 25 cycles of denaturation at 98°C for 30 seconds, annealing at 53°C for 30 seconds, and extension at 72°C for 45 seconds. The process concluded with a final extension step at 72°C for 5 minutes. Post-amplification, the PCR products underwent purification and individual quantification. Subsequently, the amplicons were pooled in equimolar concentrations and subjected to 2×250 bp paired-end sequencing on the Illumina NovaSeq platform. Sequence analysis The generated sequencing data were processed using QIIME 2, integrating the DADA2 algorithm for amplicon sequence variant (ASV) inference [27]. Quality control procedures included adapter trimming, quality filtering, error correction, paired-end read merging, and chimera removal. Taxonomic classification of ASVs was performed using the classify-sklearn naive Bayes classifier [28] against the Silva 138.1 database. Alpha diversity indices (Chao1, Shannon and Goods_coverage) and beta diversity metrics (Bray-Curtis dissimilarity, weighted UniFrac) were calculated following rarefaction to even sequencing depth. Statistical analyses were conducted in IBM SPSS Statistics v19. Permutational multivariate analysis of variance (PERMANOVA) tested group differences, and Spearman's correlation assessed variable relationships. A two - tailed test was used to determine the statistical significance of the correlations, with a significance level set at P< 0.05. In addition, Source Tracker analysis was carried out using R software (Version 4.5.1) to infer the contributions of potential source communities to the target microbial communities [29]. The analysis was performed using open-source code obtained from the GitHub repository (https://github.com/danknights/sourcetracker), with appropriate modifications made to suit the specific requirements of this study. Results Sequencing and species annotation Sequencing of the 85 samples generated a total of 8,023,707 sequences. After quality control, denoising, assembly, and chimera removal, 5,411,339 high-quality sequences were obtained, with an average read length of 425bp, and a total of 27,894 ASVs were matched. The pigeon milk group (M0) exhibited the highest number of ASVs, whereas the albumen group (DB) showed the lowest (Figure 1), indicating higher microbial diversity in pigeon milk and simpler composition in albumen. As shown in Figure 1, the proportion of ASVs annotated to the genus level exceeded 80% in most groups, whereas fewer ASVs were annotated in the meconium (G0), feed & water (H), and health sand (T) groups, suggesting abundant unknown microbes requiring further identification. The number of identified taxa at each taxonomic level for each group is presented in Table 1. The T group exhibited the highest number of taxa identified across all taxonomic levels, followed by the H, G0, and eggshell (DK) groups. At the family and genus levels, the number of species identified in groups G0-G7 showed a trend of initial decrease followed by an increase, peaking at the lowest point at 2 days of age (G2), after which microbial diversity gradually increased. Table 1 Number of identified taxa at each taxonomic level G0 G1 G2 G3 G4 G5 G7 M0 XZ DK DB H T Phylum 16 10 5 7 5 16 16 16 13 17 12 18 23 Class 33 15 11 12 11 27 25 22 21 35 19 36 60 Order 81 42 27 33 32 65 64 48 47 76 50 105 151 Family 148 77 51 61 61 102 102 83 88 129 75 178 264 Genus 281 144 94 105 112 160 168 142 160 240 106 325 510 Alpha diversity analysis Alpha diversity analysis revealed that M0 exhibited the highest Chao1 and Shannon indices, which were significantly higher than those of DB and female cloaca (XZ) groups (P< 0.05). This indicates that the group of M0 possessed the highest microbial richness and diversity. Notably, the M0 had the lowest Good's coverage index, suggesting that a small proportion of microbes in pigeon milk might remain undetected. Future studies may need to increase sample size, enhance sequencing depth, or optimize the identification strategy to address this. Conversely, the DB group showed the lowest Chao1 and Shannon indices, while its Good's coverage index was close to 1. This indicates that the microbial community within the albumen was characterized by low species richness and diversity, and that the current experimental approach nearly completely characterized its microbial composition. Among age groups, G0 showed lower richness/diversity than G1-G7, though not statistically significant, while other age groups exhibited minimal differences in diversity metrics. Characteristics of Gut microbiota in early-stage squabs Firmicutes, Actinobacteria, and Proteobacteria constituted the dominant bacterial phyla in the early-stage gut microbiota of squabs, collectively accounting for over 97% of the total microbial abundance. In fecal samples, Firmicutes abundance increased from 21.1% at hatching to 81.3% at day 7; Actinobacteriota peaked at 21.0% on day 2 and dropped to 7.5% on day 3; Proteobacteria decreased from 66.1% at hatching to 0.1% on day 2. At the genus level, the meconium microbiota displayed distinct compositional differences compared to other age groups. The meconium was dominated by Pseudomonas (19.6%), Enterococcus (14.5%), Escherichia-Shigella (8.7%), and Bifidobacterium (3.7%). In contrast, postnatal stages were dominated by Lactobacillus , Limosilactobacillus , Aeriscardovia , and Turicibacter , with Lactobacillus representing the most predominant taxon. The abundances of Lactobacillus , Limosilactobacillus , and Aeriscardovia showed initial decreases followed by subsequent increases. Turicibacter displayed an abrupt abundance surge at day 3, followed by a similar decrease-increase pattern. Figure 3C illustrates the dynamic changes in relative abundance of the top 40 genera across developmental timepoints. Spearman's correlation analysis between microbial taxa and age (Figure 3D) identified Erysipelotrichaceae_UCG-006 and Solobacterium as exhibiting highly significant positive correlations with age (P < 0.001). Conversely, Staphylococcus , Ralstonia , and Chelativorans demonstrated significant negative correlations with advancing age (P < 0.05). We then applied linear discriminant analysis effect size (LEfSe, LDA threshold = 3) to identify robust significantly different abundant taxa across groups. The analysis revealed that group G0 exhibited the highest number of significantly different genera (7 in total), including Pseudomonas , Enterobacter , Acinetobacter , Faecalibacterium , Bacteroides , Asticcacaulis , and Prevotell a. In group G1, three genera were found to be significantly different abundant than in other groups: Lactobacillus , Limosilactobacillus , and Aeriscardovia , among which Lactobacillus and Limosilactobacillus are the most abundant probiotics genera in the gut microbiota. A single significantly different abundant genera was identified in each of groups G2, G4, G5, and G7: Atopobium , Ligilactobacillus , Corynebacterium , and Dubosiella , respectively. No significantly different abundant genera were detected in group G3. The relatively low number of differentially abundant taxa in groups G2 to G7 suggests that their microbial community structures are relatively similar. We further analyzed the group-specific taxa among microbes with a relative abundance greater than 0.01% at the genus level. In meconium samples, a total of 155 genera surpassed the 0.01% abundance threshold, of which 133 were unique to meconium, while only 11 genera were consistently detected in the feces of all age groups. This indicates that only a small fraction of meconium microbes persist in the intestine with age, whereas most disappear during subsequent development and are replaced by new microbes. In group G1, only 11 unique genera were identified. The numbers of unique genera in groups G2 to G7 were 2, 1, 5, 2, and 4, respectively. A total of 29 genera were shared among groups G2 to G7, collectively accounting for 95% of the relative abundance in these samples. In contrast, these 29 genera represented only 29% of the total relative abundance in G0 but accounted for 97% in G1. These findings suggest that the microbial composition undergoes a dramatic shift at day 1, with unique species became fewer starting from day 2 onward and intestinal microbiota composition tending to stabilize. Inter-group difference analysis (table S2) showed that meconium was extremely significantly different from all other groups. The G1 also showed extremely significant differences from all other groups. The G2 group only had significant differences from G7 group, while there were no significant differences among groups G3-G7. The clustering heatmap (figure 3C) of the top 40 dominant genera at the genus level showed that the G0 group was the farthest from other age groups, followed by the G1 and G2groups, while the groups G3-G7 were relatively clustered, indicating that the composition of the microbes gradually tended to be similar with age. Based on the Bray-Curtis distance matrix, non-metric multidimensional scaling (NMDS) analysis showed that the G0 group was clearly separated from other groups, while the distances between other age groups were relatively small. The results of multiple analyses showed that meconium had a unique microbial composition, and as age progresses, the similarity of the microbial structure between groups gradually increased. This reflects a rapid change in the gut microbiota during early development, followed by a stabilization phase. This transition is the result of a combination of various factors. Microbial correlation analysis To investigate the relationship of microbiota in potential factors (XZ, M0, DB, DK, H, T) on the gut microbiota in early-stage squabs, we conducted inter-group differential analysis between these factors and the microbiota at different age groups. Additionally, we performed Spearman's correlation analysis on the shared microbial taxa at the genus level. Since the squabs no longer had contact with the eggshell and albumen after hatching, these two factors were not included in the analysis of relationship between factors and post-hatch gut microbiota. Furthermore, as the squabs had no contact with pigeon milk before hatching, pigeon milk was also excluded from the analysis of relationship between factors and meconium microbiota. The results of the inter-group differential analysis showed that G0 differed significantly from all groups except DB, while G1-G7 differed from all factors. To further explore the relationship between the gut microbiota of squabs and the factor groups, we combined the data from the five groups (G2-G7) with relatively small differences, named GN, and then performed an NMDS analysis. The NMDS analysis results were similar to the inter-group differential analysis. The G0 group was closest to the DB group, while the group of G1 to G7 were closest to the M0 group. These findings suggest that the gut microbiota composition before hatching may be more influenced by the microbes present in the albumen, while the gut microbiota composition after hatching may be more influenced by the microbes in pigeon milk. Spearman's correlation analysis of shared microbes at the genus level revealed that the number of shared microbes among G1-G7 ranged from 62 to 93, with all correlation coefficients > 0.7 (P<0.001). In contrast, the number of shared microbes between G0 and other age groups ranged from 41 to 79, with only G1 showing a significant moderate correlation, while correlations with other age groups did not reach significance. M0 exhibited moderate correlations (0.449-0.595, P<0.001) with the group of G1-G7. The correlation coefficients between XZ and the group of squabs ranged from 0.490 to 0.668 (P<0.001). The H showed only a significant weak correlation with G7. The group of T exhibited significant or highly significant weak correlations with all age groups. Notably, G0 showed weak correlations with DB, DK, and H groups, but no statistically significant correlations with XZ or T. Furthermore, we further screened for shared dominant genera with relative abundances >0.01% across groups and performed Spearman's correlation analysis. The analysis showed that the number of shared dominant genera among G1-G7 ranged from 25 to 39, with all correlation coefficients >0.7 (P<0.001), consistent with previous findings. Meconium had fewer shared dominant microbes with G1-G7, ranging from 12 to 16, and no significant correlations were observed. The number of shared dominant genera between M0 and the group of G1 to G7 ranged from 16 to 20, with strong positive correlations, particularly with G1, where the correlation coefficient >0.9 (P<0.001). The correlations between XZ and all age groups were lower compared to the previous analysis, even with no significant correlation observed between with the group of G1, G3, and G6. The group of H had a strong negative correlation with G7. The group of Tshowed no significant correlations with the group of G1-G7. Notably, no statistically significant correlations were observed between any of the factors and meconium. Microbial Source Tracking Analysis We performed source tracking analysis on the microbiota of groups G0-G7 using SourceTracker (figure6). The analysis showed that the microbiota in G0 mainly originated from XZ and DB, with fewer contributions from DK, H, and T, indicating that XZ and DB had the greatest impact on prenatal microbial colonization. However, 58.5% of the microbiota in G0 had unknown sources, suggesting that the origins of prenatal colonizing microbiota require further exploration. Postnatally, the gut microbiota composition of squabs at each postnatal stage evolved based on the previous stage. Therefore, when conducting source tracking analysis of the postnatal gut microbiota, the previous stage was always considered as an influencing factor for the subsequent stage. The results showed that 72% of the microbiota in G1 originated from M0, 9.6% from XZ, and extremely little from G0. For groups G2-G7, the microbiota primarily originated from the previous stage (65.9%-89.1%), followed by M0 and XZ. Contributions from H and T remained limited throughout. Notably, the proportion of microbiota from external sources in the squabs' gut gradually decreased over time, dropping to only 10.9% by day 7. Discussion Dynamic Succession of Gut Microbiota Early-stage in Squabs Multiple studies have demonstrated that the gut microbiota composition of squabs undergoes substantial restructuring during the first week post-hatching [14, 31]. This study reveals the spatiotemporal dynamics of gut microbiota in White King pigeon squabs (0–7 days post-hatch) using 16S rRNA gene sequencing. Our findings revealed that the microbial composition of meconium was substantially distinct from that of all postnatal stages, with the greatest intergroup sample Bray-Curtis distances and a limited number of shared microbial taxa, indicating that most prenatally colonized microbes had been replaced by new colonizers within the first day after hatching. Consequently, the contribution of the prenatally established microbiota to the composition of the postnatal gut microbiota appears minimal. The fecal microbial composition of broilers at 0 days old differs most significantly from that at other ages [16], which is similar to the results of this study. Although the primary dominant bacterial taxa were already established squabs by day 1 post-hatching, the 1-day-old group still harbored some unique microbial taxa, showing distinct compositional differences from subsequent age groups. In contrast, from days 2 to 7, the gut microbiota composition showed minimal changes, with fewer differential species and a lower number of unique species, leading to closer sample distances between the groups. Multiple analytical approaches confirmed that the gut microbial composition had approached maturity by day 2 post-hatching. Studies have demonstrated that the maturation time of early gut microbiota varies significantly across different species and even among different strains. The gut microbiota of Arctic shorebirds (including Calidris alpina and Calidris canutus ) begins to mature by 3 days of age [22]; for crested ibises, this process takes approximately 44 days[32]. Broilers typically have their intestinal microbiota mature within about 10 days [16], while the maturation of gut microbiota in layer hens is slower , even not being achieved until the laying period [17]. This rapid succession (48-hour maturation window) is comparable to broilers and Arctic shorebirds but occurs much earlier than in crested ibises or layer hens. Chicks inoculated with a developed donor microbiota exhibit less variation in gut microbiota composition with age, and their gut microbiota composition is more mature [12, 33]. It is possible that the rapid maturation of the gut microbiota in squabs is due to the strong correlation between the microbiota in pigeon milk and the gut microbiota. Key Microbiota Taxa and Their Functional Implications Within the meconium microbial community, Pseudomonas , Enterococcus , and Escherichia-Shigella represent the dominant taxa, aligning with the dominant bacterial genera identified in the oviduct [23, 34]. Among these, Enterococcus and Escherichia-Shigella are recognized potential pathogens, while Pseudomonas and Escherichia-Shigella also dominate the pre-hatch microbiota of broilers, undergoing rapid replacement by newly established taxa—a pattern mirrored in this study [16, 35]. The stabilized gut microbiota was dominated by Firmicutes, with the most abundant genera being Lactobacillus and Limosilactobacillus , consistent with previous findings [14]. Lactobacillus and Limosilactobacillus facilitate carbohydrate fermentation, acidifies the gut, and inhibits pathogen colonization [36]. Their critical roles extend to maintaining gut microbial homeostasis, enhancing immune function, facilitating nutrient absorption, and mitigating inflammation—attributes that underpin their widespread use in probiotic applications [37-39]. Spearman's correlation analysis showed positive associations between Lactobacillus abundance and age, while potential pathogens ( Staphylococcus , Ralstonia ) negatively correlated with age, indicating a possible co-evolution with the host immune system[40] Sources of Prenatal and Postnatal Microbiota Colonization To elucidate the origin of intestinal microbiota in squabs, we performed differential analysis, analysis of shared microbes and their correlations, as well as microbial source tracking analysis between the gut microbiota of squabs at different ages and microbes from various potential sources. Our analyses revealed that the microbes in meconium exhibited the smallest difference from those in albumen, and showed a statistically significant weak correlations with the shared microbes in albumen and eggshell. However, there was no statistically significant correlation among the dominant shared microbes with an abundance greater than 0.1%. This suggests that rare microbial taxa within the albumen and eggshell may exert a more substantial influence on prenatal colonization than dominant species. Source racker analysis further indicated that the prenatally colonizing microbiota primarily originated from the albumen and the female cloaca, with a more limited contribution from the eggshell. Nevertheless, microbes in the albumen originate from the magnum, whereas the eggshell is formed in the uterus, and the microbes in the eggshell are closely related to the uterus. The magnum, uterus, and cloaca are interconnected and as the parts of the reproductive tract, leading to a certain degree of homology among their microbial communities [23, 34]. Previous studies have demonstrated that the microbiota in the cecum of chicken embryos may be derived from the oviductal microbiota, which is transmitted via albumen [23]. There is also study demonstrating that eggshells play an important role in the development of the chicken gut microbiota, especially in the jejunum and ileum [41]. Integrating the results of multiple analyses, and the research results of predecessors, we conclude that microbes in albumen are the most important source of prenatally colonized microbes, and that microbes in the cloaca and eggshell are also indispensable sources. The gut microbiota of newborns is primarily acquired from parents through vertical transmission and from the environment through horizontal transmission. A study has shown that just 24 hours of contact between a hen and newly hatched chicks is enough for the hen's gut microbiota to be transferred to the chicks [24]. The gut microbial community of zebra finch chicks tends to converge with that of their rearing parents, with the parental oral cavity/crop microbiota making a high contribution to the early gut microbiota of the chicks [42]. The environment, as a huge microbial pool, also provides a rich microbial library for the establishment of the gut microbiota. As an environment where newborns live together with their parents, the nest is an important driver of the assembly of the neonatal microbiota [43, 44]. Source tracking analysis of the gut microbiota in 1-day-old squabs revealed that crop milk contributed substantially to colonization, accounting for 72% of the microbiota. In contrast, only a minimal proportion of the prenatally established microbiota persisted at this stage. This finding underscores the critical role of pigeon milk in establishing the early-stage microbial community in squabs, while also indicating that prenatally acquired microbes are rapidly displaced in the squab's gut shortly after hatching. From day 2 onwards, the number of newly colonized microbial species in the squab gut gradually decreased, with only 10.9% of the microbiota originating from external environmental sources y day 7. Pigeon milk exerts a significant impact on the gut microbiota composition of 1-day-old squabs, while its influence on the gut microbiota of squabs at other ages weakens gradually. Nevertheless, the cloacal microbiota of female pigeons contributes to the gut microbiota of squabs across all age groups. Furthermore, multiple analytical approaches consistently demonstrated that although microorganisms present in diet (feed, drinking water, grit) rarely successfully colonized the squab gut. Similar results have also been obtained from the traceability analysis of the gut microbiota in zebra finches [42]. Conclusion This study systematically characterizes the dynamic establishment and microbial sources of the gut microbiota in White King pigeon squabs (0-7 days post-hatch) using 16S rRNA gene sequencing. The prenatal meconium (G0) harbored a distinct community dominated by Pseudomonas (19.6%), Enterococcus (14.5%), and Escherichia-Shigella (8.7%), which were rapidly replaced by pigeon milk-derived probiotics within 24 hours post-hatch. By day 2, the gut microbiota tended to stabilize, with Lactobacillus and Limosilactobacillus from Firmicutes accounting for >80% of the community. This rapid succession highlights the first 48 hours as a critical window for microbiota maturation, with minimal persistence of prenatal microbes in postnatal stages. Source tracking analysis revealed that prenatal colonization primarily originated from albumen (DB) and the female cloaca (XZ), contributing 58.5% of G0 microbiota. Postnatally, pigeon milk (M0) drove 72% of the G1 microbiota, surpassing the negligible contribution from G0 (1.2%). From day 2 onward, microbiota composition was sustained by previous-stage communities (65.9–89.1%), with diminishing external inputs (10.9% by day 7). These results confirm that maternal-derived microbes (via egg components and pigeon milk) are the primary drivers of early gut colonization, while environmental factors (feed, health sand) play a minor role. The rapid replacement of potential pathogens (e.g., Escherichia-Shigella ) by probiotics within 48 hours underscores the protective role of pigeon milk in establishing a healthy gut ecosystem. This study provides a scientific basis for developing probiotic-supplemented artificial pigeon milk formulations to mimic natural colonization patterns. Additionally, targeting the prenatal microbial sources (e.g., eggshell and cloacal disinfection) may mitigate pathogen transmission risks, enhancing squab health and productivity in commercial farming. In summary, this research elucidates the spatiotemporal dynamics and maternal origins of the pigeon gut microbiota, establishing a framework for translational applications in avian microbiome management. Declarations Acknowledgements Not applicable. Funding This project received support from multiple funding sources, including the Natural Science Foundation of Sichuan Province, grant number 2023YFN0032, Innovation Team Funds of China West Normal University, grant number KCXTD2024-5. Data availability The datasets generated during the current study are available in NCBI repository with BioProject ID PRJNA1288493. This is the accession numbers of the datasets SUB15436841. Ethics approval and consent participate The experiment was performed in accordance with the Regulations for the Administration of Affairs Concerning Experimental Animals (Ministry of Science and Technology, Beijing, China, revised in June 2004) and was approved by the Institutional Animal Care and Use Committee of China West Normal University, Sichuan, China (No. 2025LLSC0065). Consent for publication Not applicable Competing interests The authors declare no competing interests. Author Contributions Xiaoqin Xu, conceived research; all contributed to the study design, Jundong He, Bangyuan Wu, Long Zhang and Li Liu collected data. Yue He conducted analyses and wrote the paper. All participated in revisions and read and approved the final manuscript. Author details 1 Key Laboratory of Southwest China Wildlife Resources Conservation (Ministry of Education), China West Normal University, Nanchong 637009, P. R. China 2 Sichuan Wildlife Rehabilitation and Breeding Research Center, China West Normal University, Nanchong 637002, P. R. China 3 Institute of Ecology, China West Normal University, Nanchong 637002, P. R. China 4 College of Life Sciences, China West Normal University, Nanchong 637009, P. R. China 5 Yingshan Fucheng Meat Pigeon Breeding Professional Cooperative, Nanchong 637770, Sichuan, China 6 Agricultural Technique Promotion Station of Nanchong, Nanchong 637002, P. R. 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Dempsey E, Corr SC: Lactobacillus spp. for Gastrointestinal Health: Current and Future Perspectives. FRONT IMMUNOL 2022, 13:840245. http://doi.org/ 10.3389/fimmu.2022.840245. Lee AH, Rodriguez JD, Meisel M: Limosilactobacillus reuteri - a probiotic gut commensal with contextual impact on immunity. GUT MICROBES 2025, 17(1):2451088. http://doi.org/ 10.1080/19490976.2025.2451088. Abuqwider J, Altamimi M, Mauriello G: Limosilactobacillus reuteri in Health and Disease. MICROORGANISMS 2022, 10(3). http://doi.org/ 10.3390/microorganisms10030522. El Kafsi H, Gorochov G, Larsen M: La génétique de l’hôte influe sur les écosystèmes microbiens par l’intermédiaire du système immunitaire de l’hôte. Biologie Aujourd'hui 2017, 211(1):39-49. http://doi.org/ 10.1051/jbio/2017010. Maki JJ, Bobeck EA, Sylte MJ, Looft T: Eggshell and environmental bacteria contribute to the intestinal microbiota of growing chickens. J ANIM SCI BIOTECHNO 2020, 11:60. http://doi.org/10.1186/s40104-020-00459-w. Chen CY, Chen CK, Chen YY, Fang A, Shaw GT, Hung CM, Wang D: Maternal gut microbes shape the early-life assembly of gut microbiota in passerine chicks via nests. MICROBIOME 2020, 8(1):129. http://doi.org/ 10.1186/s40168-020-00896-9. Campos-Cerda F, Bohannan B: The Nidobiome: A Framework for Understanding Microbiome Assembly in Neonates. TRENDS ECOL EVOL 2020, 35(7):573-582. http://doi.org/ 10.1016/j.tree.2020.03.007. Teyssier A, Lens L, Matthysen E, White J: Dynamics of Gut Microbiota Diversity During the Early Development of an Avian Host: Evidence From a Cross-Foster Experiment. FRONT MICROBIOL 2018, 9:1524. http://doi.org/ 10.3389/fmicb.2018.01524. Additional Declarations No competing interests reported. Supplementary Files tableS.xlsx Table S1. Inter-group difference analysis of Gut Microbes in Early-stage Squabs Table S2. Inter-group difference analysis of Microbes in Early-stage Squabs and Factors Cite Share Download PDF Status: Published Journal Publication published 29 Oct, 2025 Read the published version in BMC Microbiology → Version 1 posted Editorial decision: Revision requested 19 Sep, 2025 Reviews received at journal 18 Sep, 2025 Reviewers agreed at journal 15 Sep, 2025 Reviews received at journal 25 Aug, 2025 Reviewers agreed at journal 20 Aug, 2025 Reviewers invited by journal 12 Aug, 2025 Editor assigned by journal 31 Jul, 2025 Editor invited by journal 25 Jul, 2025 Submission checks completed at journal 24 Jul, 2025 First submitted to journal 24 Jul, 2025 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. 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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-7144754","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":501996557,"identity":"54539836-1d86-4783-a442-c7b26cf50fe0","order_by":0,"name":"Yue He","email":"","orcid":"","institution":"China West Normal University","correspondingAuthor":false,"prefix":"","firstName":"Yue","middleName":"","lastName":"He","suffix":""},{"id":501996558,"identity":"b9b71ee3-5f19-430c-89b3-f6ee02874bab","order_by":1,"name":"Jie Deng","email":"","orcid":"","institution":"China West Normal University","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Deng","suffix":""},{"id":501996559,"identity":"8c3119e3-72d7-430c-b1a0-90f3f27b8cba","order_by":2,"name":"Jundong He","email":"","orcid":"","institution":"China West Normal University","correspondingAuthor":false,"prefix":"","firstName":"Jundong","middleName":"","lastName":"He","suffix":""},{"id":501996560,"identity":"24d237d7-98c0-42f0-9d64-61f895d403d7","order_by":3,"name":"Bangyuan Wu","email":"","orcid":"","institution":"China West Normal University","correspondingAuthor":false,"prefix":"","firstName":"Bangyuan","middleName":"","lastName":"Wu","suffix":""},{"id":501996561,"identity":"c5470ab7-aff1-49f9-ac7f-4ed6b2bcdc7a","order_by":4,"name":"Long Zhang","email":"","orcid":"","institution":"China West Normal University","correspondingAuthor":false,"prefix":"","firstName":"Long","middleName":"","lastName":"Zhang","suffix":""},{"id":501996562,"identity":"4c616e33-f185-40c7-b5b8-a5d1d38bac40","order_by":5,"name":"Zihan Wang","email":"","orcid":"","institution":"China West Normal University","correspondingAuthor":false,"prefix":"","firstName":"Zihan","middleName":"","lastName":"Wang","suffix":""},{"id":501996563,"identity":"52d0a885-927b-400c-88bb-ac959d23cfb2","order_by":6,"name":"Li Liu","email":"","orcid":"","institution":"Yingshan Fucheng Meat Pigeon Breeding Professional Cooperative","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Liu","suffix":""},{"id":501996564,"identity":"ca3926ca-930f-403a-8e62-6c46a5b428ac","order_by":7,"name":"Hui Liu","email":"","orcid":"","institution":"Yingshan Fucheng Meat Pigeon Breeding Professional Cooperative","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Liu","suffix":""},{"id":501996565,"identity":"2e258bb1-ae54-4928-9321-ad62b00de914","order_by":8,"name":"Xiaoqin Xu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYJCCA0DMw8De2PggocKGFC08h5sNHpxJI8UuifQ2yYdthwgrNLiRY3jgR4W1jDlDYltFAtsBBv727gS8WiRnpCUc7DmTzmPZcLDtRgLPHQaJM2c34NXCL5F84ABv22Eeg4ONQC0SzxgMJHLxa2GTSGw4+Bek5TBjW0GCwWHCWkC2HAbbcoyxjSEhgQgtkj3PEg7LAP1icIaxWSLhQBoPQb8YHM8x/vimwtre4P7zhx9//rOR42/vxa8FCpjhLB5ilKNqGQWjYBSMglGAAQDKuk3clrhPwwAAAABJRU5ErkJggg==","orcid":"","institution":"China West Normal University","correspondingAuthor":true,"prefix":"","firstName":"Xiaoqin","middleName":"","lastName":"Xu","suffix":""}],"badges":[],"createdAt":"2025-07-17 04:23:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7144754/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7144754/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12866-025-04449-8","type":"published","date":"2025-10-29T15:56:54+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":89467231,"identity":"4379d7bb-d9f0-4f0c-a803-b8557480b4d0","added_by":"auto","created_at":"2025-08-20 08:49:46","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":863795,"visible":true,"origin":"","legend":"\u003cp\u003eTaxonomic annotation of species in each group. The abscissa represents each group, and the ordinate represents the number of ASVs annotated to the phylum, class, order, family, and genus levels. G0, meconium; G1, 1 days of age; G2, 2 days of age; G3, 3 days of age; G4, 4 days of age; G5, 5 days of age; G7, 7 days of age; M0, pigeon milk; XZ, cloaca; DK, eggshell; DB, albumen; H, feed \u0026amp; water; T, health sand.\u003c/p\u003e","description":"","filename":"figure1.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7144754/v1/de5896d3c2e72abf036c2b4b.jpg"},{"id":89467232,"identity":"179d66f3-37d7-4d8b-b2df-c5ad64a04d65","added_by":"auto","created_at":"2025-08-20 08:49:46","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1667262,"visible":true,"origin":"","legend":"\u003cp\u003eAlpha diversity analysis by Chao1, Shannon and Good’s coverage index. *, P \u0026lt; 0.05; **, P \u0026lt; 0.01; ***, P \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"figure2.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7144754/v1/aca7cd1cb5e53aa500a1bda9.jpg"},{"id":89468108,"identity":"eaba2690-6188-492e-a7f8-159cf3315375","added_by":"auto","created_at":"2025-08-20 08:57:46","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":5686648,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Relative abundances of microbial communities at the phylum level. (B) Relative abundances of microbial communities at the genus level. (C) The relative abundances of the top 40 genera in different age-day groups. score = - log10(Relative abundance + 1e-10). (D) Spearman's correlation analysis between microbial taxa and age. ρ represents the Spearman's correlation coefficient, ρ ∈ [-1, 1], *, P \u0026lt; 0.05; **, P \u0026lt; 0.01; ***, P \u0026lt; 0.001. (E) Linear discriminant analysis effect size (LEfSe) among the age groups. Prefix meaning: p, pylum; c, Class; o, oder；f, fmily; g, genus.\u003c/p\u003e","description":"","filename":"figure3.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7144754/v1/033ca58e36fa3d90c9352845.jpg"},{"id":89467234,"identity":"e2fb18f6-2c29-4dda-aa3d-be113d37ed11","added_by":"auto","created_at":"2025-08-20 08:49:46","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1751945,"visible":true,"origin":"","legend":"\u003cp\u003e(A) The UpSet diagram of each age group. The height of the blue columns indicates the number of genera, with individual red dots representing the presence of unique genera and connected red dots denoting shared genera. (B) The non-metric multidimensional scaling (NMDS) analysis of the age groups. (C) NMDS analysis of the age and factors groups.\u003c/p\u003e","description":"","filename":"figure4.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7144754/v1/03f1e8fd9975163d346ac83a.jpg"},{"id":89468109,"identity":"019783fb-ba4c-4368-8854-3aaaec5304f6","added_by":"auto","created_at":"2025-08-20 08:57:46","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1692438,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Spearman's correlation analysis of shared genera. (B) Spearman's correlation analysis of shared dominant genera. The size of the circles corresponds to the number of shared genera. The internal color of the circles follows a red-blue gradient, where red indicates a positive correlation and blue indicates a negative correlation, with darker shades representing stronger correlation intensity. The color of the circle borders reflects the significance of the correlation derived from P-values in Spearman's correlation analysis, with darker shades indicating higher statistical significance. Significance is denoted as: *, P \u0026lt; 0.05; **, P \u0026lt; 0.01. If ∣ ρ ∣ ≥ 0.7, it indicates a relatively strong linear relationship between the two variables. A value of 0.3 ≤ ∣ ρ ∣ \u0026lt; 0.7 denotes a moderate correlation, while ∣ ρ ∣ \u0026lt; 0.3 suggests there is almost no noticeable trend of mutual influence between the changes in the two variables [30]. *, the difference is significant ( P \u0026lt; 0.05 ). **, the difference is more significant. (P \u0026lt; 0.01).\u003c/p\u003e","description":"","filename":"figure5.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7144754/v1/7bf67e68d3e3d80eddca44c8.jpg"},{"id":89468110,"identity":"ed73ce86-c8a5-4d7f-b0b7-3032857fb7df","added_by":"auto","created_at":"2025-08-20 08:57:46","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1163692,"visible":true,"origin":"","legend":"\u003cp\u003eSourceTracker analysis of gut microbiota\u003c/p\u003e","description":"","filename":"figure6.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7144754/v1/25dcf7dac599249828a5df95.jpg"},{"id":95039774,"identity":"668469c3-161c-4be2-90fe-318b590855dc","added_by":"auto","created_at":"2025-11-03 16:00:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":13599162,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7144754/v1/f1d0a59e-bde6-43d7-85e1-9bf6371bfd0f.pdf"},{"id":89467235,"identity":"c0f9c846-3923-4859-a6b9-c5bb56424bcd","added_by":"auto","created_at":"2025-08-20 08:49:46","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":13660,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S1.\u003c/strong\u003e Inter-group difference analysis of Gut Microbes in Early-stage Squabs\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S2. \u003c/strong\u003eInter-group difference analysis of Microbes in Early-stage Squabs and Factors\u003c/p\u003e","description":"","filename":"tableS.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7144754/v1/0ff9df3e3f1a347946da5b3e.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dynamic Succession and Origin of Gut Microbiota During Early-life in White King Pigeon","fulltext":[{"header":"Background","content":"\u003cp\u003eA large number of diverse microbial communities are colonized in the animal gut, which is regarded as the \u0026quot;second genome\u0026quot; of the animal and has an inseparable symbiotic relationship with the host\u0026nbsp;[1]. Gut microbiota profoundly participate in multiple host physiological processes, including nutrient metabolism (e.g., fermentation of dietary fibers into short-chain fatty acids)[2, 3], development and regulation of the immune system (e.g., promoting immune cell differentiation and maturation, maintaining intestinal barrier integrity)[4-6], and defense against pathogen colonization through mechanisms such as competitive exclusion[7, 8]. Additionally, microbiota-derived metabolites serve as key messengers in the gut-brain axis, underpinning neuromodulatory functions\u0026nbsp;[9]. Consequently, the gut microbiota serves as a critical determinant of host health, growth, development, environmental adaptation. Maintaining structural and functional stability of this microbial community is essential for host health, whereas dysbiosis is closely associated with the pathogenesis of various diseases, including inflammatory bowel disease, metabolic syndromes (obesity, diabetes), autoimmune disorders, neuropsychiatric conditions, and increased infection susceptibility[10, 11]. Thus, deciphering the dynamic processes governing the establishment, succession, and ultimate stabilization of gut microbiota during early life, along with identifying key factors shaping this trajectory, has emerged as a cutting-edge research frontier in life sciences\u0026nbsp;[12].\u003c/p\u003e\n\u003cp\u003eExtensive research indicates that the establishment of the avian gut microbiota commences during the hatching process. Post-hatching, a phase of rapid colonization ensues, with the microbiota tending to reach relative stability within days to weeks. Utilizing high-throughput sequencing, Ding et al.\u0026nbsp;[13]detected substantial microbial communities within chicken embryos at 4 and 19 days of incubation. Our previous research identified abundant microbes present in the meconium of hatchling pigeons[14], from which viable bacteria were successfully isolated and cultured (Unpublished data). Studies in broilers demonstrate that 0-4 days post-hatch represent a period of rapid microbial colonization, 4-10 days are characterized by the rapid proliferation of dominant bacterial groups, and after 10 days old, microbial growth rate declines, leading to stabilization of the microbial composition\u0026nbsp;[15, 16]. In contrast, the gut microbiota of layer hens matures more slowly, typically stabilizing during peak or late lay periods\u0026nbsp;[17]. Research on arctic shorebirds similarly indicates stabilization of the gut microbial structure by 3 days of age\u0026nbsp;[18]. This early rapid colonization phase of the avian gut microbiota likely represents the most plastic period for microbial community assembly, exerting profound implications for subsequent physiological functions, growth performance, and disease resistance in hatchlings\u0026nbsp;[19].\u003c/p\u003e\n\u003cp\u003eEarly gut microbiota colonization is a complex process, where microbes from parents, the environment, and diet are transmitted to the gut of newborn individuals through direct or indirect means. Various intrinsic factors (such as species, genotype, and sex) and extrinsic factors (such as food type, climate conditions, and behavior) may selectively shape the acquired microbiota, resulting in a unique gut microbiota profile for each population or individual\u0026nbsp;[19-22]. Microbiota residing in the maternal oviduct and cloaca can be indirectly transmitted to the embryo via the egg albumen or eggshell\u0026nbsp;[23]. Following hatching, Microbiota from the parental oral cavity, feathers, and skin can be directly or indirectly transferred to the offspring. Research showed that a mere 24-hour-long contact between a hen and newly hatched chickens was long enough for transfer of hen gut microbiota to chickens\u0026nbsp;[24]. The diet is the main driving force in shaping the gut microbiota. The composition of gut microbiota in animals with different feeding habits varies significantly\u0026nbsp;[25], and the microbes in food can also directly colonize the gut\u0026nbsp;[14]. Furthermore, the living or incubation environment continuously introduces new microbial members to the developing bird\u0026nbsp;[26].\u0026nbsp;Although the sources of early gut microbiota colonization are well understood, the contribution of these factors to early gut microbiota establishment remains unclear, especially in the case of pigeons, where relevant studies are still lacking.\u003c/p\u003e\n\u003cp\u003ePrevious research by our team revealed that the gut microbial community structure in squabs stabilizes within the first week after birth. Pigeon milk, serving as the sole nutritional source for squabs, plays a crucial role in establishing their gut microbiota [14]. However, the critical window for the maturation of their gut microbial composition remains unclear, and the influence of other key factors affecting microbial colonization is yet to be elucidated. Therefore, this study employs 16S rRNA gene sequencing to characterize the dynamic changes in the gut microbiota of squabs during the first week post-hatching and to investigate the influence of the microbiota in the cloaca of parent pigeons, pigeon milk, feed, eggshell, albumen, etc. on the early development process of the squab gut microbiota. Through this research, we aim to identify the critical time window for gut microbiota development in pigeons, evaluate the contribution of various factors on the gut microbial community structure in squabs, and thus provide novel data to support theoretical research in pigeon gut microbiota, alongside a scientific basis for modulating microbial communities in practical pigeon farming.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eAnimal model and sampling\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe samples for this study were obtained from Yingshan Fucheng Meat Pigeon Breeding Professional Cooperative (Nanchong, Sichuan, China). Parent pigeons (2-year-old, peak-laying period) were housed in an individual cage under standardized conditions and provided with three meals per day, along with constant access to water and natural light. Their egg-laying cycles remained stable throughout the study.\u003c/p\u003e\n\u003cp\u003eIn this study, fecal samples were collected from squabs on day 0, day 1, day 2, day 3, day 4, day 5, and day 7 post-hatch, designated as G0, G1, G2, G3, G4, G5, and G7, respectively, with seven replicates for each group. The sample collection method for all fecal samples followed the procedure outlined in a previous study\u0026nbsp;[14]. Before rearing squabs, one egg was collected from each breeding pigeon in every group. The egg shell and albumen were aseptically separated, and the samples were designated as DK and DB, respectively. Additionally, a sterile swab was gently rotated within the cloaca of the female pigeon and held for 5 seconds to collect a cloacal swab sample, designated as XZ. A total of 25 g of feed (including compound feed, corn, peas, sorghum, wheat, etc.) was mixed with 50 ml of drinking water from the cage. After shaking for 1 hour and standing for 15 minutes, the supernatant was collected and centrifuged at 12,000 rpm for 10 minutes. The enriched precipitate, representing microbiota from the feed and water, was designated as H. Health sand samples (T) were also collected. Previous studies have shown that microbiota in pigeon colostrum have a significant impact on the establishment of gut microbiota in squabs. Therefore, this experiment will reference colostrum-related data (designated as M0) from previous studies for subsequent analyses\u0026nbsp;[14].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDNA extraction and 16S rRNA gene amplicon sequencing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMicrobial genomic DNA extraction was carried out using the OMEGA Soil DNA Extraction Kit (D5625 - 01; Omega Bio-tek, Norcross, GA, USA), strictly adhering to the manufacturer\u0026apos;s provided protocols. Subsequently, the V3-V4 hypervariable region of the 16S rRNA gene was amplified via polymerase chain reaction (PCR) from the extracted microbial genomic DNA templates.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe amplification employed the forward primer 338F (5\u0026apos;-ACTCCTACGGGAGGCAGCA-3\u0026apos;) and the reverse primer 806R (5\u0026apos;-GGACTACHVGGGTWTCTAAT-3\u0026apos;).Each PCR reaction was configured with a total volume of 25 \u0026mu;L, containing 5 \u0026mu;L of 5\u0026times; buffer, 0.25 \u0026mu;L of Fast Pfu DNA polymerase (5 U/\u0026mu;L), 2 \u0026mu;L of dNTPs (2.5 mM), 1 \u0026mu;L each of the forward and reverse primers (10 \u0026mu;M), 1 \u0026mu;L of template DNA, and 14.75 \u0026mu;L of nuclease-free water (ddH2O). The thermal cycling protocol started with an initial denaturation at 98\u0026deg;C for 5 minutes, followed by 25 cycles of denaturation at 98\u0026deg;C for 30 seconds, annealing at 53\u0026deg;C for 30 seconds, and extension at 72\u0026deg;C for 45 seconds. The process concluded with a final extension step at 72\u0026deg;C for 5 minutes. Post-amplification, the PCR products underwent purification and individual quantification. Subsequently, the amplicons were pooled in equimolar concentrations and subjected to 2\u0026times;250 bp paired-end sequencing on the Illumina NovaSeq platform.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSequence analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe generated sequencing data were processed using QIIME 2, integrating the DADA2 algorithm for amplicon sequence variant (ASV) inference\u0026nbsp;[27]. Quality control procedures included adapter trimming, quality filtering, error correction, paired-end read merging, and chimera removal. Taxonomic classification of ASVs was performed using the classify-sklearn naive Bayes classifier\u0026nbsp;[28]\u0026nbsp;against the Silva 138.1 database. Alpha diversity indices (Chao1, Shannon and Goods_coverage) and beta diversity metrics (Bray-Curtis dissimilarity, weighted UniFrac) were calculated following rarefaction to even sequencing depth.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStatistical analyses were conducted in IBM SPSS Statistics v19. Permutational multivariate analysis of variance (PERMANOVA) tested group differences, and Spearman\u0026apos;s correlation assessed variable relationships. A two - tailed test was used to determine the statistical significance of the correlations, with a significance level set at P\u0026lt; 0.05.\u003c/p\u003e\n\u003cp\u003eIn addition, Source Tracker analysis was carried out using R software (Version 4.5.1) to infer the contributions of potential source communities to the target microbial communities [29]. The analysis was performed using open-source code obtained from the GitHub repository (https://github.com/danknights/sourcetracker), with appropriate modifications made to suit the specific requirements of this study.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eSequencing and species annotation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSequencing of the 85 samples generated a total of 8,023,707 sequences. After quality control, denoising, assembly, and chimera removal, 5,411,339 high-quality sequences were obtained, with an average read length of 425bp, and a total of 27,894 ASVs were matched. The pigeon milk group (M0) exhibited the highest number of ASVs, whereas the albumen group (DB) showed the lowest (Figure 1), indicating higher microbial diversity in pigeon milk and simpler composition in albumen. As shown in Figure 1, the proportion of ASVs annotated to the genus level exceeded 80% in most groups, whereas fewer ASVs were annotated in the meconium (G0), feed \u0026amp; water (H), and health sand (T) groups, suggesting abundant unknown microbes requiring further identification. The number of identified taxa at each taxonomic level for each group is presented in Table 1. The T group exhibited the highest number of taxa identified across all taxonomic levels, followed by the H, G0, and eggshell (DK) groups. At the family and genus levels, the number of species identified in groups G0-G7 showed a trend of initial decrease followed by an increase, peaking at the lowest point at 2 days of age (G2), after which microbial diversity gradually increased.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"553\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"15\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003eNumber of identified taxa at each taxonomic level\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eG0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eG1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eG2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eG3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eG4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eG5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eG7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eM0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eXZ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eDK\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eDB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePhylum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eClass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOrder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFamily\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGenus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e510\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAlpha diversity analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAlpha diversity analysis revealed that M0 exhibited the highest Chao1 and Shannon indices, which were significantly higher than those of DB and female cloaca (XZ) groups (P\u0026lt; 0.05). This indicates that the group of M0 possessed the highest microbial richness and diversity. Notably, the M0 had the lowest Good\u0026apos;s coverage index, suggesting that a small proportion of microbes in pigeon milk might remain undetected. Future studies may need to increase sample size, enhance sequencing depth, or optimize the identification strategy to address this. Conversely, the DB group showed the lowest Chao1 and Shannon indices, while its Good\u0026apos;s coverage index was close to 1. This indicates that the microbial community within the albumen was characterized by low species richness and diversity, and that the current experimental approach nearly completely characterized its microbial composition. Among age groups, G0 showed lower richness/diversity than G1-G7, though not statistically significant, while other age groups exhibited minimal differences in diversity metrics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCharacteristics of Gut microbiota in early-stage squabs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFirmicutes, Actinobacteria, and Proteobacteria constituted the dominant bacterial phyla in the early-stage gut microbiota of squabs, collectively accounting for over 97% of the total microbial abundance. In fecal samples, Firmicutes abundance increased from 21.1% at hatching to 81.3% at day 7; Actinobacteriota peaked at 21.0% on day 2 and dropped to 7.5% on day 3; Proteobacteria decreased from 66.1% at hatching to 0.1% on day 2. At the genus level, the meconium microbiota displayed distinct compositional differences compared to other age groups. The meconium was dominated by \u003cem\u003ePseudomonas\u003c/em\u003e (19.6%), \u003cem\u003eEnterococcus\u003c/em\u003e (14.5%),\u003cem\u003eEscherichia-Shigella\u0026nbsp;\u003c/em\u003e(8.7%), and \u003cem\u003eBifidobacterium\u003c/em\u003e (3.7%). In contrast, postnatal stages were dominated by \u003cem\u003eLactobacillus\u003c/em\u003e, \u003cem\u003eLimosilactobacillus\u003c/em\u003e, \u003cem\u003eAeriscardovia\u003c/em\u003e, and \u003cem\u003eTuricibacter\u003c/em\u003e, with \u003cem\u003eLactobacillus\u003c/em\u003e representing the most predominant taxon. The abundances of \u003cem\u003eLactobacillus\u003c/em\u003e, \u003cem\u003eLimosilactobacillus\u003c/em\u003e, and \u003cem\u003eAeriscardovia\u003c/em\u003e showed initial decreases followed by subsequent increases. \u003cem\u003eTuricibacter\u003c/em\u003e displayed an abrupt abundance surge at day 3, followed by a similar decrease-increase pattern. Figure 3C illustrates the dynamic changes in relative abundance of the top 40 genera across developmental timepoints. Spearman\u0026apos;s correlation analysis between microbial taxa and age (Figure 3D) identified Erysipelotrichaceae_UCG-006 and Solobacterium as exhibiting highly significant positive correlations with age (P \u0026lt; 0.001). Conversely, \u003cem\u003eStaphylococcus\u003c/em\u003e, \u003cem\u003eRalstonia\u003c/em\u003e, and \u003cem\u003eChelativorans\u003c/em\u003e demonstrated significant negative correlations with advancing age (P \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003eWe then applied linear discriminant analysis effect size (LEfSe, LDA threshold = 3) to identify robust significantly different abundant taxa across groups. The analysis revealed that group G0 exhibited the highest number of significantly different genera (7 in total), including \u003cem\u003ePseudomonas\u003c/em\u003e, \u003cem\u003eEnterobacter\u003c/em\u003e, \u003cem\u003eAcinetobacter\u003c/em\u003e, \u003cem\u003eFaecalibacterium\u003c/em\u003e, \u003cem\u003eBacteroides\u003c/em\u003e, \u003cem\u003eAsticcacaulis\u003c/em\u003e, and \u003cem\u003ePrevotell\u003c/em\u003ea. In group G1, three genera were found to be significantly different abundant than in other groups: \u003cem\u003eLactobacillus\u003c/em\u003e, \u003cem\u003eLimosilactobacillus\u003c/em\u003e, and \u003cem\u003eAeriscardovia\u003c/em\u003e, among which \u003cem\u003eLactobacillus\u003c/em\u003e and \u003cem\u003eLimosilactobacillus\u003c/em\u003e are the most abundant probiotics genera in the gut microbiota. A single significantly different abundant genera was identified in each of groups G2, G4, G5, and G7: \u003cem\u003eAtopobium\u003c/em\u003e, \u003cem\u003eLigilactobacillus\u003c/em\u003e, \u003cem\u003eCorynebacterium\u003c/em\u003e, and \u003cem\u003eDubosiella\u003c/em\u003e, respectively. No significantly different abundant genera were detected in group G3. The relatively low number of differentially abundant taxa in groups G2 to G7 suggests that their microbial community structures are relatively similar.\u003c/p\u003e\n\u003cp\u003eWe further analyzed the group-specific taxa among microbes with a relative abundance greater than 0.01% at the genus level. In meconium samples, a total of 155 genera surpassed the 0.01% abundance threshold, of which 133 were unique to meconium, while only 11 genera were consistently detected in the feces of all age groups. This indicates that only a small fraction of meconium microbes persist in the intestine with age, whereas most disappear during subsequent development and are replaced by new microbes. In group G1, only 11 unique genera were identified. The numbers of unique genera in groups G2 to G7 were 2, 1, 5, 2, and 4, respectively. A total of 29 genera were shared among groups G2 to G7, collectively accounting for 95% of the relative abundance in these samples. In contrast, these 29 genera represented only 29% of the total relative abundance in G0 but accounted for 97% in G1. These findings suggest that the microbial composition undergoes a dramatic shift at day 1, with unique species\u0026nbsp;became fewer starting from day 2 onward and intestinal microbiota composition tending to stabilize.\u003c/p\u003e\n\u003cp\u003eInter-group difference analysis (table S2) showed that meconium was extremely significantly different from all other groups. The G1 also showed extremely significant differences from all other groups. The G2 group only had significant differences from G7 group, while there were no significant differences among groups G3-G7. The clustering heatmap (figure 3C) of the top 40 dominant genera at the genus level showed that the G0 group was the farthest from other age groups, followed by the G1 and G2groups, while the groups G3-G7 were relatively clustered, indicating that the composition of the microbes gradually tended to be similar with age. Based on the Bray-Curtis distance matrix, non-metric multidimensional scaling (NMDS) analysis showed that the G0 group was clearly separated from other groups, while the distances between other age groups were relatively small. The results of multiple analyses showed that meconium had a unique microbial composition, and as age progresses, the similarity of the microbial structure between groups gradually increased. This reflects a rapid change in the gut microbiota during early development, followed by a stabilization phase. This transition is the result of a combination of various factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMicrobial correlation analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate the relationship of microbiota in potential factors (XZ, M0, DB, DK, H, T) on the gut microbiota in early-stage squabs, we conducted inter-group differential analysis between these factors and the microbiota at different age groups. Additionally, we performed\u0026nbsp;Spearman\u0026apos;s\u0026nbsp;correlation analysis on the shared microbial taxa at the genus level. Since the squabs no longer had contact with the eggshell and albumen after hatching, these two factors were not included in the analysis of relationship between factors and post-hatch gut microbiota. Furthermore, as the squabs had no contact with pigeon milk before hatching, pigeon milk was also excluded from the analysis of relationship between factors and meconium microbiota.\u003c/p\u003e\n\u003cp\u003eThe results of the inter-group differential analysis showed that G0 differed significantly from all groups except DB,\u0026nbsp;while G1-G7 differed from all factors. To further explore the relationship between the gut microbiota of squabs and the factor groups, we combined the data from the five groups (G2-G7) with relatively small differences, named GN, and then performed an NMDS analysis. The NMDS analysis results were similar to the inter-group differential analysis. The G0 group was closest to the DB group, while the group of G1 to G7 were closest to the M0 group. These findings suggest that the gut microbiota composition before hatching may be more influenced by the microbes present in the albumen, while the gut microbiota composition after hatching may be more influenced by the microbes in pigeon milk.\u003c/p\u003e\n\u003cp\u003eSpearman\u0026apos;s correlation analysis of shared microbes at the genus level revealed that the number of shared microbes among G1-G7 ranged from 62 to 93, with all correlation coefficients \u0026gt; 0.7 (P\u0026lt;0.001). In contrast, the number of shared microbes between G0 and other age groups ranged from 41 to 79, with only G1 showing a significant moderate correlation, while correlations with other age groups did not reach significance. M0 exhibited moderate correlations (0.449-0.595, P\u0026lt;0.001) with the group of G1-G7. The correlation coefficients between XZ and the group of squabs ranged from 0.490 to 0.668 (P\u0026lt;0.001). The H showed only a significant weak correlation with G7. The group of T exhibited significant or highly significant weak correlations with all age groups. Notably, G0 showed weak correlations with DB, DK, and H groups, but no statistically significant correlations with XZ or T.\u003c/p\u003e\n\u003cp\u003eFurthermore, we further screened for shared dominant genera with relative abundances \u0026gt;0.01% across groups and performed Spearman\u0026apos;s correlation analysis. The analysis showed that the number of shared dominant genera among G1-G7 ranged from 25 to 39, with all correlation coefficients \u0026gt;0.7 (P\u0026lt;0.001), consistent with previous findings. Meconium had fewer shared dominant microbes with G1-G7, ranging from 12 to 16, and no significant correlations were observed. The number of shared dominant genera between M0 and the group of G1 to G7 ranged from 16 to 20, with strong positive correlations, particularly with G1, where the correlation coefficient \u0026gt;0.9 (P\u0026lt;0.001). The correlations between XZ and all age groups were lower compared to the previous analysis, even with no significant correlation observed between with the group of G1, G3, and G6. The group of H had a strong negative correlation with G7. The group of Tshowed no significant correlations with the group of G1-G7. Notably, no statistically significant correlations were observed between any of the factors and meconium.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMicrobial Source Tracking Analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe performed source tracking analysis on the microbiota of groups G0-G7 using SourceTracker (figure6). The analysis showed that the microbiota in G0 mainly originated from XZ and DB, with fewer contributions from DK, H, and T, indicating that XZ and DB had the greatest impact on prenatal microbial colonization. However, 58.5% of the microbiota in G0 had unknown sources, suggesting that the origins of prenatal colonizing microbiota require further exploration. Postnatally, the gut microbiota composition of squabs at each postnatal stage evolved based on the previous stage. Therefore, when conducting source tracking analysis of the postnatal gut microbiota, the previous stage was always considered as an influencing factor for the subsequent stage. The results showed that 72% of the microbiota in G1 originated from M0, 9.6% from XZ, and extremely little from G0. For groups G2-G7, the microbiota primarily originated from the previous stage (65.9%-89.1%), followed by M0 and XZ. Contributions from H and T remained limited throughout. Notably, the proportion of microbiota from external sources in the squabs\u0026apos; gut gradually decreased over time, dropping to only 10.9% by day 7.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cstrong\u003eDynamic Succession of Gut Microbiota Early-stage in Squabs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMultiple studies have demonstrated that the gut microbiota composition of squabs undergoes substantial restructuring during the first week post-hatching\u0026nbsp;[14, 31]. This study reveals the spatiotemporal dynamics of gut microbiota in White King pigeon squabs (0\u0026ndash;7 days post-hatch) using 16S rRNA gene sequencing. Our findings revealed that the microbial composition of meconium was substantially distinct from that of all postnatal stages, with the greatest intergroup sample Bray-Curtis distances and a limited number of shared microbial taxa, indicating that most prenatally colonized microbes had been replaced by new colonizers within the first day after hatching. Consequently, the contribution of the prenatally established microbiota to the composition of the postnatal gut microbiota appears minimal. The fecal microbial composition of broilers at 0 days old differs most significantly from that at other ages\u0026nbsp;[16], which is similar to the results of this study.\u003c/p\u003e\n\u003cp\u003eAlthough the primary dominant bacterial taxa were already established squabs by day 1 post-hatching, the 1-day-old group still harbored some unique microbial taxa, showing distinct compositional differences from subsequent age groups. In contrast, from days 2 to 7, the gut microbiota composition showed minimal changes, with fewer differential species and a lower number of unique species, leading to closer sample distances between the groups. Multiple analytical approaches confirmed that the gut microbial composition had approached maturity by day 2 post-hatching. Studies have demonstrated that the maturation time of early gut microbiota varies significantly across different species and even among different strains. The gut microbiota of Arctic shorebirds (including \u003cem\u003eCalidris alpina\u003c/em\u003e and \u003cem\u003eCalidris canutus\u003c/em\u003e) begins to mature by 3 days of age [22]; for crested ibises, this process takes approximately 44 days[32]. Broilers typically have their intestinal microbiota mature within about 10 days [16], while the maturation of gut microbiota in layer hens is slower , even not being achieved until the laying period [17]. This rapid succession (48-hour maturation window) is comparable to broilers and Arctic shorebirds but occurs much earlier than in crested ibises or layer hens. Chicks inoculated with a developed donor microbiota exhibit less variation in gut microbiota composition with age, and their gut microbiota composition is more mature [12, 33]. It is possible that the rapid maturation of the gut microbiota in squabs is due to the strong correlation between the microbiota in pigeon milk and the gut microbiota.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKey Microbiota Taxa and Their Functional Implications\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWithin the meconium microbial community, \u003cem\u003ePseudomonas\u003c/em\u003e, \u003cem\u003eEnterococcus\u003c/em\u003e, and \u003cem\u003eEscherichia-Shigella\u003c/em\u003e represent the dominant taxa, aligning with the dominant bacterial genera identified in the oviduct [23, 34]. Among these, \u003cem\u003eEnterococcus\u003c/em\u003e and \u003cem\u003eEscherichia-Shigella\u003c/em\u003e are recognized potential pathogens, while \u003cem\u003ePseudomonas\u003c/em\u003e and \u003cem\u003eEscherichia-Shigella\u0026nbsp;\u003c/em\u003ealso dominate the pre-hatch microbiota of broilers, undergoing rapid replacement by newly established taxa\u0026mdash;a pattern mirrored in this study [16, 35]. The stabilized gut microbiota was dominated by Firmicutes, with the most abundant genera being \u003cem\u003eLactobacillus\u003c/em\u003e and \u003cem\u003eLimosilactobacillus\u003c/em\u003e, consistent with previous findings\u0026nbsp;[14].\u003cem\u003e\u0026nbsp;Lactobacillus\u003c/em\u003e and \u003cem\u003eLimosilactobacillus\u003c/em\u003e facilitate carbohydrate fermentation, acidifies the gut, and inhibits pathogen colonization [36]. Their critical roles extend to maintaining gut microbial homeostasis, enhancing immune function, facilitating nutrient absorption, and mitigating inflammation\u0026mdash;attributes that underpin their widespread use in probiotic applications [37-39]. Spearman\u0026apos;s correlation analysis showed positive associations between \u003cem\u003eLactobacillus\u003c/em\u003e abundance and age, while potential pathogens (\u003cem\u003eStaphylococcus\u003c/em\u003e, \u003cem\u003eRalstonia\u003c/em\u003e) negatively correlated with age, indicating a possible co-evolution with the host immune system[40]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSources of Prenatal and Postnatal Microbiota Colonization\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo elucidate the origin of intestinal microbiota in squabs, we performed differential analysis, analysis of shared microbes and their correlations, as well as microbial source tracking analysis between the gut microbiota of squabs at different ages and microbes from various potential sources. Our analyses revealed that the microbes in meconium exhibited the smallest difference from those in albumen, and showed a statistically significant weak correlations with the shared microbes in albumen and eggshell. However, there was no statistically significant correlation among the dominant shared microbes with an abundance greater than 0.1%. This suggests that rare microbial taxa within the albumen and eggshell may exert a more substantial influence on prenatal colonization than dominant species. Source racker analysis further indicated that the prenatally colonizing microbiota primarily originated from the albumen and the female cloaca, with a more limited contribution from the eggshell. Nevertheless, microbes in the albumen originate from the magnum, whereas the eggshell is formed in the uterus,\u0026nbsp;and the microbes in the eggshell are closely related to the uterus. The magnum, uterus, and cloaca are interconnected and as the parts of the reproductive tract, leading to a certain degree of homology among their microbial communities\u0026nbsp;[23, 34].\u0026nbsp;Previous studies have demonstrated that the microbiota in the cecum of chicken embryos may be derived from the oviductal microbiota, which is transmitted via albumen\u0026nbsp;[23]. There is also study demonstrating that eggshells play an important role in the development of the chicken gut microbiota, especially in the jejunum and ileum\u0026nbsp;[41]. Integrating the results of multiple analyses, and the research results of predecessors,\u0026nbsp;we conclude that microbes in albumen are the most important source of prenatally colonized microbes, and that microbes in the cloaca and eggshell are also indispensable sources.\u003c/p\u003e\n\u003cp\u003eThe gut microbiota of newborns is primarily acquired from parents through vertical transmission and from the environment through horizontal transmission. A study has shown that just 24 hours of contact between a hen and newly hatched chicks is enough for the hen\u0026apos;s gut microbiota to be transferred to the chicks [24]. The gut microbial community of zebra finch chicks tends to converge with that of their rearing parents, with the parental oral cavity/crop microbiota making a high contribution to the early gut microbiota of the chicks [42]. The environment, as a huge microbial pool, also provides a rich microbial library for the establishment of the gut microbiota. As an environment where newborns live together with their parents, the nest is an important driver of the assembly of the neonatal microbiota [43, 44]. Source tracking analysis of the gut microbiota in 1-day-old squabs revealed that crop milk contributed substantially to colonization, accounting for 72% of the microbiota. In contrast, only a minimal proportion of the prenatally established microbiota persisted at this stage. This finding underscores the critical role of pigeon milk in establishing the early-stage microbial community in squabs, while also indicating that prenatally acquired microbes are rapidly displaced in the squab\u0026apos;s gut shortly after hatching. From day 2 onwards, the number of newly colonized microbial species in the squab gut gradually decreased, with only 10.9% of the microbiota originating from external environmental sources y day 7. Pigeon milk exerts a significant impact on the gut microbiota composition of 1-day-old squabs, while its influence on the gut microbiota of squabs at other ages weakens gradually. Nevertheless, the cloacal microbiota of female pigeons contributes to the gut microbiota of squabs across all age groups. Furthermore, multiple analytical approaches consistently demonstrated that although microorganisms present in diet (feed, drinking water, grit) rarely successfully colonized the squab gut. Similar results have also been obtained from the traceability analysis of the gut microbiota in zebra finches [42].\u003c/p\u003e"},{"header":"Conclusion ","content":"\u003cp\u003eThis study systematically characterizes the dynamic establishment and microbial sources of the gut microbiota in White King pigeon squabs (0-7 days post-hatch) using 16S rRNA gene sequencing. The prenatal meconium (G0) harbored a distinct community dominated by \u003cem\u003ePseudomonas\u003c/em\u003e (19.6%), \u003cem\u003eEnterococcus\u003c/em\u003e (14.5%), and \u003cem\u003eEscherichia-Shigella\u003c/em\u003e (8.7%), which were rapidly replaced by pigeon milk-derived probiotics within 24 hours post-hatch. By day 2, the gut microbiota tended to stabilize, with \u003cem\u003eLactobacillus\u003c/em\u003e and \u003cem\u003eLimosilactobacillus\u0026nbsp;\u003c/em\u003efrom Firmicutes accounting for \u0026gt;80% of the community. This rapid succession highlights the first 48 hours as a critical window for microbiota maturation, with minimal persistence of prenatal microbes in postnatal stages. Source tracking analysis revealed that prenatal colonization primarily originated from albumen (DB) and the female cloaca (XZ), contributing 58.5% of G0 microbiota. Postnatally, pigeon milk (M0) drove 72% of the G1 microbiota, surpassing the negligible contribution from G0 (1.2%). From day 2 onward, microbiota composition was sustained by previous-stage communities (65.9\u0026ndash;89.1%), with diminishing external inputs (10.9% by day 7). These results confirm that maternal-derived microbes (via egg components and pigeon milk) are the primary drivers of early gut colonization, while environmental factors (feed, health sand) play a minor role. The rapid replacement of potential pathogens (e.g., \u003cem\u003eEscherichia-Shigella\u003c/em\u003e) by probiotics within 48 hours underscores the protective role of pigeon milk in establishing a healthy gut ecosystem. This study provides a scientific basis for developing probiotic-supplemented artificial pigeon milk formulations to mimic natural colonization patterns. Additionally, targeting the prenatal microbial sources (e.g., eggshell and cloacal disinfection) may mitigate pathogen transmission risks, enhancing squab health and productivity in commercial farming.\u003c/p\u003e\n\u003cp\u003eIn summary, this research elucidates the spatiotemporal dynamics and maternal origins of the pigeon gut microbiota, establishing a framework for translational applications in avian microbiome management.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis project received support from multiple funding sources, including the Natural Science Foundation of Sichuan Province, grant number 2023YFN0032, Innovation Team Funds of China West Normal University, grant number KCXTD2024-5.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during the current study are available in NCBI repository with BioProject ID PRJNA1288493. This is the accession numbers of the datasets SUB15436841.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe experiment was performed in accordance with the Regulations for the Administration of Affairs Concerning Experimental Animals (Ministry of Science and Technology, Beijing, China, revised in June 2004) and was approved by the Institutional Animal Care and Use Committee of China West Normal University, Sichuan, China (No. 2025LLSC0065).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXiaoqin Xu, conceived research; all contributed to the study design, Jundong He, Bangyuan Wu, Long Zhang and Li Liu collected data. Yue He conducted analyses and wrote the paper. All participated in revisions and read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1 Key Laboratory of Southwest China Wildlife Resources Conservation (Ministry of Education), China West Normal University, Nanchong 637009, P. R. China\u003c/p\u003e\n\u003cp\u003e2 Sichuan Wildlife Rehabilitation and Breeding Research Center, China West Normal University, Nanchong 637002, P. R. China\u003c/p\u003e\n\u003cp\u003e3 Institute of Ecology, China West Normal University, Nanchong 637002, P. R. China\u003c/p\u003e\n\u003cp\u003e4 College of Life Sciences, China West Normal University, Nanchong 637009, P. R. China\u003c/p\u003e\n\u003cp\u003e5 Yingshan Fucheng Meat Pigeon Breeding Professional Cooperative, Nanchong 637770, Sichuan, China\u003c/p\u003e\n\u003cp\u003e6 Agricultural Technique Promotion Station of Nanchong, Nanchong 637002, P. R. China\u003c/p\u003e\n\u003cp\u003e*Corresponding author: Xiaoqin Xu; [email protected]\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eZhu B, Wang X, Li L: Human gut microbiome: the second genome of human body. \u003cem\u003ePROTEIN CELL\u003c/em\u003e 2010, 1(8):718-725. https://doi.org/10.1007/s13238-010-0093-z.\u003c/li\u003e\n\u003cli\u003eTannock GW, Liu Y: Guided dietary fibre intake as a means of directing short-chain fatty acid production by the gut microbiota. 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10.3389/fmicb.2018.01524.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mcro","sideBox":"Learn more about [BMC Microbiology](http://bmcmicrobiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mcro","title":"BMC Microbiology","twitterHandle":"#bmcmicrobiology","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Squabs, Gut microbiota, Early establishment Source tracking, 16S rRNA sequencing","lastPublishedDoi":"10.21203/rs.3.rs-7144754/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7144754/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground \u003c/strong\u003eThe gut microbiota plays a critical role in host health, yet the dynamic establishment and key influencing factors of the early-life gut microbiota in pigeons remain poorly understood.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e This study employed 16S rRNA gene sequencing to characterize the spatiotemporal succession of gut microbiota in White King pigeon squabs (Within one week after birth) and quantify contributions from potential sources, including pigeon milk, cloaca, egg components, feed, and environment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e Revealed a dramatic transition from prenatal to postnatal microbiota: meconium (G0) was dominated by \u003cem\u003ePseudomonas\u003c/em\u003e(19.6%), \u003cem\u003eEnterococcus\u003c/em\u003e 14.5%), and \u003cem\u003eEscherichia-Shigella\u003c/em\u003e (8.7%), whereas postnatal communities rapidly shifted to a stable composition dominated by \u003cem\u003eLactobacillus\u003c/em\u003e and \u003cem\u003eLimosilactobacillus \u003c/em\u003e(Firmicutes) by day 2. Source tracking analysis demonstrated that prenatal colonization primarily originated from albumen (DB) and female cloaca (XZ), contributing 58.5% of G0 microbiota, while pigeon milk (M0) drove 72% of the microbiota in 1-day-old squabs (G1), outcompeting prenatal microbes. Postnatally, microbiota assembly was increasingly driven by previous-stage communities (65.9-9.1%), with minimal environmental input (10.9% by day 7).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e These findings establish the first 48 hours as a critical developmental window for gut microbiota maturation and highlight pigeon milk as the primary driver of early microbial assembly. The study provides a scientific basis for microbial modulation strategies in pigeon farming, including probiotic-supplemented artificial pigeon milk formulation and biosecurity measures to mitigate prenatal pathogen transmission.\u003c/p\u003e","manuscriptTitle":"Dynamic Succession and Origin of Gut Microbiota During Early-life in White King Pigeon","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-20 08:49:42","doi":"10.21203/rs.3.rs-7144754/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-19T06:05:21+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-18T16:41:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"188622029950236725667819585145598345261","date":"2025-09-16T03:40:08+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-25T06:14:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"129535756273184882780184865328794948513","date":"2025-08-21T00:42:39+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-12T15:22:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-31T07:36:34+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-25T06:53:38+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-24T07:17:13+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Microbiology","date":"2025-07-24T07:12:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mcro","sideBox":"Learn more about [BMC Microbiology](http://bmcmicrobiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mcro","title":"BMC Microbiology","twitterHandle":"#bmcmicrobiology","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e1df0c74-2a30-4612-8c52-f5c350e317b1","owner":[],"postedDate":"August 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-03T15:59:10+00:00","versionOfRecord":{"articleIdentity":"rs-7144754","link":"https://doi.org/10.1186/s12866-025-04449-8","journal":{"identity":"bmc-microbiology","isVorOnly":false,"title":"BMC Microbiology"},"publishedOn":"2025-10-29 15:56:54","publishedOnDateReadable":"October 29th, 2025"},"versionCreatedAt":"2025-08-20 08:49:42","video":"","vorDoi":"10.1186/s12866-025-04449-8","vorDoiUrl":"https://doi.org/10.1186/s12866-025-04449-8","workflowStages":[]},"version":"v1","identity":"rs-7144754","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7144754","identity":"rs-7144754","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}