Inter-breed diversity and temporal dynamics of the fecal microbiota in two local chicken breeds from karst regions in China

Abstract

The aim of this study was to investigate the temporal dynamics and inter-breed differences of fecal microbiota in two indigenous chicken breeds from Guizhou during the early post-hatch period. Six hundred chicks, including 300 Guizhou yellow chickens (GY) and 300 Changshun green-shell egg chickens (CG), were reared under identical conditions, and fecal samples were collected at post-hatch days 1, 3, 7, 14, 30, and 45. Microbial communities were analyzed using 16S rRNA gene sequencing. Alpha diversity analysis revealed a progressive increase in microbial richness and diversity with age in both breeds, with CG peaking at day 45 and GY at day 30. Beta diversity analysis indicated breed-specific clustering, reflecting distinct temporal succession patterns. Taxonomic profiling showed Bacillota as the dominant phylum across all time points, while Enterococcus and Ligilactobacillus were the core genera, with early samples enriched in Escherichia-Shigella and Clostridium. LEfSe analysis identified age- and breed-specific microbial biomarkers, demonstrating a general trend of early dominance by Clostridia and Escherichia-Shigella, mid-stage enrichment of diverse genera including Candidatus Arthromitus and Ruminococcus, and late-stage prevalence of lactic acid bacteria such as Lactobacillus and Streptococcus. Functional prediction indicated carbohydrate and amino acid metabolism as the predominant microbial functions. Quantification of ecological processes revealed that homogeneous selection constituted the dominant mechanism, overwhelmingly governed microbiota assembly, with stochastic processes playing a minor, breed-specific role. The CG breed exhibited a pattern of rapid, directional succession, culminating in a stable and phylogenetically conserved community. This suggests that host genetic factors may exert strong and consistent deterministic selection, which could thereby facilitate the formation of specialized microbiota. In contrast, the GY breed displayed a more gradual and variable assembly process, resulting in a community with higher temporal turnover and greater phylogenetic plasticity. Overall, the findings reveal that inter-breed diversity is not merely compositional but extends to the underlying ecological rules governing community succession. Meanwhile, these findings suggest that inter-breed diversity may be shaped by host genetic factors, which in turn mold the temporal assembly and functional features of the early gut microbiota, providing insights into precision nutritional management and health optimization for local poultry breeds.

Introduction

Poultry farming represents a critical pillar of global agriculture, playing an indispensable role in food security by supplying high-quality animal protein. Chicken meat and eggs serve as vital nutritional sources, offering an advantageous profile of high protein and low fat, alongside highly efficient production traits characterized by short cycles and low cost (Kralik and Kralik, 2017). Over the past two decades, global per capita consumption of poultry meat has risen substantially, highlighting its key role in meeting the dietary demands of a growing population, especially in developing regions. However, the rapid shift toward intensive production systems has led to markedly increased stocking densities, which in turn pose significant challenges. Elevated densities are closely associated with higher ammonia emissions, deteriorated litter quality, and an increased risk of infectious disease outbreaks, such as avian influenza and Newcastle disease (Bist et al., 2023). Antibiotics have historically been used in poultry production to mitigate disease and productivity losses, but excessive or inappropriate use can accelerate antimicrobial resistance and has prompted restrictions on antibiotic growth promoters in many production systems. This regulatory shift has intensified the search for sustainable alternatives to maintain flock health and performance. Within this context, a deeper understanding of the intestinal microbiota and its functional roles has emerged as a promising scientific and practical avenue for advancing sustainable poultry production.

The intestinal microbiota exerts multifaceted and critical functions in poultry, encompassing nutrient absorption, immunomodulation, growth promotion, and disease resistance(Johnsson et al., 2016). In chickens, the hindgut harbors the most abundant and diverse microbial communities within the gastrointestinal tract (Feng et al., 2023). These microbial assemblages are central to key metabolic processes, including dietary fiber degradation, short-chain fatty acid production, and vitamin synthesis, while also playing a decisive role in the development of the host's mucosal immune system and the maintenance of intestinal barrier integrity (Pan and Yu, 2014; Wang et al., 2023; Zhu La et al., 2023). Given the pivotal influence of hindgut microbiota on host health and performance, analyzing fecal microbial dynamics offers a valuable, non-invasive strategy for precision management of flock health and nutrient utilization, with potential to reduce reliance on antibiotics. However, the majority of existing research on the chicken gut microbiome has focused on commercial breeds. Indigenous breeds, which may possess unique microbial profiles shaped by local environmental adaptation and distinct selection histories, remain largely unexplored. Furthermore, the early post-hatch period represents a critical window for microbial colonization, with profound implications for lifelong health and productivity outcomes.

Chinese native chicken breeds, shaped by long-term local adaptation, distinct genetic backgrounds, and unique selection histories, may harbor gut microbiota that differ substantially from those of commercial breeds (Niu et al., 2002; Jin et al., 2026). Developmental profiling of Hainan Wenchang chickens demonstrated marked age-associated changes in cecal microbiota and metabolites(He et al., 2023). Hotan Black and Baicheng You chickens showed pronounced breed- and gut-compartment-specific microbial patterns(Dou et al., 2026). However, these indigenous breeds remain underexplored in terms of their gut microbial composition. Investigating their gut microbiota could reveal microbial markers linked to environmental adaptability and stress resistance. The period immediately after hatching constitutes a critical phase for the establishment and maturation of the gut microbiota, with long-lasting consequences for host health. Microbiota established during this formative phase can profoundly influence lifelong health, growth, immunity, and productivity. Indigenous breeds such as the Guizhou yellow chicken (GY) and the Changshun green-shell egg chicken (CG) are important components of local agricultural systems, supporting biodiversity and sustainable poultry farming. The GY is noted for strong adaptability, rapid growth, and high feed conversion efficiency (Tian et al., 2025), whereas the CG, a genetically protected breed, is valued for its stress resilience, tender meat, and particularly its nutritionally enhanced green-shelled eggs (Mu et al., 2021). Despite their economic and cultural significance, the developmental dynamics of their gut microbiota remain largely unknown.

In this study, we applied 16S rRNA gene sequencing to profile fecal microbiota in GY and CG chickens from day 1 to day 45 post-hatch. Our objectives were to (i) characterize temporal changes in fecal microbiota during early growth, (ii) compare microbial community structure and diversity between breeds, and (iii) identify breed-specific microbial signatures. These findings are expected to advance our understanding of gut microbiota development in indigenous chickens and provide a foundation for improving health management, nutritional strategies, and breeding programs, thereby supporting the sustainable utilization and conservation of local poultry genetic resources.

Materials and methods

Animals and sample collection

All animal procedures in this study were conducted in strict accordance with the Guidelines for the Care and Use of Experimental Animals issued by the Ministry of Agriculture of China. The experimental protocol was reviewed and approved by the Ethics Committee for the Care and Use of Laboratory Animals of Guizhou University (Approval No. EAE-GZU-2023-E086). Throughout the study, every effort was made to minimize the number of animals used and to alleviate potential pain or distress.

A total of 600 one-day-old chicks, comprising 300 Guizhou yellow chickens (GY) and 300 Changshun green-shell egg chickens (CG), all hatched from the same cohort, were employed. The GY is a synthetic breed developed by crossbreeding the indigenous Weining chicken (maternal line) with New Hampshire and Golden Rock breeds (paternal lines), known for its balanced meat and egg production traits. The CG is a protected national genetic resource, characterized by a compact body, notable stress resilience, and the production of green-shelled eggs enriched in carotenoids with lower cholesterol content. All birds were reared under standardized conditions at a research facility of Guizhou University. They were housed in tiered cages equipped with automatic environmental control systems, with the temperature maintained at 32–35°C during the first week and then reduced by 2–3°C each week thereafter. Humidity was maintained at 60%–65%, and the lighting duration was gradually reduced until natural lighting conditions were reached. Birds had ad libitum access to water and a standard corn-soybean meal diet, formulated by a designated feed mill to meet National Research Council (NRC) nutritional specifications (detailed composition in Table 1, based on China National Feeding Standard NY/T 33-2004).

Table 1.

Basal diet ingredients and nutrient composition (%, as-fed basis).

Items	Content
Ingredients (%)
Corn	64.00
Soybean meal	28.00
Corn germ	4.00
Limestone	1.05
CaHPO4	0.71
NaCI	0.22
Montmorillonite	0.20
Lysine	0.60
Methionine	0.10
Threonine	0.12
Premixa	1.00
Total	100
Nutrient components (%)b
Metabolizable energy (MJ/kg)	12.17
Crude protein	19.34
Calcium	0.84
Available phosphorus	0.30
Lysine	1.34

^aThe premix provided per kilogram of compound in the diet: Vitamin A, 11,000 IU; vitamin D3, 16,500 IU; vitamin E, 5.5 IU; vitamin K, 4.409 mg; vitamin B1, 0.551 mg; vitamin B2, 1.102 mg; vitamin B12, 0.07 mg; folic acid, 1.0 mg; vitamin C, 75 mg; pantothenic acid, 8.81 mg; folic acid, 1.05 mg; choline chloride, 0.1 g; calcium formate, 0.2 g; complex enzyme (SFQ-081), 0.1 g.

^bAnalyzed levels.

Fresh fecal samples were collected at six post-hatch ages: days 1, 3, 7, 14, 30, and 45. At each time point, six chickens showing relatively uniform body weight were randomly selected from the corresponding breed population for fecal collection, with six chickens per time point, resulting in a total of 36 fecal samples. Fecal collection was non-invasive and did not require slaughter. The sampling design was population-based at each time point rather than a fixed-individual repeated-measures design, and the same birds were not intentionally tracked across all six sampling points. Samples were collected into sterile tubes prefilled with an anaerobic preservation solution, immediately placed on ice, and processed aseptically within 2 hours of collection to preserve microbial community integrity.

DNA extraction, PCR amplification and sequencing

Total microbial-community DNA was extracted from fecal samples using the E.Z.N.A.® Soil DNA Kit (Omega Bio-tek, Norcross, GA, USA) following the manufacturer’s instructions. Here, the term DNA refers to total DNA extracted from the fecal microbial sample for subsequent 16S rRNA gene amplicon sequencing. The quality and concentration of the extracted DNA were assessed by 1.0% agarose gel electrophoresis and measured using a NanoDrop 2000 spectrophotometer (Thermo Scientific, USA). All DNA samples were stored at −80°C until polymerase chain reaction (PCR) amplification.

The hypervariable V3–V4 region of the bacterial 16S rRNA gene was amplified via polymerase chain reaction (PCR) using the universal primers 338F and 806R on a T100 Thermal Cycler (Bio-Rad, USA). Each 20 µL reaction contained 10 ng of template DNA, 0.8 µL of each primer (5 µM), 10 µL of Pro Taq DNA Polymerase master mix, and nuclease-free water. The thermal cycling program comprised initial denaturation at 95°C for 3 min; 29 cycles of denaturation at 95°C for 30 s, annealing at 53°C for 30 s, and extension at 72°C for 45 s; followed by a final extension at 72°C for 10 min and a hold at 4°C. PCR products were visualized on a 2% agarose gel, purified with a PCR Clean-Up Kit (YuHua, Shanghai, China) according to the manufacturer’s instructions, and quantified using a Qubit 4.0 fluorometer (Thermo Fisher Scientific, USA).

Purified amplicons were pooled in equimolar amounts and subjected to paired-end sequencing (2 × 250 bp) on an Illumina Nextseq 2000 platform (Illumina, San Diego, USA) by Majorbio Bio-Pharm Technology Co., Ltd. (Shanghai, China), following the manufacturer’s standard protocols.

Data processing

Raw paired-end FASTQ files were demultiplexed using an in-house Perl script. Sequence processing was performed using fastp (version 0.19.6) for quality control and FLASH (version 1.2.7) for pair merging, with the following criteria: (i) reads were truncated at any site with an average quality score below 20 over a 50-bp sliding window, and reads shorter than 50 bp or containing ambiguous nucleotides were discarded; (ii) paired-end reads were assembled where the overlap exceeded 10 bp, allowing a maximum mismatch ratio of 0.2; unassembled reads were removed; (iii) samples were assigned based on exact barcode matching, allowing up to 2 nucleotide mismatches in primer sequences, and the orientation was adjusted accordingly.

High-quality sequences were clustered into operational taxonomic units (OTUs) at 97% similarity using the UPARSE pipeline (version 11), and the most abundant sequence within each OTU was selected as the representative sequence. To mitigate the influence of uneven sequencing depth on diversity analyses, all samples were rarefied to 20,000 sequences per sample, which resulted in an average Good’s coverage of 99.09%.

Taxonomic assignment of OTU representative sequences was performed using the RDP Classifier (version 2.2) against the SILVA database (v138) with a confidence threshold of 0.7. Putative metagenomic functional profiles were predicted from the 16S rRNA data using PICRUSt2 (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States) following the developer’s recommended workflow. Because PICRUSt2 predicts gene-family content from marker-gene profiles rather than directly measuring microbial genes or metabolites, all downstream functional results were treated as predictions of functional potential.

Statistical analysis

Bioinformatic and statistical analyses were performed on the Majorbio Cloud Platform (https://cloud.majorbio.com). Alpha diversity indices, including the observed number of OTUs, Chao1 richness estimator, Ace index, Shannon index, and Simpson index, were calculated from the rarefied OTU table using Mothur (v1.30.2). Differences in these indices between groups were assessed using the non-parametric Kruskal-Wallis test.

Beta diversity, representing the dissimilarity in microbial community structure between samples, was evaluated based on Bray-Curtis distances. Visualization was performed via Non-metric Multidimensional Scaling (NMDS). Permutational multivariate analysis of variance (PERMANOVA) was conducted using the vegan package (v2.5-3) in R to test for statistically significant differences in overall community structure between groups. PERMANOVA effect sizes (R²) and P values are reported together with the NMDS results; the NMDS stress value is reported separately as a measure of ordination fit.

To identify bacterial taxa (from phylum to genus level) that were differentially abundant between groups, Linear Discriminant Analysis Effect Size (LEfSe) was employed. The analysis utilized the non-parametric factorial Kruskal-Wallis sum-rank test followed by Linear Discriminant Analysis to estimate the effect size. Taxa with a Linear Discriminant Analysis (LDA) score greater than 4.0 and a p-value less than 0.05 were considered significant biomarkers. The mean relative abundances of significant biomarkers at different time points were evaluated using the Kruskal–Wallis H test.

To assess changes in microbial community structure and ecological strategies over time, the beta deviation index of microbial communities at different time points was first calculated, and differences among time points were compared using boxplots combined with significance tests. Subsequently, taxa were classified into transient, intermittent, and persistent groups according to their occurrence frequencies across samples, and the proportions of their abundance and taxonomic numbers were determined. Furthermore, based on niche breadth indices, microbial taxa were categorized into generalists, neutralists, and specialists to analyze variations in ecological strategies at different treatment times. After log10 transformation, scatter plots integrating average relative abundance and niche breadth were constructed to reveal the relationships between microbial abundance distribution and ecological adaptation strategies.

To further elucidate microbial community assembly mechanisms, the beta nearest taxon index (βNTI) combined with Bray–Curtis-based Raup–Crick metrics (RCBray) was employed to evaluate the relative contributions of deterministic and stochastic processes. When |βNTI| < 2, the phylogenetic turnover between coexisting communities was considered lower than expected under the null model, indicating that deterministic selection was not statistically distinguishable from stochastic expectations. Based on βNTI and RCBray values, community assembly processes were further classified into five ecological processes: homogeneous selection (βNTI < −2), heterogeneous selection (βNTI > +2), dispersal limitation (|βNTI| < 2 and RCBray > 0.95), homogenizing dispersal (|βNTI| < 2 and RCBray < −0.95), and undominated processes (|βNTI| < 2 and |RCBray| < 0.95). The thresholds used for βNTI and RCBray were based on established null-model frameworks. Specifically, βNTI values exceeding ±2 represent significant deviations (>2 standard deviations) from the null expectation, indicating deterministic assembly processes driven by selection. In contrast, |βNTI| < 2 suggests that phylogenetic turnover does not significantly differ from stochastic expectations, and stochastic processes may dominate community assembly. For communities with |βNTI| < 2, RCBray was further applied to identify stochastic mechanisms. The thresholds of RCBray > 0.95 and RCBray < −0.95 correspond to significant deviations from random community composition and indicate dispersal limitation and homogenizing dispersal, respectively, whereas |RCBray| < 0.95 represents undominated processes. These criteria follow previously established ecological null-model approaches (Stegen et al., 2012; Ning et al., 2019).

In addition, the neutral community model (NCM) was used to analyze the relationship between the occurrence frequency of taxa and their relative abundance, thereby distinguishing the contribution of stochastic processes to microbial community assembly. Functional prediction of microbial communities was performed using PICRUSt2 (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States) based on 16S rRNA gene sequencing data. Moreover, the BugBase platform was applied to predict microbial phenotypic characteristics, including Gram staining properties, oxygen utilization types, potential pathogenicity, and mobile genetic elements. Phenotypic inference was conducted using normalized OTU abundance tables, and the relative abundance of each phenotype was calculated. Differences among treatment groups were analyzed using the Kruskal–Wallis H test, with statistical significance defined as P < 0.05.

Results

Microbial community richness and overview

The distribution of shared and unique operational taxonomic units (OTUs) in the fecal microbiota of Changshun green-shell egg chicken (CG) and Guizhou yellow chicken (GY) across six developmental time points (days 1, 3, 7, 14, 30, and 45) is presented in Fig. 1. In the CG breed, the number of unique OTUs at each time point (CG1, CG3, CG7, CG14, CG30, and CG45) was 79, 22, 155, 10, 76, and 638, respectively. Notably, the CG45 time point harbored the highest number of unique OTUs, constituting 38.09% of the total OTUs observed in this breed. Only 38 OTUs (2.27% of the total) were shared across all six time points, representing the core microbiota of the CG breed during early development. The GY breed exhibited a distinct pattern. The number of unique OTUs at corresponding time points (GY1, GY3, GY7, GY14, GY30, and GY45) was 248, 36, 37, 85, 248, and 106 respectively, with a notable peak at GY30 (15.75% of total OTUs). The core microbiota shared across all time points in the GY breed was even smaller, comprising only 12 OTUs (0.76% of the total). Furthermore, the total observed OTUs at the final sampling point (day 45) was substantially lower in the GY breed (595 OTUs) compared to the CG breed (1,238 OTUs).

Fig. 1.

Distribution of shared and unique OTUs. (A) Venn plot showing OTU overlap across six time points in CG. (B) Venn plot showing OTU overlap across six time points in GY. The numbers in each Venn sector represent the number and percentage of unique or shared OTUs for the corresponding section. The bar plot below each Venn diagram shows the total OTU count of each sampling time-point group. CG, Changshun green-shell egg chicken; GY, Guizhou yellow chicken. The numeric suffix in sample names (e.g., CG1, GY45) indicates the postnatal sampling day.

Alpha diversity of the fecal microbiota

The alpha diversity of the fecal microbiota in both chicken breeds was assessed using Chao1, Ace, Shannon, and Simpson indices (Fig. 2). In the CG group (Fig. 2A), the species richness indices (Chao1 and Ace) of CG45 were significantly higher than those of CG1, CG3, and CG14. Regarding diversity indices, the Shannon index of CG1 was significantly lower than that of CG3, CG7, CG30, and CG45, whereas its Simpson index was significantly higher than all other groups.

Fig. 2.

(A) Presents the alpha diversity indices (Ace, Chao1, Shannon, and Simpson) of the fecal microbiota for Group CG; (B) displays the corresponding alpha diversity indices for Group GY.

In the GY group (Fig. 2B), GY30 exhibited the highest species richness, with its Chao1 and Ace indices being significantly higher than all other time points. Specifically, the Chao1 and Shannon indices of GY30 were significantly greater than those of GY1 and GY3, and its Ace index was significantly higher than that of GY1, GY3, and GY7. Conversely, the Simpson index of GY1 was significantly higher than that of GY14, GY30, and GY45.

Beta diversity reveals breed-specific clustering

Beta diversity, reflecting the compositional differences in microbial communities between samples, was assessed using Non-metric Multidimensional Scaling (NMDS) based on OTU-level data (Fig. 3). In the NMDS plot, each point represents an individual sample, with points of the same color belonging to the same experimental group. A shorter distance between two points indicates a higher similarity in their microbial community structure.

Fig. 3.

Distinct β-diversity between CG and GY groups assessed by NMDS. (A) Non-metric multidimensional scaling (NMDS) ordination plot (stress < 0.2) showing the separation of CG and GY samples, accompanied by density contours. (B-C) Box plots comparing the distribution of NMDS1 and NMDS2 scores between groups. All panels display statistically significant differences (P = 0.001) in microbial community composition.

For the CG group (Fig. 3B), the NMDS ordination showed a stress value of 0.17, an R² of 0.43711 and a P-value of 0.001, indicating a reliable representation of the multidimensional data. A clear separation was observed, with the CG1 group clustering distinctly apart from all other groups. In contrast, the later time point groups (CG3, CG7, CG14, CG30, and CG45) exhibited considerable overlap in the ordination space, suggesting a convergence and increased similarity in their community structures over time.

Similarly, the NMDS analysis for the GY group (Fig. 3C) yielded a stress value of 0.153, an R² of 0.43884 and a P-value of 0.001, confirming the goodness of fit. The GY30 and GY45 groups formed distinct clusters that were separated from the other groups. Meanwhile, the GY1 and GY3 groups were positioned closely together, indicating a high degree of similarity in their fecal microbial composition at these early stages.

Breed-specific differences in microbial composition

Analysis of the fecal microbiota at the phylum, family, and genus levels revealed a conserved core community alongside dynamic shifts influenced by time and host breed (Fig. 4).

Fig. 4.

Dynamic shifts and breed-associated differences in the fecal microbial community composition. (A) Relative abundance of dominant bacterial phyla. (B) Relative abundance of dominant bacterial families. (C) Relative abundance of dominant bacterial genera. The stacked bar charts display the community profiles of CG and GY across six developmental time points, sorted chronologically.

At the phylum level (Fig. 4A), the community was overwhelmingly dominated by Bacillota in all samples. Pseudomonadota and Bacteroidota served as the major subdominant phyla, although their relative abundances showed significant fluctuations at specific time points (e.g., GY3, CG14, CG30). The GY group exhibited a notably higher incidence and variability of low-abundance phyla (e.g., Actinomycetota, Campylobacterota) compared to the CG group, indicating greater community-level heterogeneity.

Finer taxonomic resolution at the family level (Fig. 4B) uncovered clearer successional patterns and inter-group divergence. A marked temporal shift was observed: Enterococcaceae significantly dominated early communities (CG1, GY1), whereas Lactobacillaceae became the predominant family in later stages (CG45, GY45). The abundances of other prevalent families, including Clostridiaceae and Enterobacteriaceae, were highly variable. Notably, the GY group demonstrated a more complex and transient enrichment of several minor families (e.g., Streptococcaceae, Lachnospiraceae) compared to the CG group.

Genus-level analysis (Fig. 4C) detailed this succession, characterized by a clear reciprocal abundance shift between two core genera. The early-dominant Enterococcus was progressively replaced by Ligilactobacillus, which showed a marked increase in relative abundance, culminating in GY45. Several other genera (e.g., Clostridium, Escherichia-Shigella) exhibited transient peaks at intermediate time points. Consistent with the trends observed at higher taxonomic levels, the microbial composition in the GY group was more dynamic and exhibited greater genus-level complexity than that in the CG group throughout the experimental timeline.

Fig. 5 employs a Sankey diagram to visualize the dynamic restructuring of fecal microbiota across taxonomic ranks (from phylum to genus) and developmental stages in CG and GY chicken breeds, revealing distinct successional patterns and breed-specific trajectories. The results delineate an orderly developmental succession, characterized by significant and continuous restructuring of the gut microbiota in both breeds, evidenced by systematic shifts in the relative abundance of core phyla, such as Bacillota and Bacteroidota. Crucially, CG and GY exhibited pronounced breed-specific assembly patterns, demonstrated by divergent community transition pathways and key nodal points beyond simple compositional differences. At finer taxonomic levels, key bacterial genera implicated in host metabolism and health displayed contrasting colonization dynamics between breeds. This indicates that host genetic factors may differentially regulate the temporal assembly and functional succession of specific key taxa in the gut ecosystem.

Fig. 5.

Flow dynamics of the fecal microbiota across taxonomic ranks and developmental stages in CG and GY breeds. The alluvial (Sankey) diagram visualizes the proportional flow and redistribution of major microbial taxa from the phylum to genus level, highlighting distinct community succession trajectories and breed-specific flux patterns over time.

Identification of discriminant and differentially abundant taxa

LEfSe and Kruskal-Wallis H test analyses were integrated to define and quantify the key microbial taxa governing the divergent development of gut microbiota in CG and GY chickens. LEfSe identified temporally-ordered, breed-specific sets of discriminant taxa (biomarkers, Fig. 6A), revealing fundamentally different successional trajectories. The CG succession was rapid and directional: it progressed from early anaerobes (e.g., Clostridium at CG1) through a transient state marked by Escherichia-Shigella and Enterococcus (CG3), to a community enriched with putative beneficial genera like Ligilactobacillus and Butyricicoccus by day 7, culminating in a mature state (CG45) definitively dominated by Lactobacillus and Coriobacteriaceae. Conversely, the GY succession was prolonged and exhibited serial dominance of distinct consortia: initial dominance of Escherichia-Shigella and Enterococcus (GY3) was followed by a rise in Blautia and Ruminococcus_torques_group (GY7), succeeded sequentially by butyrate producers like Faecalibacterium (GY14) and other taxa including Romboutsia (GY30), before reaching a final state co-dominated by Lactobacillus and Streptococcus (GY45). Taken together, both groups CG and GY exhibited distinct temporal succession patterns of microbial enrichment during chick development, while sharing a general trend of early dominance by Clostridia and Escherichia-Shigella, mid-stage enrichment of diverse genera including Candidatus Arthromitus and Ruminococcus, and late-stage prevalence of lactic acid bacteria such as Lactobacillus and Streptococcus.

Fig. 6.

Identification of temporal microbial biomarkers and their abundance shifts in CG and GY series. (A) LEfSe bar plots identifying taxa that are significantly enriched at different longitudinal time points in the CG (left) and GY (right) series, ranked by LDA scores (The default LDA score threshold was > 4.0, and P < 0.05). (B) Bar plots depicting the mean relative abundance of the corresponding enriched taxa across time points, with statistical significance assessed by the Kruskal-Wallis H test (*, P < 0.05; **, P < 0.01; ***, P < 0.001).

Quantitative validation via the Kruskal-Wallis H test (Fig. 6B) confirmed these patterns with statistical rigor, detailing the significant differences in mean relative abundance for ten key genera. The early divergence was anchored by significantly higher abundances of Enterococcus and Escherichia-Shigella in GY at initial time points (p < 0.01). The contrasting mature states were characterized by a consistent and significant enrichment of Lactobacillus and Faecalibacterium in mid-to-late CG stages, alongside a near-exclusive and significant presence of Akkermansia in CG. In contrast, genera such as Bacteroides and Prevotella were generally more abundant in GY.

Distinct developmental trajectories shape fecal microbiota succession

Analysis of fecal samples from Changshun green-shell egg chicken (CG) and Guizhou yellow chicken (GY) across six postnatal time points (days 1, 3, 7, 14, 30, and 45) revealed that gut microbiota assembly is governed by breed-specific processes, leading to divergent ecological structures. Beta diversity analysis showed a clear and significant separation between the CG and GY groups throughout the developmental timeline, indicating distinct breed-specific community structures (Fig. 7A). The trajectory of microbial succession differed markedly. The CG microbiota composition exhibited a more rapid and directional shift, stabilizing earlier. In contrast, the GY microbiota showed a more gradual and variable successional path, with greater within-group dispersion at intermediate stages (days 14-30), suggesting a more stochastic or plastic assembly process.

Fig. 7.

Divergent assembly processes and ecological strategies shape the fecal microbiota of two local chicken breeds during early development. (A) Beta Deviation Index (a metric quantifying deterministic assembly) for CG and GY at six postnatal time points. Asterisks indicate significant differences between breeds at each time point (*, P < 0.05; **, P < 0.01; NS, not significant). (B) Temporal changes in the relative abundance of bacterial types classified by environmental sensitivity: transient, intermittent, and persistent types. The GY group shows a more dynamic shift, while the CG group exhibits a stable core dominated by persistent types. (C) Relative proportions of bacterial taxa classified by ecological strategy: Generalists, Neutrals, and Specialists. The mature CG community is enriched in Specialists, while the GY community shows a higher and more variable proportion of Generalists. (D) Relationship between the averaged relative abundance (log10-transformed) and niche breadth for individual taxa. Generalist species (blue circles) and specialist species (orange triangles) are highlighted. A positive correlation is observed, with generalists predominantly occupying the high-abundance, broad-niche region, especially in the GY group.

Classifying taxa by their prevalence (a proxy for environmental sensitivity) as transient, intermediate, or persistent revealed starkly different developmental dynamics (Fig. 7B). In CG, persistent (core) taxa rapidly became dominant, achieving high relative abundance and stability by day 7-14. In GY chickens, transient taxa maintained a significantly higher proportion throughout the 45-day period, and the establishment of a dominant persistent core was slower and less pronounced. This indicates a more stable, resilient community in CG versus a more dynamic, fluid community in GY. The distinct assembly processes selected for communities with fundamentally different ecological strategies (Fig. 7C). The mature CG community was significantly enriched in specialist taxa, consistent with a stable, highly selective gut environment that rewards niche optimization. Conversely, the mature GY community harbored a significantly higher proportion of generalist species, indicative of an environment where metabolic plasticity and a broad niche breadth confer an advantage, aligning with its more variable community state. The relationship between taxon abundance and niche breadth provided mechanistic support for the observed ecological strategies (Fig. 7D). A positive correlation was found where more abundant taxa possessed broader niche widths. Notably, the generalist species clustered in the high-abundance, broad-niche region. Their dominance in the GY community visually corroborates the strategy of generalism, whereas the CG community’s abundance was distributed among a mix of strategies, with specialists contributing significantly to its stable structure. Together, these results demonstrate that the CG gut imposes a stronger, more consistent deterministic filter, leading to the rapid selection of a stable, specialist-enriched core microbiota. The GY gut, associated with meat-type production, fosters a more variable environment that selects for generalist taxa and maintains higher community turnover, resulting in a distinct and more plastic microbial ecosystem.

Deterministic and stochastic processes in fecal microbiota

The assembly of the fecal microbiota in both chicken breeds was overwhelmingly governed by deterministic processes, with stochastic forces playing a secondary, breed-specific role (Fig. 8). Quantification of ecological processes revealed that homogeneous selection constituted the dominant mechanism, accounting for the largest proportion of community assembly in both breeds (Fig. 8A). This indicates that host-associated filters (e.g., physiology, diet) consistently shaped the microbial communities. The overall contribution of stochastic processes (dispersal limitation, drift) was minor in comparison. The dominance of deterministic selection was further corroborated by a poor fit of the Neutral Community Model (NCM) to the observed data for both CG (R²= 0.183) and GY (R²= 0.110) breeds (Fig.s 8B, 8C). These low R²values signify a substantial deviation from neutral expectations, confirming that stochastic dispersal alone cannot explain the community structure. Notably, breed-specific nuances were observed: although deterministic processes prevailed, the GY breed exhibited marginally higher estimates of migration rate (m) and metacommunity-migration product (Nm) than the CG breed, suggesting a subtly stronger, though still secondary, influence of stochastic dispersal and drift.

Fig. 8.

Integrating ecological processes and neutral model fitting for the fecal microbiota of CG and GY groups. (A) The relative contributions of different ecological processes to community assembly. Fit of the neutral community model to the microbiota of group CG (B) and group GY (C), showing the relationship between occurrence frequency and mean relative abundance. R² indicates the goodness of fit; Nm (N × m) represents the product of metacommunity size and migration rate.

Predicted functional divergence of the fecal microbiota

Predicted functional potential and phenotypic traits of the gut microbiota differed between the CG and GY breeds (Fig. 9). Analysis of Clusters of Orthologous Groups (COG) revealed a consistent functional profile across both breeds, with pathways related to translation, ribosomal structure, and biogenesis (Category J) exhibiting the highest relative abundance (Fig. 9A). Other major functional categories included amino acid transport and metabolism (E), carbohydrate transport and metabolism (G), and transcription (K). In contrast, predicted phenotypic traits showed significant breed-specific variations (Fig. 9B). Notably, the relative abundance of microbes with potentially pathogenic traits was significantly different between the two breeds. Furthermore, traits related to oxygen requirement (e.g., Anaerobic, Aerobic, Facultatively_Anaerobic), presence of mobile elements (Contains_Mobile_Elements), and Gram-staining characteristics (Gram_Positive) all displayed significant divergence. This indicates that while the core functional potential of the microbiota is conserved, the phenotypic attributes pertaining to host interaction and environmental adaptation are distinct between the CG and GY breeds.

Fig. 9.

Functional prediction reveals distinct metabolic potentials and divergent phenotypic features between two breeds. (A) Abundance of microbial genes categorized by COG functional classes. (B) Relative abundance of specific phenotypic traits showing significant inter-breed differences (*, P < 0.05; **, P < 0.01; ***, P < 0.001).

Discussion

The early post-hatch gut microbiota of the two local chicken breeds, Changshun green-shell (CG) and Guizhou yellow (GY), exhibited dynamic assembly with significant temporal variability, consistent with patterns reported in other avian species (Li et al., 2022; Akram et al., 2024). A key finding was the minimal size of the core microbiota, defined as operational taxonomic units (OTUs) persistent across all developmental stages, which accounted for only 2.27% and 0.76% of total OTUs in CG and GY breeds, respectively. This aligns with the view of the chick gut as an open, rapidly assembling ecosystem shaped substantially by host and environmental factors (Kers et al., 2018). The CG breed showed progressive microbial accrual and diversification, indicating a continuously expanding niche that may align with physiological maturation (Zenner et al., 2021). In contrast, the GY breed exhibited an early peak in unique OTUs followed by a contraction, pointing to a stronger selective winnowing process (He et al., 2023). These divergent paths reflect fundamental, breed-specific differences in gut niche formation, likely driven by genetic factors affecting gut physiology, immunity, and nutrient utilization (Maki et al., 2019; Ren et al., 2026). Because the two breeds were reared under the same experimental environment, breed-linked host factors are plausible contributors to the observed divergence. Consequently, the exceptionally small and breed-specific core microbiota highlights the need for longitudinal study designs in poultry microbiome research, as cross-sectional approaches may not capture such dynamic colonization (Li et al., 2022).

Alpha diversity analysis revealed divergent temporal patterns in gut microbiota development between the two chicken breeds. Both breeds showed the expected post-hatch increase in microbial richness and diversity, aligning with general gut maturation processes (Teyssier et al., 2018; Proszkowiec-Weglarz et al., 2022). However, the trajectories differed between breeds. The Changshun green-shell (CG) breed demonstrated a pattern of progressive increase, with species richness and diversity indices generally rising to reach their highest observed values at the final time point (day 45). In the Guizhou yellow (GY) breed, the pattern varied, with species richness showing a notable increase at day 30 that was significantly higher than at earlier time points, while diversity indices followed a more complex temporal profile. Under identical nutritional conditions, these divergent trends may indicate that breed-specific genetic factors influence the rate and pattern of microbial community assembly (Jurburg et al., 2019). The observed differences may be linked to genetic variations affecting gut development, immune function, or other physiological traits that structure the microbial niche (Ren et al., 2026). Therefore, the definition of an optimal or ‘mature’ gut microbiota may be breed-specific, underscoring the importance of tailoring nutritional and health interventions not just to production phase, but to the genetic lineage of the poultry (Ding et al., 2017).

Beta diversity reinforced the presence of distinct breed-associated developmental trajectories. Despite identical rearing conditions, the Changshun green-shell breed exhibited a pattern of structural convergence toward homogeneity over time. In contrast, the Guizhou yellow breed formed distinct community structures at the later sampling points (e.g., day 30 and 45), suggesting stronger niche differentiation or periodic selection (Sheppard et al., 2018; Jurburg et al., 2019). Importantly, these structural patterns, specifically convergence versus state shifts, provide a community-level explanation for the alpha diversity trajectories described earlier. The structural convergence aligns with the progressive increase in diversity observed in the CG breed, while the state shifts correspond to the characteristic pattern of an initial increase followed by a later decline in richness and the more restricted core microbiota in the GY breed, as transitions between distinct community states can impose stronger selective filters (Chen et al., 2020). This evidence collectively demonstrates that breed differences govern not only taxonomic membership but also the fundamental principles of structural succession and assembly dynamics (Maki et al., 2019). Consequently, a comprehensive understanding of gut microbial development, as well as the design of effective microbial management strategies, will need to consider these inherent, breed-dependent ecological landscapes (Stanley et al., 2014).

Analysis of microbial composition across taxonomic ranks reveals that divergent assembly trajectories between the two poultry breeds arise from dynamic, breed-specific successional scripts within a conserved high-level phylogenetic structure (Paul et al., 2021). At the phylum level, while a consistent dominance of Bacillota establishes a fundamental framework for gut homeostasis (Cheng et al., 2025), differential dynamics of subdominant phyla underscore early-life divergence. Specifically, the Changshun green-shell (CG) breed exhibited temporally specific increases in Pseudomonadota, whereas the Guizhou yellow (GY) breed displayed higher variability with a greater incidence of low-abundance phyla such as Actinomycetota. This pattern suggests that the GY breed harbors a more ecologically permissive gut niche (Wang et al., 2023), and this divergence is further resolved at finer taxonomic scales. Although a conserved functional transition from Enterococcaceae to Lactobacillaceae is observed at the family level, the implementation of this transition remains breed-specific. The core succession from Enterococcus to Ligilactobacillus encapsulates a maturation towards a symbiotic community (Such et al., 2021), yet the GY breed exhibits persistently higher genus-level dynamism, characterized by transient peaks of Escherichia-Shigella and Clostridium, which reflects a more variable assembly process (Sekelja et al., 2012) in contrast to the rapid and directional maturation observed in the CG breed.

To elucidate the microbial determinants of these divergent trajectories, LEfSe analysis further identified breed-specific discriminant taxa that define distinct successional blueprints. Succession in the CG breed is characterized by rapid maturation significantly enriched in Lactobacillus, Limosilactobacillus, and Butyricicoccus, which highlights a functional shift toward butyrate production (Boesmans et al., 2018; Brito Sampaio et al., 2022). Conversely, the GY breed displays a prolonged, multiphasic assembly marked by the sequential dominance of Faecalibacterium, Romboutsia, and Streptococcus, suggesting a more gradual niche establishment (Burrows et al., 2025). Early-life divergence in the GY cohort was driven by the overrepresentation of Escherichia-Shigella and Enterococcus, while its terminal mature state was uniquely characterized by Bacteroides and Rikenellaceae_RC9_gut_group, indicating broader ecological diversification (Dou et al., 2026). Collectively, these taxonomic flux patterns confirm that while both breeds converge towards a Lactobacillus-abundant endpoint, the pathways of succession and the stability of intermediate states are fundamentally distinct. These findings indicate that host genetic factors serve as a precise filter of microbial assembly and can coordinate the spatiotemporal hierarchy of taxonomic recruitment as well as the consequent ecosystem functions (Choi et al., 2015).

The early-life gut microbiota of the two chicken breeds assembled along fundamentally divergent successional trajectories, culminating in mature communities with distinct ecological structures. This divergence is best understood as a consequence of breed-specific host physiology imposing selection pressures of differing strength and consistency, a concept well-supported in host-microbiome research (Aruwa et al., 2021; Mallott and Amato, 2021). The CG microbiota demonstrated rapid, directional succession characterized by the early establishment and dominance of a persistent core. This pattern is consistent with a strong, consistent deterministic filter, favoring the enrichment of specialist taxa optimized for specific, stable niches, a process often observed in stable, host-controlled environments (Jurburg et al., 2019). The resultant community reflects a strategy of high efficiency and resilience within a predictable gut environment. Conversely, the GY microbiota followed a more gradual and variable path, maintaining higher proportions of transient members and ultimately enriching for generalist species, which is a signature of ecosystems experiencing greater environmental variability or weaker selective filters (Xiao et al., 2021; Sofia et al., 2026). This outcome suggests a more permissive gut habitat where metabolic plasticity and broad niche breadth are advantageous, leading to a community with higher turnover and inherent plasticity. This breed-dependent continuum, ranging from a stable-specialist to a dynamic-generalist strategy, provides a conceptual framework for hypothesizing that host genetics, likely associated with divergent metabolic demands of production types, modulate the evolutionary pressures acting upon the symbiotic microbiome, which warrants further investigation in the context of precision husbandry (Aruwa and Sabiu, 2024).

Quantifying the assembly mechanisms revealed a clear hierarchy of ecological processes governing succession, where deterministic processes, particularly homogeneous selection, were overwhelmingly dominant in both breeds. This unequivocally establishes host-associated factors as the primary architects of the community, a principle central to contemporary microbiome theory (Feng et al., 2023). However, the subtle but significant breed-level nuance in the residual influence of stochastic processes provides a critical mechanistic insight into the observed ecological divergence. The marginally stronger stochastic signal detected in the GY breed, consistent with patterns noted in other comparative studies (Sekelja et al., 2012), does not indicate random assembly but rather suggests that the deterministic filter imposed by the GY host may be slightly weaker or less consistent. This creates a wider zone of influence where stochastic events, such as dispersal limitation or ecological drift, can leave a more discernible imprint on the emerging community structure. This mechanistic detail aligns with the observed ecological outcome, as a slightly more influential stochastic process can amplify historical contingency and contribute to the higher variability, delayed stabilization, and the competitive success of generalist strategies observed in the GY breed. Therefore, assembly is best described by a unified model where potent deterministic selection sets the primary ecological agenda, while the degree to which stochastic processes can modulate the final structure and dynamics is itself a variable trait influenced by the host (Zhang et al., 2026). This refined understanding highlights that host functional consequences, such as nutrient extraction efficiency or immune interactions, may be closely associated with the balance of host-regulated ecological forces (Bakre et al., 2026).

PICRUSt2 and BugBase analyses predicted key patterns of shared core metabolic potential and differential microbial phenotypic traits between the two breeds. The coexistence of conserved core functions and divergent adaptive traits underscores a hierarchical selective regime in the gut: host-imposed environmental filters preferentially act upon microbial traits mediating niche adaptation and inter-kingdom signaling, whereas essential housekeeping processes remain largely constrained by evolutionary purifying selection (Costello et al., 2012). The conserved enrichment in core functional categories, such as those involved in translation and ribosomal biogenesis, reflects the universal biochemical requirements for maintaining a microbial community within the gut ecosystem. In stark contrast, the significant breed-specific disparities in predicted phenotypes, including oxygen tolerance, Gram-staining characteristics, and the abundance of mobile genetic elements, are not stochastic. They directly index the distinct physicochemical and immunological landscapes of the respective host gut environments (Benson et al., 2010). The phenotypic profile associated with the GY breed, indicative of a more aerotolerant and genetically versatile microbiota, aligns with the previously inferred more variable and less restrictive gut niche that selects for generalist ecological strategies. This is consistent with observations that dynamic environments can favor microbes with greater phenotypic plasticity and horizontal gene transfer potential (Wong and Rawls, 2012; Pan and Yu, 2014). Conversely, the distinct phenotypic signature in the CG breed suggests a stable, anaerobic, and strongly selective environment that enriches for specialized symbionts with traits optimized for persistence in a consistent habitat. Therefore, these functional predictions mechanistically connect the previously described assembly processes to potential physiological outcomes. The breed-specific ecological filters that govern community structure concurrently shape the collective phenotypic repertoire of the microbiota, which in turn likely mediates differential effects on host physiology, including nutrient metabolism and immune modulation (Cui et al., 2023; Jian et al., 2025).

Fecal sampling was utilized in this longitudinal study because it is an efficient and non-invasive method suitable for capturing key developmental time points and temporal dynamics. While it is recognized that microbial communities vary considerably along the gastrointestinal tract and that fecal microbiota may exhibit greater compositional variability compared to specific sites like the cecum, our detection of core taxa and clear temporal patterns supports its utility for delineating broad developmental trends. Future studies should compare successional trajectories across distinct gut compartments (e.g., foregut, midgut, hindgut) under real-world conditions incorporating variables such as farm management, season, and diet. Such a multi-compartment approach in complex environments is essential to fully understand how host genetic frameworks interact with external factors to shape the functional microbiome, thereby translating these ecological insights into precise strategies for gut health management in poultry production. In addition, sampling ended at day 45, before the later grow-out and pre-market stages of yellow-feathered chicken production. Future studies should follow identified individuals where feasible, extend sampling through the actual market age, and collect paired fecal and intestinal-compartment samples to determine whether early breed-associated trajectories persist, converge, or reorganize later in production.

Conclusion

This study identified distinct early-life fecal microbiota succession patterns in two local chicken breeds from Guizhou reared under standardized conditions. The Changshun green-shell (CG) breed exhibited a pattern of rapid, directional succession, culminating in a stable and phylogenetically conserved community. In contrast, the Guizhou yellow (GY) breed displayed a more gradual and variable assembly process, resulting in a community with higher temporal turnover and greater phylogenetic plasticity. This pattern is consistent with a comparatively weaker or more variable host selection pressure, allowing for a greater relative influence of stochastic processes. This fundamental divergence in assembly ecology is further reflected in breed-specific predicted microbial phenotypes, suggesting that host genetic factors may affect functional traits associated with niche adaptation. This study identified distinct early-life fecal microbiota succession patterns in two local chicken breeds from Guizhou reared under standardized conditions. Therefore, the concept of an optimal gut microbiome should be considered breed-specific. This insight underscores the necessity of developing precision husbandry strategies, where nutritional and health interventions are tailored to align with the distinct host genetic framework that shapes the microbial ecosystem, thereby optimizing poultry health and production sustainability.

Authors’ contributions

Zhengdong Wang curated the experimental data, performed formal and statistical analyses, interpreted the results, and drafted the manuscript. He also critically reviewed and revised the manuscript for important intellectual conten. Qian Xiao contributed to the conception of this study, was primarily responsible for data acquisition and sample collection, and performed critical revisions to the final version of the manuscript. Mengyuan Wang and Yong Yue contributed to experimental planning, with a primary focus on the statistical design of this study. They analyzed and interpreted the data, and participated in manuscript writing and critical revision. Sanqiang Du and Fuping Zhang provided the resources and software used in this study, participated in experimental investigation, result validation and interpretation of experimental findings, and offered scientific supervision and research resources for this study. Xiongxiong Cui conceived the study, designed the experiments, contributed to the interpretation of the findings, and critically reviewed and revised the manuscript for important intellectual content. He also obtained funding, administered the project, and supervised the research. All authors have critically reviewed and approved the final version of the manuscript and agree to be personally and publicly responsible for all aspects of the work.

Disclosures

The authors declare that there is no conflict of interest in the submission of this manuscript, and all the authors listed have read and approved the manuscript that is enclosed.

Data availability

The 16S rRNA sequencing raw data have been deposited in the NCBI database (accession number PRJNA1354596).