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. 2026 May 28;8:90. doi: 10.1186/s42523-026-00580-7

Temporal variations in the gut microbiota of François’ langur (Trachypithecus francoisi): implications for adaptation to seasonal dietary change and conservation

Jialiang Han 1, Jinyuan Liu 2, Tao Wang 3, Bingnan Dong 4, Fan Zhang 5, Siyao Li 5, Qixian Zou 5,✉, Diyan Li 2,✉
PMCID: PMC13422307  PMID: 42210378

Abstract

Despite growing research on gut microbiota in wild primates, seasonal functional dynamics of the gut microbiota in this endangered folivorous species remain poorly understood. This study investigates the seasonal variations in the gut microbiota of François’ langur (Trachypithecus francoisi) and their implications for dietary adaptation and conservation. Using shotgun metagenomic sequencing, fecal samples were collected across four seasons within the Mayanghe National Nature Reserve in China (n = 24). The study identified significant seasonal shifts in microbial diversity and composition. While alpha diversity metrics reflecting community evenness (Shannon and Simpson equivalents) remained stable (Padj > 0.05), species richness (Hill number, q = 0) was significantly lower in Fall compared to Spring and Winter (Padj = 0.013). Results revealed that dominant phyla included Bacillota and Bacteroidota, with a significant enrichment of Faecalibacterium during Fall. Functional analysis showed a predominance of carbohydrate metabolism, which remained stable at broad metabolic levels; however, fine-scale functional units (KOs and CAZy families) exhibited distinct seasonal signatures. A moderate correlation between taxonomic and functional profiles (Mantel r = 0.43, P = 0.001) suggests a partial decoupling. These findings highlight the ecological plasticity of the gut microbiota and underscore how taxonomic flexibility enables functional homeostasis, aiding the physiological resilience of endangered primates in fluctuating environments.

Supplementary Information

The online version contains supplementary material available at 10.1186/s42523-026-00580-7.

Keywords: François’ langur, Metagenomic sequencing, Seasonal variation, Dietary adaptation

Introduction

The gastrointestinal tract of mammals harbors a dynamic microbial ecosystem essential for digestion, immune regulation, and ecological adaptation [1, 2]. Beyond basic digestion, these microbial communities act as key modulators of host physiology, allowing species to respond flexibly to environmental pressures [3]. For folivorous primates, the gut microbiome is particularly critical for fermenting fibrous plant polysaccharides into short-chain fatty acids (SCFAs) and detoxifying plant secondary metabolites [4–7]. Comparative studies in other seasonal feeders, such as Howler monkeys (Alouatta) and Snub-nosed monkeys (Rhinopithecus), demonstrate that taxonomic shifts allow hosts to maintain energy balance despite significant fluctuations in food quality [5].

François’ langur (Trachypithecus francoisi) is an endangered colobine primate endemic to the karst limestone forests of southern China and northern Vietnam [8–10]. Due to extensive human-induced habitat loss, the population of the species has declined to fewer than 2,000 individuals [11, 12]. It has been classified as Endangered (EN) by the International Union for Conservation of Nature (IUCN) and is designated as a Class I protected species under China’s National Key Protected Wildlife List [13]. In these habitats, food availability and nutritional quality fluctuate markedly across seasons, forcing the langurs to transition from fresh young leaves in the wet season to lower quality mature leaves and bark during the dry season [8, 14]. Understanding the microbial mechanisms that facilitate survival on these lower quality foods is not only of biological interest but also has significant conservation implications. By identifying the critical taxa and metabolic pathways that support host resilience during resource scarce periods, conservationists can better identify and protect key seasonal food plants, thereby informing more effective habitat restoration and management strategies in karst ecosystems.

While previous 16 S rRNA based studies have established that the François’ langur’s microbiota is dominated by Bacillota and Bacteroidota, these descriptions remain largely taxonomic [15, 16]. A major knowledge gap persists regarding the seasonal functional dynamics specifically, how metabolic pathways switch to support the host’s energy and immune requirements under varying dietary stressors. To address this gap, we employed shotgun metagenomic sequencing to analyze fecal samples from wild François’ langurs across four seasons within the Mayanghe National Nature Reserve (MNNR). We aimed to: (i) characterize seasonal variations in the taxonomic composition and functional profiles of the gut microbiota, (ii) identify microbial taxa and metabolic pathways associated with seasonal dietary shifts, and (iii) investigate the potential role of gut microbiota in mediating host physiological adaptations to seasonal environmental changes. By moving beyond taxonomic composition to functional analysis, this study offers novel insights into the ecological plasticity of endangered primates and the adaptive potential of their microbiome in response to environmental fluctuations.

Materials and methods

Study area and subject animals

The study was conducted in the MNNR (28°37′30″–28°54′20″N, 108°3′58″–108°19′45″E), located in northeastern Guizhou Province, China (Fig. 1). Established in 1987, the reserve covers an area of 311.13 km² and was designated to protect the endangered François’ langur and its habitat. The terrain ranges from 280 to 1,441 m in elevation and features a subtropical monsoon climate with distinct seasonal variation in temperature and precipitation. The MNNR supports diverse vegetation types, including evergreen broadleaf forests, coniferous forests, mixed coniferous-broadleaf forests, deciduous broadleaf forests, bamboo groves, and shrublands. Twenty-four wild groups of François’ langurs inhabit the reserve, with a total population estimated at 500–600 individuals [17].

Fig. 1.

Fig. 1

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(A) Geographic location of the study site in the Mayanghe National Nature Reserve, China. (B) François’ langur feeding on sweet potatoes. (C) François’ langur engaging in playful behavior on a rooftop. (D) François’ langur feeding on corn stalks. (Photograph taken by Xiugang Yan)

The study focused on 17 habituated François’ langurs residing in the southwestern portion of the reserve, at elevations between 330 and 760 m. These langurs exhibit a folivorous frugivorous diet, primarily consuming leaves, fruits, flowers, and buds [8]. At night, they typically roost in cliff caves or on rock platforms and often defecate near their sleeping sites before departing in the morning.

Fecal sample collection and preservation

To minimize disturbance, François’ langurs were observed continuously during early morning defecation periods. Sleeping sites were identified through direct observation on the previous day, and fecal samples were collected from beneath cliffs the following morning. Due to the free ranging of the study population, it was impossible to confirm whether samples originated from the same individuals across seasons. We employed a systematic spatial distancing strategy to ensure sample independence and mitigate pseudoreplication, collection sites were spaced at least 3 m apart, and only samples of similar freshness were collected simultaneously, aiming to represent different individuals within the troop [18]. Sampling was conducted across four distinct seasons: October 2023 (fall), January 2024 (winter), May 2024 (spring), and July 2024 (summer). A total of 24 fecal samples (n = 6 per season) were successfully collected for shotgun metagenomic analysis. This sample size was determined based on the logistical constraints of sampling an endangered species in karst terrain and is consistent with metagenomic standards for identifying core microbial shifts in wild primates. Using sterile gloves and tools, approximately 3–5 g of the inner, uncontaminated portion of each fecal sample were transferred into individually labeled sterile tubes. Following documentation, the collected samples were promptly placed on dry ice and transferred to a -80 °C ultralow-temperature freezer for subsequent analysis. Although individual age and sex metadata were unavailable, broad-scale sampling across collective roosting sites allowed us to characterize a representative community-wide microbial profile across seasons.

Ethics approval

Fecal sampling was conducted with the approval of the MNNR Administration. All procedures were strictly followed non-invasive protocols: no direct contact with the animals occurred, and no methods that could cause harm or distress to François’ langurs were used. To further minimize disturbance, all samples were collected only after the langurs had naturally departed from their sleeping sites.

DNA extraction, library preparation and high-throughput sequencing

Genomic DNA (gDNA) was extracted using the BIOMICS DNA Microprep Kit. DNA integrity and concentration were evaluated via agarose gel electrophoresis, an Agilent 2100 Bioanalyzer, and PicoGreen assay [19]. Libraries were prepared using the NEBNext Ultra II DNA Library Prep Kit. Sequencing was performed on an Illumina NovaSeq 6000 platform, generating 150 bp paired-end reads (PE150) [20]. All procedures followed standard quality control measures.

Bioinformatic analysis

Preprocessing and host removal

Raw reads were quality-filtered using Trimmomatic (v0.39) (adapter removal, sliding window Q15, minimum length 100 bp). To remove host-derived sequences, filtered reads were aligned against the François’ langur reference genome using BWA (v0.7.17), and unmapped reads were extracted via SAMtools (v1.12) for downstream analysis [21].

Metagenomic assembly and gene prediction

Quality controlled reads were de novo assembled using SPAdes (v3.15.3) in meta mode. Assembly quality was evaluated using QUAST (v5.0.2). Open reading frames (ORFs) were predicted from contigs using Prodigal (v2.6.3) in p meta mode [22].

Non-redundant gene catalog and abundance profiling

A non-redundant gene catalog was constructed using Mmseqs2 (identity ≥ 95%, coverage ≥ 90%). Quality-filtered reads were mapped to this catalog using BBMap to calculate gene abundance, which was then normalized to Transcripts Per Million (TPM) [23].

Taxonomic and functional annotation

Taxonomic assignment was conducted by aligning representative genes against the NCBI Non-Redundant (NR) database using Mmseqs2 [24, 25]. Functional annotation was performed by searching against multiple databases, including KEGG, CAZy, CARD, and VFDB.

Statistical analysis

To account for variations in sequencing depth, taxonomic profiles were normalized to relative abundance. Alpha diversity was evaluated using Hill numbers (qD), where the order q determines the sensitivity to relative species abundance: q = 0 (species richness), q = 1 (the exponential of the Shannon entropy), and q = 2 (the inverse of the Simpson concentration). This framework provides a unified ecological interpretation by converting diversity into the “effective number of species”. Beta diversity was assessed based on Bray-Curtis dissimilarity using the vegan package in R (v4.0.5).

Seasonal differences in microbial community structure were quantified using Permutational Multivariate Analysis of Variance (PERMANOVA) and Analysis of Similarities (ANOSIM). Prior to PERMANOVA, the homogeneity of multivariate dispersions was verified using the betadisper function [26]. To further investigate the relationship between taxonomic and functional variations, a Mantel test was performed to evaluate the correlation between the taxonomic (genus-level) and functional (KO-level) beta diversity matrices. Abundance data were Hellinger transformed prior to calculating Bray-Curtis distances, and the correlation was assessed using Spearman’s coefficient. For all statistical tests, p-values were adjusted using the Benjamini-Hochberg False Discovery Rate (FDR) method (q < 0.05). All visualizations were performed using the ggplot2 package [27, 28].

Results

Sequencing quality

A total of approximately 1.99 billion raw paired-end reads were generated from 24 fecal samples of François’ langurs. After quality control and host DNA removal, 1.98 billion high-quality reads were retained, averaging 82.6 million reads per sample (Supplementary Table S1). Metagenomic assembly produced 63,766–148,962 contigs (≥ 1 kb), with N50 values ranging from 2.8 to 11.2 kb and an average length of 4.3 kb. From these assemblies, 15.3 million putative coding sequences were predicted, which were clustered into 1.05 million non-redundant genes to establish a comprehensive gene catalog for functional analyses.

Seasonal characteristics of gut microbiota composition

Significant seasonal variations were observed in the gut microbial composition of François’ langur. At the phylum level (Fig. 2A), Bacillota (0.49 ± 0.09), Bacteroidota (0.19 ± 0.08), Pseudomonadota (0.16 ± 0.09), and Actinomycetota (0.06 ± 0.01) were the predominant taxa across all seasons. At the genus level (Fig. 2B), Faecalibacterium showed marked seasonal differences, with relative abundances increasing from spring (0.03 ± 0.01) to fall (0.12 ± 0.05), then declining slightly in winter (0.07 ± 0.02). Other prevalent genera included Bacteroides (0.05–0.13), Paenibacillus (0.03–0.05), Blautia (0.03–0.04), and Phocaeicola (0.02–0.04), all displaying moderate seasonal fluctuation.

Fig. 2.

Fig. 2

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(A) Histogram of the relative abundance at the phylum level. Relative abundance (3%) of the 20 most abundant bacterial phyla and genera obtained from 24 fecal samples of François’ langur. (B) Histogram of the relative abundance at the genus level. Relative abundance of the 20 most abundant bacterial phyla and genera obtained from 24 fecal samples of François’ langur. Seasonal codes: Spr, spring; Sum, summer; Fal, fall; Win, winter

Seasonally-associated difference analysis of gut microbiota of François’ langur

To evaluate the microbial distribution, an unsupervised clustering heatmap at the genus level was performed. The results showed that while samples did not cluster strictly by calendar season, certain microbial consortia exhibited distinct enrichment patterns across specific seasonal subsets (Fig. 3A). For instance, a cluster comprising specific spring and summer samples (Spr_1, Spr_4, Sum_1) was characterized by the dominance of Pseudomonas, Comamonas, and Acinetobacter. In contrast, a predominant group of winter samples (Win_2, Win_4, Win_5) displayed a shared enrichment of Lachnospira and Blautia. Fall samples bifurcated into two distinct sub-clusters with differing dominant taxa (e.g., Faecalibacterium vs. Oscillibacter), suggesting intra seasonal variation or individual plasticity in the gut microbiota.

Fig. 3.

Fig. 3

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Seasonal variation in gut microbiota composition. (A) Genus-level heatmap; (B) Species-level heatmap. Samples and taxa were hierarchically clustered by abundance profiles and arranged accordingly. Relative abundances were log₁₀-transformed and Z-score standardized across samples. Red indicates higher, and blue lower abundance; (C) LefSe analysis showing taxa with significantly different abundances among seasons (Kruskal–Wallis test, α = 0.05; LDA score > 3.0). Bars indicate taxa enriched in each season, colored by group. Seasonal codes: Spr, spring; Sum, summer; Fal, Fall; Win, winter

The species-level heatmap (Fig. 3B) revealed that functional guilds, rather than entire seasonal cohorts, drove the clustering. Summer samples showed a notable trend of enrichment in fiber- and mucin-degrading species such as R. champanellensis and A. muciniphila. However, the clustering also highlighted significant individual variation within the spring and fall groups, where samples partitioned based on the prevalence of specific taxa like E. coli or F. prausnitzii, rather than forming a single seasonal cluster.

To identify robust biomarkers associated with seasonal shifts, we employed a dual-statistical approach using LEfSe (LDA > 4.0, P < 0.05) and DESeq2 (P < 0.05). A total of 24 taxonomic features were identified by LEfSe as differentially abundant across seasons (Fig. 3C). Among these, 18 features were consistently validated by DESeq2. In spring, both methods confirmed a significant enrichment of taxa within the phylum Pseudomonadota, specifically the genus Escherichia and its associated higher ranks (order Enterobacterales and family Enterobacteriaceae), as well as the genus Barnesiella and its corresponding family Barnesiellaceae. Although LEfSe indicated higher abundances for the order Moraxellales (including family Moraxellaceae and genus Acinetobacter) and the family Comamonadaceae (including genus Comamonas and class Betaproteobacteria), these specific taxa did not reach statistical significance in the DESeq2 assessment. For summer samples, the phylum Bacteroidota, class Bacteroidia, and order Bacteroidales were consistently identified as the most characteristic biomarkers. In fall, while LEfSe suggested an expansion of the phylum Bacillota and class Clostridia, these high-level taxa were not validated by DESeq2. However, both tools robustly identified the order Eubacteriales, family Oscillospiraceae, and the genus Faecalibacterium and Caproiciproducens as key biomarkers for the fall season. In winter, the genus Roseburia was the sole biomarker consistently identified by both methods.

Community diversity analysis of François’ langur in different seasons

To characterize the seasonal variations in the gut microbiota of François’ langur, alpha diversity was assessed using Hill numbers. Our results indicated that while the estimated community evenness remained relatively consistent throughout the year, as evidenced by the non-significant seasonal differences in the Shannon and Simpson equivalents (Padj > 0.05; Supplementary Table S2、S3). The species richness (Hill number) exhibited significant seasonal fluctuations (Padj < 0.05). Specifically, richness was significantly lower in Fall (Mean = 6657.5) compared to Spring (Mean = 7051.8, Padj = 0.013) and Winter (Mean = 6826.2, Padj = 0.013) (Fig. 4A).

Fig. 4.

Fig. 4

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Seasonal variation in α- and β- diversity of gut microbiota. (A) Boxplots of α-diversity indices (Hill numbers) showing differences among seasons; asterisks indicates significant differences (p < 0.05, FDR-adjusted). (B) Principal coordinates analysis (PCoA) based on Bray–Curtis distances illustrating β-diversity patterns among seasons; percentages on axes indicate the proportion of variance explained by each principal coordinate. (C) PCoA plot based on Jaccard distances illustrating microbial community structures among seasons; ellipses represent 95% confidence intervals for each grou. (D) Non-metric multidimensional scaling (NMDS) based on Bray–Curtis distances; the stress value indicates the goodness of fit. Seasonal codes: Spr, spring; Sum, summer; Fal, fall; Win, winter

The PERMANOVA based on Bray-Curtis distance revealed that season significantly influenced the gut microbiota (R2 = 0.276, p = 0.001). The PCoA plot (Fig. 4B) showed clear seasonal clustering, with the first two axes explaining 44.8% of the total variation. Pairwise PERMANOVA confirmed that the Spring microbiota was distinct from all other groups (e.g., Spring vs. Fall: R2 = 0.273, p = 0.003; Spring vs. Summer: R2 = 0.188, p = 0.007), while Summer, Fall, and Winter showed closer proximity. Analysis based on Jaccard distance also indicated significant overall differences across the four seasons ((R2 = 0.256, p = 0.001; Fig. 4C). Pairwise comparisons further revealed that while most seasonal transitions led to significant shifts in species presence/absence (p < 0.05), the community structure remained relatively stable between Summer and Winter (p = 0.129). These findings were corroborated by NMDS analysis (Fig. 4D, Stress = 0.13).

Microbiome functional annotation and classification revealed the higher demand for degrading plant polysaccharides

To investigate the functional characteristics of the gut metagenome of Francois’ langurs across four seasons, we analyzed KEGG pathways and Carbohydrate-Active Enzymes (CAZy). At the KEGG Class 1 level, functional pathways were predominantly associated with Metabolism (mean 62.9%), followed by Genetic Information Processing and Environmental Information Processing (Fig. 5A). Further analysis at the KEGG Class 2 level (Fig. 5B) identified Carbohydrate metabolism, Amino acid metabolism, and Energy metabolism as the dominant categories, collectively accounting for 38.2% of the functional profile. Kruskal-Wallis tests revealed that these broad functional classes remained statistically stable across seasons (p > 0.05; Fig. 5C), indicating a conserved functional core in the Francois’ langur gut microbiota.

Fig. 5.

Fig. 5

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Seasonal dynamics of microbial functional categories and carbohydrate-active enzymes. (A–B) Functional composition: Relative abundance and quantification of KEGG functional categories at Level 1 (A) and Level 2 (B) across four seasons. (C) Seasonal variation: Violin plots showing the distribution and statistical significance of major functional pathways across spring, summer, fall, and winter. (D) Heatmap of key genes: Seasonal distribution of representative KO (KEGG Orthology) genes. Color intensity indicates normalized abundance (Z-score). (E) LEfSe analysis: Identification of biomarker KO genes with significant seasonal enrichment (LDA Score > 2.0). Colors represent the season in which the gene is significantly enriched. (F) CAZymes profile: Relative abundance (left) and absolute quantification (right) of Carbohydrate-Active Enzymes, including AA (Auxiliary Activities), PL (Polysaccharide Lyases), CE (Carbohydrate Esterases), CBM (Carbohydrate-Binding Modules), GT (Glycosyl Transferases), and GH (Glycoside Hydrolases)

A heatmap of the top KEGG Orthologs (KOs) (Fig. 5D) showed that K03106 (DNA-directed RNA polymerase subunit beta) was consistently enriched across all seasons, with relatively higher abundance observed in Spring and Fall. To identify statistically significant seasonal biomarkers, we performed LEfSe analysis at the KO level, which revealed distinct functional signatures for each season (LDA > 2.0, p < 0.05; Fig. 5E).

The study investigated the seasonal compositional changes of CAZy (Carbohydrate-Active Enzymes) to explore the impact of food resource fluctuations on microbial function. The results showed that gene abundances belonging to the Glycoside Hydrolase (GH) and Glycosyl Transferase (GT) families were extremely high, whereas the abundance of the Auxiliary Activity (AA) family was notably low (Fig. 5F). This suggests that the gut microbiota of Francois’ langurs has a high demand for degrading plant polysaccharides and a lower metabolic requirement for complex compounds such as lignins. While the overall proportions of CAZy classes remained consistent across seasons, LEfSe analysis identified specific GH and CE families that were significantly enriched in different seasons (p < 0.05), supporting the specialization of the microbial enzymatic repertoire in response to seasonal dietary fiber content. The Mantel test revealed a moderate but significant correlation between genus-level taxonomic beta-diversity and KO-level functional beta-diversity (Mantel r = 0.43, P = 0.001). Despite the statistical significance, the relatively low r value suggests a substantial decoupling between the community’s taxonomic composition and its functional potential across different seasons.

Discussing

This study represents the first comprehensive investigation of seasonal variation in the structure and function of the gut microbiota in François’ langur across a complete annual cycle using metagenomic sequencing. The results revealed observable seasonal shifts in microbial composition and functional potential, suggesting that the gut microbiome may exhibit ecological plasticity in response to fluctuating environmental conditions. The findings demonstrate the power of metagenomic approaches to capture fine-scale temporal dynamics in microbial communities, providing critical insights into host ecology, nutrition, and adaptive strategies in the wild.

Seasonal shifts in microbial composition at the phylum and genus levels

The findings revealed distinct seasonal shifts in microbial composition at both the phylum and genus levels. At the phylum level, the community remained stable, dominated by Bacillota, Bacteroidota, Pseudomonadota, and Actinomycetota, with Bacillota contributing nearly half of the total abundance, reflecting a core microbiota involved in fermentation, SCFA production, and complex carbohydrate degradation [29–31]. The genus-level composition exhibited significant seasonal variation, Faecalibacterium was markedly enriched during fall and winter, likely reflecting increased consumption of mature leaves and fruits, which supply complex fibers that this butyrate-producing bacterium ferments into short-chain fatty acids, thereby supporting host energy homeostasis [8]. These seasonal shifts in gut microbiota may enhance intestinal barrier function and immunomodulatory capacity, facilitating host adaptation to colder environments, consistent with observations reported in wild primates [32–35]. Bacteroides peaked in summer, may be attributed to François’ langurs consuming not only mature leaves but also starch-rich crops such as sweet potatoes and corn, which they frequently raid from nearby farmlands [8, 36, 37]. Bacteroides are adapted to degrade complex carbohydrates, particularly dietary starches [38], and similar increases have been observed in primates exposed to human-associated foods [39, 40]. The result suggests that crop-raiding behavior may significantly shape the seasonal gut microbial profile [31, 41]. Other genera, such as Paenibacillus, Blautia, and Phocaeicola, remained relatively stable in four seasons, indicating potential functional redundancy or niche flexibility [42]. Acinetobacter was generally rare but spiked in two samples (Spr_1, Spr_4). Occasional foraging near household waste or human-associated food items has been observed at the study site with relatively high human disturbance, which could represent one possible explanation for the presence of these taxa. However, the direct link between human-derived food sources and the observed microbial pattern cannot be confirmed in this study. Seasonal dietary shifts may also contribute to this pattern. In many wild herbivores, the transition from winter to spring is accompanied by changes in food availability and the consumption of newly emerging plant materials, which can alter gut microbial composition [32]. We also observed that Comamonas had a high abundance in PET microplastic fragmentation and aromatic pollutant metabolism (1. Mechanisms of Polyethylene Terephthalate Pellet Fragmentation into Nanoplastics and Assimilable Carbons by Wastewater Comamonas; 2. Complex regulation in a Comamonas platform for diverse aromatic carbon metabolism). Human food provisioning and environmental disturbance can significantly reshape primate gut microbiota imbalance [43–45]. In Rhinopithecus bieti, tourist feeding increased the Firmicutes/Bacteroidetes ratio, reduced microbial network stability, and enriched antibiotic resistance genes [46]. Baboons in disturbed habitats showed reduced microbial diversity and higher pathogen loads [47]. Environmental taxa such as Acinetobacter, common in soil and human-associated settings, often proliferate in hosts exposed to polluted or antibiotic-rich environments, suggesting transmission via environmental contact [48].

Species-level signatures reflect dietary and environmental transitions

The analysis of genus- and species-level clustering revealed significant seasonal patterns, emphasizing that while individual and ecological variability modulate gut microbial structure, season remains a primary driver in Francois’ langurs. Spring samples frequently clustered with enrichment in Escherichia coli, Acinetobacter lwoffii, and Comamonas, highlighting potential transient Proteobacteria blooms during environmental transition. Summer communities were dominated by fiber- and mucin-degrading taxa such as Ruminococcus champanellensis, Bacteroides thetaiotaomicron, and Akkermansia muciniphila, indicative of enhanced carbohydrate utilization [32, 49]. The bifurcation of Fall profiles into clusters enriched either in spore-forming Paenibacillus or in anti-inflammatory taxa like Faecalibacterium prausnitzii further underscores the inter-individual dietary or habitat heterogeneity within the population. Winter samples were dominated by short-chain fatty acid (SCFA)-producing Firmicutes, including Roseburia, Blautia, and Lachnospira, suggesting an enhanced capacity for microbial fermentation as a compensatory energy source during periods of reduced food availability [50–52]. Over 50% of the winter diet in MNNR of François’ langurs consisted of fruits and seeds, which are rich in fermentable fibers and amounts of lipids [8]. LefSe analysis identified 23 season-specific biomarkers—including Pseudomonadota in spring, Bacteroidota in summer, and Roseburia in fall/winter—confirming that microbial assemblages shift in tandem with seasonal ecological and dietary changes.

Microbial diversity reflects temporal community stability and change

Gut microbial diversity in François’ langurs exhibited distinct seasonal shifts, likely shaped by transitions in diet and environmental conditions. Contrary to our initial expectations, alpha diversity metrics reflecting community evenness and dominance (Shannon and Simpson equivalents) remained stable across seasons (Padj > 0.05). This suggests that the core microbial assembly of François’ langurs possesses high resilience, maintaining a balanced functional backbone despite environmental fluctuations. However, we observed a significant decline in species richness (Hill number, q = 0) during Fall compared to Spring and Winter (Padj = 0.013). This indicates that seasonal transitions primarily drive the turnover of rare or low-abundance taxa rather than shifting the overall community evenness. The highest observed species richness aligns with the high dietary diversity of François’ langurs in the MNNR during Spring, where a wider variety of plant resources becomes available [8]. This suggests that the “expansion” of the microbial community in Spring is characterized by the recruitment of specialized rare taxa, such as members of the Pseudomonadota identified in our LEfSe and DESeq2 analyses, to exploit diverse seasonal resources, rather than a fundamental restructuring of the dominant species. Similar patterns have been observed in other wild primates, where microbiota richness increases with resource availability and dietary variety [15, 32]. Beta diversity analyses supported the conclusion that gut microbial communities undergo significant compositional restructuring across seasons. The distinct clustering of Spring samples in the PCoA and NMDS plots, in contrast to the relatively closer proximity between Summer, Fall, and Winter, indicates a highly specialized microbial community structure during the Spring period. This seasonal restructuring is consistent with our identification of robust biomarkers, particularly the consistent enrichment of Bacteroidales in Summer, Faecalibacterium in Fall, and Roseburia in Winter. The separation of Spring samples from other seasons, coupled with higher species richness, further underscores the unique ecological niche the gut microbiota occupies during the period of peak resource availability. This seasonal restructuring is consistent with observations in other primate species, such as baboons and great apes, where the microbiota dynamically adjusts to seasonal ecological pressures [53, 54].

Functional adaptation of the microbiome across seasons

The gut metagenome of François’ langurs reflects a specialized folivorous adaptation, essential for extracting energy from fibrous plant materials [8, 55, 56]. The consistent dominance of metabolism related pathways, such as carbohydrate, amino acid, and energy metabolism across all seasons underscores their role in supplementing host nutrition. The results showed that these broad functional categories (KEGG Class 2) did not exhibit significant seasonal shifts (p > 0.05), this stability highlights the maintenance of a conserved functional core that supports fundamental physiological requirements regardless of environmental fluctuations. The macro-level stability does not preclude fine scale seasonal adaptation. As identified by LEfSe analysis, the significant enrichment of specific KOs and CAZy families across different seasons (p < 0.05) reveals a high degree of functional flexibility. The specialization of the microbial enzymatic repertoire through shifts in GH families allows the community to fine tune its degradative capacity in response to the varying fiber and secondary metabolite profiles of young leaves in Spring versus mature foliage in Fall [55, 57]. The consistently high abundance of GHs and GTs further emphasizes the necessity of degrading plant polysaccharides, a defining trait in leaf-eating primates [57–59], while the low abundance of AA enzymes suggests a limited capacity for lignin degradation, potentially explaining the langurs’ avoidance of highly lignified plant tissues. Collectively, our findings suggest a “stable yet flexible” microbial strategy: while the core metabolic framework remains robust to ensure survival, fine-scale microbial adjustments provide the necessary plasticity for François’ langurs to adapt to seasonal dietary transitions. The partial decoupling between taxonomic and functional variations, as evidenced by the Mantel test (r = 0.43), highlights a high degree of functional redundancy within the François’ langur gut microbiota. This redundant framework suggests that different microbial taxa within the community occupy overlapping metabolic niches. Such stability in core metabolic pathways, despite seasonal taxonomic shifts, underscores the ecological plasticity of the gut microbiome, ensuring consistent energy harvest and nutrient processing for the host throughout the year.

Despite the robust seasonal patterns identified, this study has certain limitations that should be acknowledged. Due to the challenges of sampling in the wild, individual identity could not be confirmed for each fecal sample, our results reflect seasonal variation at the population level rather than within-individual longitudinal changes. The distinct seasonal clustering and significant PERMANOVA results observed in our beta diversity analysis suggest that dietary shifts remain the primary driver of microbial variation in this population, outweighing individual-level noise. To further untangle individual-level effects from seasonal dynamics, future studies should incorporate non-invasive genetic individual identification, such as microsatellite or SNP analysis of fecal DNA, to allow for a more high-resolution, longitudinal assessment of the Francois’ langur microbiome.

Conclusion

This study demonstrates that the gut microbiota of François’ langurs exhibits a stable yet flexible response to seasonal environmental and dietary transitions. The results reveal that while the overall functional core remains conserved to ensure fundamental energy extraction from fibrous diets, specific taxonomic assemblages and fine-scale functional units (KOs and CAZy families) undergo statistically significant seasonal shifts. In particular, the elevated species richness in Spring and the enrichment of specialized fermentative taxa in Winter highlight the community’s plasticity in maximizing nutrient acquisition from seasonally available food resources. The study is limited by the lack of individual metadata (e.g., age and sex) and the inability to track longitudinal changes within specific individuals. Nonetheless, the significant seasonal clustering observed across the population underscores diet as a primary driver of microbial dynamics. These insights emphasize the importance of maintaining habitat diversity to provide a sufficiently broad dietary niche for this endangered primate. Understanding the microbial mechanisms that facilitate adaptation to seasonal resource scarcity provides a critical physiological baseline for the management and long-term survival of François’ langurs in their natural habitats. By maintaining a resilient and flexible gut microbiome, these primates are better equipped to cope with the ecological pressures of their specialized folivorous niche.

Electronic Supplementary Material

Below is the link to the electronic supplementary material.

Supplementary Material 1 (13.3KB, xlsx)
Supplementary Material 2 (10.6KB, xlsx)
Supplementary Material 3 (10.7KB, xlsx)

Acknowledgements

We would like to thank the Mayanghe National Nature Reserve Administration for their assistance with data collection and fieldwork.

Author contributions

DY and QX designed the study and revised the manuscript. JL analyzed the data and wrote the manuscript. TW, FZ and SY collected the samples. All authors read and approved the submitted manuscript.

Funding

This work was supported by the Administration of Mayanghe National Nature Reserve (Grant No. MYH2025-HT027).

Data availability

The data in this study have been deposited in the NCBI SAR under accession number PRJNA1335193.

Declarations

Ethical approval

The animal study was conducted under the approval of the Administration of Mayanghe National Nature Reserve (Permit No. 202410), in strict accordance with national regulations and institutional policies governing the ethical use of wild animals in research.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Qixian Zou, Email: myhbhqzqx@163.com.

Diyan Li, Email: lidiyan@cdu.edu.cn.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1 (13.3KB, xlsx)
Supplementary Material 2 (10.6KB, xlsx)
Supplementary Material 3 (10.7KB, xlsx)

Data Availability Statement

The data in this study have been deposited in the NCBI SAR under accession number PRJNA1335193.

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