Abstract
The gut microbiota plays a critical role in plant digestion, nutrient absorption, and ecological adaptation in herbivores. However, how gut microbiota and diet jointly influence ecological niche differentiation in sympatric species remains unclear. Here, we use metagenomic sequencing and plant trnL (UAA) fragment sequencing to examine the gut microbiota and dietary composition of sympatric Tibetan argali (Ovis ammon hodgsoni) and blue sheep (Pseudois nayaur) in the Kunlun Mountains of the Qinghai-Tibet Plateau. Despite inhabiting similar environments, the two species harbor distinct microbial compositions and functional profiles. Interestingly, higher dietary diversity does not correspond to higher microbial diversity. Tibetan argali, despite having a simpler diet, possesses a more diverse and flexible gut microbiome. In contrast, blue sheep show broader dietary preferences and stronger microbial metabolic adaptation to glycan biosynthesis and metabolism. These findings reveal significant associations between gut microbiota composition, function, and diet, supporting a microbial contribution to trophic niche differentiation. Our results highlight distinct microbial-dietary strategies in sympatric herbivores and underscore the role of the gut microbiome in ecological adaptation and species coexistence.
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Introduction
The gut microbiome in animals is a complex ecosystem composed of various microorganisms, including archaea, bacteria, fungi, viruses, and bacteriophages1. It plays a crucial role in host physiological functions such as nutrient utilization2, immune regulation3, and metabolism4. Studies indicate that the gut microbiome dynamically adapts to changes in the host and environment, establishing a symbiotic and coevolutionary relationship with the host5,6,7. Among the factors influencing the gut microbiome, host phylogeny and diet are key determinants of microbial composition and function8,9. For ruminants, their unique gastrointestinal structure supports a diverse microbiome, essential for plant digestion10,11,12. This interaction between the microbiome and the host makes ruminants ideal models for studying diet, gut microbiota, and host adaptation13. Much of the research on domesticated ruminants has focused on optimizing the gut microbiome to improve agricultural productivity14,15. However, due to challenges in sample collection and obtaining physiological data, research on wild ruminants has primarily examined factors such as captive versus wild environments16, seasonality17, habitat differences18, diet19, and host genetic background20 in relation to the gut microbiome. These studies help explain host environmental adaptation mechanisms, which are critical for wildlife conservation21,22.
Ecological niche differentiation is a key mechanism that enables sympatric species to coexist, thereby maintaining ecosystem stability23,24,25. Herbivores, particularly ungulates with similar diets, often share overlapping spatial resources26, which can lead to interspecies competition. To reduce competition and achieve coexistence, these species undergo niche differentiation27, which manifests in dietary, temporal, and habitat-based differentiation28,29. The argali (Ovis ammon) is the largest wild sheep species in the world and belongs to the subfamily Caprinae, family Bovidae. The Tibetan argali (Ovis ammon hodgsoni), a subspecies of O. ammon, is listed in Appendix I of the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) and classified as “Near Threatened” by the IUCN30. Due to its large and distinctive horns, Tibetan argali experienced significant population declines in the past century because of overhunting31. Although it has a wide distribution, its populations are highly fragmented and patchily distributed across the Qinghai–Tibet Plateau, making wild sightings extremely rare32. The blue sheep (Pseudois nayaur), a nationally protected species in China (Category II), is a typical alpine ruminant widely distributed across the Qinghai–Tibet Plateau and surrounding regions33. As a key prey species for large carnivores such as the snow leopard, blue sheep play an essential role in maintaining the stability of high-altitude ecosystems34.
Studies have shown that the two species exhibit largely overlapping ranges on the Qinghai–Tibet Plateau, making them sympatric, yet they display pronounced ecological niche differentiation in both habitat use and dietary preferences35. The argali, being larger-bodied with long legs, primarily inhabits open alpine meadows and gently sloping scree areas, relying on speed to evade predators and typically avoiding steep mountainous terrain35,36. In contrast, blue sheep have shorter, stronger limbs adapted for climbing, and are well suited to rugged terrain, often using steep cliffs and escarpments as refuges from predators34. Such ecological niche differentiation is not only reflected in their spatial habitat use but may also be further reinforced through dietary divergence. Given the critical role of gut microbiota in plant digestion, dietary selection, and host environmental adaptation in herbivorous animals37, we hypothesize that the gut microbiome may also contribute to the niche differentiation between Tibetan argali and blue sheep38—particularly by interacting with diet to indirectly influence host foraging behavior and nutritional adaptation. For instance, Greene et al. 39 showed that lemurs’ gut microbiomes adapt to their dietary traits, fostering niche differentiation among lemur species. Moreover, the symbiotic relationship between host and microbiota suggests that gut microbial communities may exhibit plastic responses to environmental changes, thereby contributing to ecological adaptability that facilitates species coexistence40.
Despite preliminary evidence in some wild species, studies exploring the role of the gut microbiome in ecological niche differentiation and environmental adaptation of Tibetan argali and blue sheep remain scarce—particularly for argali. To address this, we conducted a comparative study in the Kunlun Mountains region of the Qinghai–Tibet Plateau using metagenomic sequencing and plant DNA metabarcoding. We examined gut microbial composition, functional profiles, antibiotic resistance genes, and dietary components to investigate diet–microbiota interactions and their potential role in species-specific ecological adaptation. Our results reveal distinct gut microbial compositions and dietary preferences between Tibetan argali and blue sheep despite their sympatric distribution. Notably, Tibetan argali exhibited a more diverse gut microbiome despite a simpler diet, whereas blue sheep showed stronger microbial metabolic adaptations to glycan metabolism. These findings suggest that diet–microbiota associations may contribute to trophic niche differentiation, supporting ecological coexistence of these two alpine herbivores.
Results
Characteristics of the metagenomic dataset
Sequencing of 18 samples yielded a total of 258,608.60 Mbp of raw data, with an average of 14,367.14 Mbp per sample. After quality control, 253,627.91 Mbp of clean data were obtained, averaging 14,090.44 Mbp per sample. The clean data accounted for 98.07% of the raw data, indicating that it met the quality requirements for subsequent analysis. The rarefaction curves of all samples plateaued with increasing sequencing depth, indicating that the sequencing depth was sufficient for downstream analyses and that the data were representative (Supplementary Fig. 1). A total of 36,426,977 Open reading frames (ORFs) were predicted across the 18 samples (Supplementary Table 1), and redundant sequences were removed, resulting in 12,153,115 genes with an average sequence length of 559.54 bp. The Venn diagram showed that the blue sheep group had more genes than the Tibetan argali group, with 5,199,398 shared genes, accounting for 42.78% of the total gene count (Fig. 1a). On average, the Tibetan argali group had about 2,925,128 genes per sample, while the blue sheep group had approximately 3,210,050 genes per sample.
a Venn diagram showing shared and unique genes between the Tibetan argali and blue sheep groups. b Bar plot displaying the relative abundance of gut microbiota at the phylum level for each sample. c Box plot comparing the relative abundance ratio of Bacillota to Bacteroidota between groups. d Heatmap illustrating the genus-level relative abundance of gut microbiota across all samples. Samples labeled with “PYA” represent Tibetan argali, and “YYF” represent blue sheep. *: P < 0.05, **: P < 0.01, calculated by Wilcoxon rank-sum test. n = 9 biologically independent samples per group.
Gut microbiome composition and abundance analysis
Based on the sequencing results, we calculated the relative abundance of the gut microbiota at both the phylum and genus levels for the two groups. The top 20 phyla were selected to generate a stacked bar plot (Fig. 1b), and the top 30 genera were visualized in a heatmap (Fig. 1d). At the phylum level, the dominant phyla (relative abundance >1%) were Bacillota (formerly Firmicutes) (67.77% ± 6.63%), Bacteroidota (25.43% ± 7.01%), and Verrucomicrobiota (1.37% ± 1.13%). At the genus level, 12 genera had a relative abundance >1% (Table 1). We analyzed the relative abundance ratio of Bacillota to Bacteroidota and the intergroup differences (Fig. 1c). The results indicated that the relative abundance ratio of Bacillota to Bacteroidota was significantly higher in the Tibetan argali group than in the blue sheep group (Welch’s t-test, t = 4.567, df = 9.028, P < 0.01). The intergroup differences in dominant phyla and genera are summarized in Table 1. The analysis showed that the average relative abundance of Bacillota, Actinomycetota, and Euryarchaeota was higher in the Tibetan argali group than in the blue sheep group (Wilcoxon rank-sum test, P < 0.05), while the relative abundance of Bacteroidota was lower in the Tibetan argali group (Wilcoxon rank-sum test, P < 0.05). Additionally, among the recognizable dominant genera, the relative abundance of Alistipes and Bacteroides was higher in the blue sheep group than in the Tibetan argali group (Wilcoxon rank-sum test, P < 0.05), while the relative abundance of Ruminococcus and Tyzzerella was lower in the blue sheep group (Wilcoxon rank-sum test, P < 0.05).
Analysis of gut microbiome α and β diversity
Comparative analysis of the α and β diversity of gut microbiota at the species level revealed differences in the composition and structure of the microbiomes between Tibetan argali and blue sheep. For α diversity, the results showed that the Shannon–Wiener index (Welch’s t-test, t = 3.401, df = 11.24, P < 0.05) and Pielou evenness index were significantly higher in the Tibetan argali group than in the blue sheep group (Welch’s t-test, t = 3.438, df = 10.86, P < 0.05) (Fig. 2a). The β diversity analysis indicated a clear separation between the Tibetan argali and blue sheep groups (Fig. 2b). Additionally, Analysis of Similarities (ANOSIM) confirmed significant differences in the gut microbiota structure between the two groups, with intergroup differences being greater than intragroup differences (ANOSIM analysis, R = 0.791, P = 0.001). To further identify gut microbiota biomarkers with significant differences between the two groups, we performed LEfSe (Linear Discriminant Analysis Effect Size) and identified 15 bacterial biomarkers with an LDA score > 4. In the Tibetan argali group, the significant biomarkers included Bacillota, Eubacteriales, Clostridia, Lachnospiraceae, and s_Lachnospiraceae_bacterium, while the biomarkers identified in the blue sheep group were primarily associated with Bacteroidota bacteria (Fig. 2c).
a Comparison of α-diversity indices between groups using Welch’s t-test. Boxplots show the median (center line), 25th and 75th percentiles (box bounds), and the full range from minimum to maximum (whiskers). b Non-metric multidimensional scaling (NMDS) plot showing gut microbial community structure. c Histogram of linear discriminant analysis (LDA) scores identifying group-specific microbial biomarkers (LDA score > 4). *: P < 0.05, **: P < 0.01. n = 9 biologically independent samples per group.
Functional analysis of the gut microbiome
To investigate the functional differences in the gut microbiomes of Tibetan argali and blue sheep, we annotated the 12,153,115 UniGenes obtained from metagenomic sequencing using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. A total of 5,989,667 UniGenes (49.29%) were successfully annotated and assigned to 9785 KEGG ortholog groups, and 3,212,229 UniGenes (26.43%) were mapped to 5426 KEGG pathways. At the Level 1 classification, Metabolism was the most abundant functional category (50.08% ± 0.46%), followed by Genetic Information Processing (17.24% ± 0.70%) and Environmental Information Processing (12.84% ± 0.58%) (Fig. 3a). At Level 2, the most abundant functions were Global and Overview Maps (26.83% ± 0.15%), followed by Carbohydrate Metabolism (9.52% ± 0.08%) and Amino Acid Metabolism (5.78% ± 0.06%) (Supplementary Table 2). Intergroup differences in KEGG Level 2 pathways revealed that the relative abundance of Glycan Biosynthesis and Metabolism was significantly higher in the blue sheep group than in the Tibetan argali group (Wilcoxon rank-sum test, P < 0.01), whereas the relative abundance of Membrane Transport, Signal Transduction and Xenobiotics Biodegradation and Metabolism functions showed the opposite trend (Fig. 3a).
a Comparison of KEGG level 1 and level 2 functional categories. Color represents KEGG level 1 categories; corresponding level 2 functions are shown with matching colors. b Heatmap of Spearman correlations between dominant bacterial genera and KEGG level 2 functional categories. c Relative abundance of key metabolic pathways (KEGG Orthologies, KOs) related to ABC transporters between groups. d LEfSe analysis of KEGG pathways between the two groups (LDA Value > 3, P < 0.01). *: P < 0.05, **: P < 0.01, calculated by Wilcoxon rank-sum test. n = 9 biologically independent samples per group.
The correlation heatmap shows the relationship between the top ten functional categories at Level 2, with significant intergroup differences, and the dominant genera (Fig. 3b). Among the identifiable genera, Bacteroides and Tyzzerella were significantly correlated with nearly all ten functions (Wilcoxon rank-sum test, P < 0.05). Notably, the Glycan Biosynthesis and Metabolism function was significantly positively correlated with Bacteroides (Spearman analysis, |R| > 0.7, P < 0.01). Additionally, by combining LEfSe analysis for different KEGG pathway (LDA Value > 3, P < 0.01), we found that Membrane Transport, Signal Transduction, Translation, and Cell Motility were the four most significantly different functional pathways at Level 2 (Supplementary Table 3). ABC transporters were identified as the most significantly different functional category at Level 3 (Fig. 3d). We then conducted a metabolic pathway difference analysis for the ABC transporters function, revealing the primary differential metabolic pathways related to proteases in Tibetan argali and blue sheep (Fig. 3c and Supplementary Table 4). The results showed that in the Tibetan argali group, ABC transporters were mainly associated with basic metabolic functions, such as iron transport (K02012), carbohydrate transport (K09690, K10439, K10440, K10441), and energy metabolism (K16785, K16786). In contrast, in the blue sheep group, ABC transporters were primarily associated with amino acid transport, metal ion transport, and pathways related to immunity and drug resistance.
Analysis of gut microbiome antibiotic resistance genes
The results showed that 254,205 UniGenes were successfully annotated using the Comprehensive Antibiotic Resistance Database, identifying a total of 763 antibiotic resistance ontologies (AROs). Among them, 692 AROs were shared between the Tibetan argali and blue sheep groups, accounting for 90.69% of the total AROs, indicating similar antibiotic resistance gene compositions between the two species. However, diversity analysis of AROs revealed significant differences between the two groups, both in terms of α and β diversity. Supplementary Fig. 2a, b show that the blue sheep group exhibited higher ARO diversity than the Tibetan argali group (Welch’s t-test, t = 7.746, df = 11.19, P < 0.01), and the difference in ARO composition structure between the two groups was also significant (ANOSIM analysis: R = 0.785, P = 0.001). The relative abundance of the top ten AROs in each fecal sample is shown in Fig. 4a. The macB gene (8.29% ± 0.45%) was the most abundant resistance gene across all samples, and its abundance was significantly higher in the Tibetan argali group than in the blue sheep group (Wilcoxon rank-sum test, P < 0.01) (Fig. 4b). At the antibiotic class level, the Multidrug category had the highest number and abundance of genes, accounting for 32.97% ± 0.82% of all categories, with significantly higher levels in the blue sheep group compared to the Tibetan argali group (Wilcoxon rank-sum test, P < 0.01) (Supplementary Fig. 2c). The abundances of macrolide-lincosamide-streptogramin (MLS) and Glycopeptide were second only to Multidrug (Supplementary Table 5), and the abundance of these antibiotics was higher in the Tibetan argali group (Wilcoxon rank-sum test, P < 0.01) (Supplementary Fig. 2c).
a Bar plot showing the relative abundance of antibiotic resistance ontology (ARO) terms in individual samples. b Violin plot displaying intergroup differences in the top 10 most abundant AROs between Tibetan argali and blue sheep. Dashed lines indicate group medians. c Redundancy analysis (RDA) illustrating associations between the top 10 most abundant AROs and dominant bacterial phyla. The gene “parY” represents the Streptomyces rishiriensis parY mutant conferring resistance to aminocoumarin. Samples labeled with “PYA” represent Tibetan argali, and “YYF” represent blue sheep. **: P < 0.01, calculated by Wilcoxon rank-sum test. n = 9 biologically independent samples per group.
Redundancy analysis (RDA) and Spearman correlation analysis of antibiotic resistance genes (Fig. 4c and Supplementary Table 6) showed that the associations of different AROs with the microbiota varied between groups. The Streptomyces rishiriensis parY mutant conferring resistance to aminocoumarin gene showed a stronger correlation with the microbiota of the blue sheep group (RDA analysis, r2 = 0.68, P < 0.01), compared to the Tibetan argali group. Spearman analysis also revealed that this gene was significantly positively correlated with Bacteroidota (Spearman analysis, R = 0.70, P < 0.01) and negatively correlated with other dominant phyla. The macB and oleC genes showed significant correlations with the Tibetan argali group microbiota (RDA analysis, P < 0.01), and were significantly positively correlated with Bacillota, Actinomycetota, and Euryarchaeota (Spearman analysis, P < 0.05).
Diet composition and the impact of diet on the gut microbiome
We analyzed the dietary composition of Tibetan argali and blue sheep using high-throughput sequencing of the chloroplast trnL (UAA) intron marker gene from fecal samples. After sequence processing, the Tibetan argali group obtained an average of 258,377 high-quality sequences per sample, while the blue sheep group obtained an average of 134,472 high-quality sequences per sample. The blue sheep group identified 66 amplicon sequence variants (ASVs), representing plants from 2 classes, 15 orders, 21 families, and 32 genera, whereas the Tibetan argali group identified 45 ASVs from 2 classes, 12 orders, 17 families, and 23 genera. At the family level, both groups shared 16 families, accounting for 72.73% of the total families, indicating similar plant types (families) consumed by both species. However, the specific proportions and abundance of the plants consumed by each species were significantly different.
We generated stacked area plots of the relative abundances of all plant families for both groups (Fig. 5a). The graph shows that the primary food sources for the blue sheep group were Rosaceae (38.90% ± 16.14%), Chenopodiaceae (18.37% ± 11.71%), and Poaceae (11.39% ± 3.94%), while the primary food sources for the Tibetan argali group were Plumbaginaceae (54.91% ± 15.99%), Chenopodiaceae (16.34% ± 7.82%), and Fabaceae (16.05% ± 11.73%). In terms of dietary diversity, the blue sheep group had significantly higher diversity than the Tibetan argali group (Fig. 5b), suggesting that the diet of Tibetan argali is more specialized. Despite living in the same environment, the two species exhibit significant differences in their dietary compositions (Supplementary Fig. 3). To assess the impact of dietary diversity on the gut microbiome diversity of Tibetan argali and blue sheep, we selected the best-fit model based on the Akaike Information Criterion (AIC) and performed linear regression analysis for both groups (Fig. 5c). The results showed that the gut microbiome diversity of the blue sheep group was significantly positively correlated with dietary diversity (LM analysis, R² = 0.56, P < 0.05), whereas no significant correlation was found in the Tibetan argali group (LM analysis, R² = −0.11, P > 0.05).
a Relative abundance of plant families detected in each sample. b Comparison of dietary diversity at the family level using Welch’s t-test. Boxplots show the median (center line), 25th and 75th percentiles (box bounds), and the full range from minimum to maximum (whiskers). c Linear regression analysis between dietary diversity and microbial diversity at the family level. d Random forest analysis showing the impact of dietary composition on microbial diversity in blue sheep. e Random forest analysis showing the impact of dietary composition on microbial diversity in Tibetan argali. Samples labeled with “PYA” represent Tibetan argali, and “YYF” represent blue sheep. *: P < 0.05, **: P < 0.01. n = 9 biologically independent samples per group.
The random forest model results showed that the model explained approximately 67% of the variation in gut microbiome diversity in the Tibetan argali group. Among the plant families, Plumbaginaceae (Permutation test, P < 0.05), Polygonaceae, and Tamaricaceae had the highest importance values (Fig. 5e), indicating their significant role in predicting microbiome diversity. In the blue sheep group, the random forest model explained 72% of the variation in gut microbiome diversity. Among the plants consumed by blue sheep, Tamaricaceae (Permutation test, P < 0.05) contributed the most to the model (Fig. 5d), followed by Primulaceae, which also had high importance values, suggesting that these families significantly influence the gut microbiome diversity of blue sheep.
Moreover, the correlation between the gut microbiome and dietary composition differed significantly between the two groups (Fig. 6a, b). In the blue sheep group, Fabaceae was significantly positively correlated with Plumbaginaceae (Spearman analysis, R > 0.7, P < 0.05), while it was significantly negatively correlated with Rosaceae (Spearman analysis, R > 0.7, P < 0.05). In the Tibetan argali group, the strongest correlation was observed between Asteraceae and Chenopodiaceae (Spearman analysis, R > 0.9, P < 0.01). The Mental test results showed that the gut microbiota of Tibetan argali was significantly correlated with Tamaricaceae (Mental test, R = 0.46, P < 0.05), whereas no such correlation was observed in blue sheep.
a Correlation network between dominant genera and dietary composition in Tibetan argali; b Correlation network between dominant genera and dietary composition in blue sheep; c Co-occurrence network of dietary composition and gut microbiome functions in Tibetan argali; d Co-occurrence network of dietary composition and gut microbiome functions in blue sheep. Edges represent significant Spearman correlations; edge thickness reflects correlation strength. *: P < 0.05, **: P < 0.01. n = 9 biologically independent samples per group.
Co-occurrence network of dietary composition and gut microbiome functions
To explore the potential link between dietary composition and gut microbiome functions, we constructed co-occurrence networks between the plant families consumed by Tibetan argali and blue sheep and their gut microbiome KEGG level 2 functions. The results indicated significant differences in the relationship between diet and functions between the two species (Table 2). Overall, the blue sheep group network contained 27 nodes and 21 edges, with negative correlations being dominant (15 edges), and a graph density of 0.06. This suggests that blue sheep consume a wider variety of plants, and their gut functions are more likely influenced by the inhibitory effects of consumed plant (Fig. 6d). In contrast, the Tibetan argali group network contained 24 nodes and 27 edges, with more positive correlations (15 edges) and a higher graph density of 0.098. This indicates a more densely connected network, suggesting that the gut functions of Tibetan argali are more influenced by the promoting effects of consumed plants (Fig. 6c).
Regarding the core nodes in the co-occurrence networks of both groups, in the Tibetan argali group, Polygonaceae and Tamaricaceae were the most central nodes, with the highest degree values, serving as key plant families closely associated with multiple gut microbiome functions. Among gut microbiome functions, Translation and Nucleotide Metabolism were prominent functional nodes with high centrality (Fig. 6c). In the blue sheep group, Polygonaceae and Tamaricaceae were also core nodes, but Polygonaceae had a higher centrality in the blue sheep network. Among microbiome functions, Membrane Transport was the key function in co-occurrence network of the blue sheep group, and it was significantly positively correlated with Tamaricaceae (Spearman analysis, R > 0.8, P < 0.05) (Fig. 6d). However, in the Tibetan argali group, Membrane Transport showed a positive correlation only with Tamaricaceae (Spearman analysis, R > 0.7, P < 0.05) (Supplementary Tables 7 and 8).
Discussion
This study investigated the dietary and gut microbiota differences between Tibetan argali and blue sheep, two sympatric species that share the same environment in the Kunlun Mountains of the Qinghai–Tibet Plateau (Fig. 7). It assessed how these differences, along with the interaction between diet and gut microbiota, contribute to ecological niche differentiation.
The map shows sampling sites of the two species across the study region. The basemap was generated using ArcGIS Pro (Esri, USA). National, provincial, and plateau boundary data were obtained from the Resource and Environment Science and Data Center (RESDC, http://www.resdc.cn). Photographs of Tibetan argali and blue sheep were taken by the authors.
Regarding the structure and composition of the gut microbiomes, Bacillota and Bacteroidota were the most dominant phyla in both species. This aligns with the findings of ref. 41 on blue sheep and is consistent with other studies on other herbivores42,43. However, significant differences in the relative abundance of these phyla, as well as in the dominant phyla, genera, and differential biomarkers, suggest that, despite living in similar environments, the gut microbiomes of Tibetan argali and blue sheep may differ due to factors such as host genetic background and diet9,44. These differences are further supported by significant variations in both α and β diversity.
A higher Bacillota/Bacteroidota ratio generally indicates a greater capacity for energy extraction from the diet45. This suggests that Tibetan argali may have higher energy demands, possibly related to their larger body size. According to Kleiber’s law, metabolic rate scales to the 3/4 power of body mass, meaning larger animals typically require more energy intake to sustain metabolic function46. Although this relationship can vary depending on species characteristics, environmental temperature, and activity levels47,48, Tibetan argali have been observed to preferentially select high-energy plants to meet their energetic requirements36,49. Furthermore, high-fiber diets have been reported to stimulate methanogenesis in the rumen50. In our study, Tibetan argali were found to consume high-fiber plants, and the relative abundance of the archaeal phylum Euryarchaeota, which is involved in methane production, was significantly higher in the argali group than in the blue sheep. Since methane is a by-product of microbial fermentation that can lead to energy loss16, this may also be linked to the higher energy requirements of Tibetan argali.
In addition, the Tibetan argali group showed significantly higher relative abundances of Lachnospiraceae and Ruminococcus (within Bacillota), both of which are known to secrete large amounts of cellulases and hemicellulases, facilitating the breakdown of plant fibers into short-chain fatty acids that serve as important energy sources for the host51. In contrast, the most significantly enriched taxa in the blue sheep group belonged to the phylum Bacteroidota, which are capable of degrading high-molecular-weight organic compounds52. Our dietary analysis revealed that Tibetan argali primarily fed on Plumbaginaceae, which are rich in cellulose, whereas blue sheep consumed more Rosaceae plants, which are high in natural sugars such as glucose and fructose. These distinct dietary preferences corresponded with their respective dominant gut microbial communities, suggesting that gut microbiota not only adapt to host diets but may also facilitate dietary niche differentiation between the two species39.
Studies have shown that higher gut microbiome diversity is associated with greater gut plasticity and a more stable microbial community53,54. In our study, the gut microbiome diversity of Tibetan argali was significantly higher than that of blue sheep, reflecting a more complex and stable microbial community in Tibetan argali. However, Tibetan argali had a more specialized diet compared to blue sheep, with significantly lower dietary diversity. We hypothesize that to maintain normal physiological activities and metabolism, Tibetan argali relies on a more diverse gut microbiome. Although blue sheep and Tibetan argali inhabit the same geographical region, they differ in habitat use and foraging strategies. Blue sheep typically rely on cliffs and steep slopes as refuges from predators34,35. However, these rugged areas often lack abundant edible vegetation, so blue sheep are known to travel outside of these escape terrains to forage35. Aryal et al found that the plant diversity index in blue sheep habitats was relatively high (ranging from 0.789 to 0.924), suggesting that blue sheep tend to forage in areas with greater vegetation biomass and diversity34. In contrast, Tibetan argali primarily depend on speed to escape predators and generally avoid steep terrain. They prefer to occupy open, gently sloping areas such as alpine meadows and scree slopes35. Singh et al found that in the trans-Himalayan region of India, Tibetan argali favored habitats with moderate vegetation biomass (The mean value of the Normalized Difference Vegetation Index was approximately 0.2) rather than high-biomass areas36. Therefore, we speculate that constraints in available foraging environments and selective feeding behavior may drive Tibetan argali to develop a more diverse and adaptable gut microbiome to compensate for limited dietary options.
In terms of functional characteristics, Metabolism, particularly Carbohydrate Metabolism and Amino Acid Metabolism, were highly abundant in both species, indicating that metabolism plays a central role in their gut microbiomes. These findings are consistent with results from other herbivores17,22. For herbivorous animals, the metabolic capacity of the microbiome is essential for the degradation of plant fibers and nutrient absorption55. We found that the functional abundance of Glycan biosynthesis and metabolism was significantly higher in blue sheep than in Tibetan argali, indicating a stronger capacity for carbohydrate metabolism in blue sheep. Blue sheep have a more diverse diet, and their primary food plants—Rosaceae (38.90% ± 16.14%) and Poaceae (11.39% ± 3.94%)—are rich in carbohydrates and proteins56,57. Therefore, the elevated abundance of Glycan biosynthesis and metabolism in blue sheep may represent an adaptive response to their dietary characteristics, facilitating the efficient breakdown of complex carbohydrates from various plant sources. In contrast, Tibetan argali exhibited significantly higher functional abundance in Xenobiotics biodegradation and metabolism, suggesting a greater microbial capacity for degrading exogenous chemical compounds in their gut microbiome58. This may be linked to their primary consumption of Plumbaginaceae plants, which are known to contain abundant phytochemicals59,60.
Analysis of differences in the ABC Transporters metabolic pathway showed that the gut microbiota of Tibetan argali may have a stronger adaptation to processing basic nutrients and maintaining energy metabolism (K16785, K16786), whereas the microbiota of blue sheep may require more complex metabolic support (K01998, K01999), particularly to cope with environmental pressures, such as antibiotics (K18887, K18889)61. These differences in metabolic functions reflect the distinct ecological adaptation strategies and dietary choices of the two species, potentially supporting their ecological niche differentiation through functional variations in their gut microbiomes.
Previous studies have shown that anthropogenic antibiotic pollution is a major driver of antibiotic resistance acquisition in wildlife62,63, and the abundance of antibiotic resistance genes (ARGs) in wild animal microbiomes may reflect the extent of such contamination64. Due to human activities such as livestock grazing, antibiotic treatments, and crop cultivation, grasslands, wetlands, and rivers across the Qinghai–Tibet Plateau have been contaminated with ARGs65,66. Wang et al.67 reported that many livestock are freely grazed across the Qinghai–Tibet Plateau in China, and that with the intensification of animal husbandry, the antibiotic resistance of livestock fecal microbiota is expected to increase significantly. In our study, we found that blue sheep harbored significantly higher ARG diversity than Tibetan argali, and both the number and abundance of genes classified under the “Multidrug” category—indicating resistance to multiple antibiotics—were greater in the blue sheep group. This suggests that blue sheep may be exposed to more complex antibiotic contamination. Additionally, we observed that genes associated with MLS and Glycopeptide antibiotic resistance were more abundant in Tibetan argali, indicating a greater tendency of their gut microbiota to tolerate or expel these specific antibiotics. These patterns may be partially explained by differences in habitat use and foraging strategies between the two species, while also suggesting that their respective habitats may already be affected by varying degrees of antibiotic pollution. Correlation analysis revealed that the structure of the gut microbiota influences the host’s antibiotic resistance68 and may play a role in degrading antibiotic compounds69.
Wildlife represents a crucial component of the “One Health” framework and may serve as vectors for the transmission of ARGs across different ecosystems64,70. For species sharing the same habitat, such ARG transmission or horizontal gene transfer is accelerated in their direct or indirect life activities71,72. The spread of ARGs not only affects wildlife populations but may also impact livestock and human health via ecological networks64. Therefore, investigating ARGs in the gut microbiota of wild animals is of great importance for wildlife conservation, environmental health monitoring, and public health. This is especially relevant for endangered species like Tibetan argali and blue sheep, where antibiotic pollution from grazing livestock may pose a long-term ecological risk. Future research should focus on monitoring antibiotic contamination in their habitats and evaluating its long-term effects on wildlife health and microbial community stability.
Many studies have demonstrated the significant impact of diet on the host’s gut microbiota13,19,73. However, most studies rely on field observations or microscopy to identify the dietary components of herbivores, methods that are often limited and lack accuracy in wild herbivore research19. Therefore, we employed high-throughput sequencing of the plant trnL (UAA) fragment74, which effectively identified the dietary composition of Tibetan argali and blue sheep. We found that blue sheep exhibited significantly higher dietary diversity than Tibetan argali, with distinct primary food sources. Furthermore, the overall dietary composition showed no significant correlation with the gut microbiota, suggesting that blue sheep adopt a broad-spectrum dietary strategy. In contrast, Tibetan argali had a more specialized diet, with specific plant families showing significant correlations with the gut microbiota. This indicates that Tibetan argali follows a specialized strategy in a resource-limited environment, suggesting that the two species may reduce competition and promote dietary niche differentiation through distinct foraging strategies.
Dietary composition plays a crucial role in shaping the gut microbiome, influencing its richness and diversity75. Random forest analysis showed that the dietary composition of blue sheep explained approximately 72% of the variation in gut microbial Shannon diversity, while that of Tibetan argali explained 67%, indicating that diet plays an important role in shaping gut microbial structure. In the blue sheep group, the Tamaricaceae family—despite not being among the most abundant dietary components—contributed significantly to the maintenance of gut microbial diversity. We speculate that Tamaricaceae plants may contain specific chemical compounds or nutrients60 that influence microbial diversity. In contrast, among the dietary components of Tibetan argali, the Plumbaginaceae family not only had the highest relative abundance but also exhibited the greatest impact on the model (P < 0.05), suggesting that this plant family may play a central role in maintaining gut ecological homeostasis in Tibetan argali. Moreover, we found that species with more diverse diets do not necessarily exhibit more diverse gut microbiomes, consistent with findings from refs. 74,76. However, the gut microbiome diversity of blue sheep was significantly positively correlated with dietary diversity, while no such correlation was observed in Tibetan argali. We speculate that the covariation between diet and microbiome composition differs between species, and therefore, dietary diversity cannot reliably predict gut microbiome diversity76. Moreover, in the co-occurrence network of blue sheep, negative correlations dominated, suggesting that the gut microbiome functions of blue sheep may be inhibited by secondary metabolites from a broad range of food sources77. The Membrane Transport function was the only key function in the blue sheep network, with ABC transporters being the primary functional group at the KO level. This further supports our previous hypothesis that blue sheep adapt to the complex chemical environment created by diverse food resources and potentially antibiotic-exposed environments through these metabolic pathways.
This study revealed distinct differences in the diet and gut microbiota composition between Tibetan argali and blue sheep, and demonstrated how these differences may drive ecological adaptation. These findings offer new insights into the mechanisms of ecological niche differentiation among herbivores and hold significant implications for the conservation of endangered wild ungulates. The gut microbiota is a key modulator of host ecological adaptation and can serve as an indicator of host health and environmental adaptability78. We therefore propose that the gut microbiome has strong potential as a biological indicator for future wildlife health monitoring and habitat quality assessment79,80. By providing baseline data on the microbiome composition and dietary structure of Tibetan argali and blue sheep, our study contributes to a better understanding of the relationship between microbial diversity, host health, and environmental adaptation. In future conservation management practices, microbiome monitoring could be integrated into reintroduction programs81, population recovery efforts79, and habitat quality assessments82, thereby supporting the development of evidence-based conservation strategies.
However, this study has certain limitations. First, the samples were collected from the Kunlun Mountains region, which can represent the microbial characteristics of Tibetan argali and blue sheep in this area. Nevertheless, given the variation in environmental conditions, vegetation types, and dietary resources across the Qinghai–Tibet Plateau, our findings may not be fully generalizable to the entire plateau population. Second, due to fieldwork constraints and conservation regulations, our sample size was relatively small. However, several recent studies on wild animal gut microbiomes have demonstrated that small sample sizes (e.g., 5–10 individuals per group) can still effectively capture microbial community structure24,38. Future studies may benefit from expanding the sample size and integrating host genetic analyses to more comprehensively explore the adaptive mechanisms of herbivore microbiomes. Despite the geographic and sample size limitations, our study provides valuable data on the gut microbiome and dietary composition of Tibetan argali and blue sheep in the Kunlun Mountains, offering important insights into the ecological adaptation and coexistence mechanisms of plateau wildlife.
Methods
Fecal sample collection
In August 2022, we collected a total of 18 fresh fecal samples from wild ungulates in the Kunlun Mountains of Golmud, Qinghai Province, China (95°16′ E, 35°44′ N), including 9 samples from Tibetan argali and 9 from blue sheep. The study area lies on the Qinghai–Tibet Plateau and is characterized as a typical cold alpine desert ecosystem83. Prior to sampling, we used binoculars to observe the animals, and collected feces immediately after defecation, once individuals had left the site. The sampling areas for Tibetan argali and blue sheep were approximately 2 km apart (Fig. 7), and all samples were collected from independent wild groups. To avoid repeated sampling from the same individual, GPS coordinates were recorded for each sampling point, and the number of collected samples did not exceed the number of observed individuals in each group (more than 10 individuals per species were observed at each site). We prioritized collecting feces with large volume and adult-like morphology while excluding smaller feces suspected to originate from juveniles. Clumped or centrally located fecal pellets were preferred over scattered ones to reduce soil contamination, and samples were taken from the top or center of fecal piles whenever possible. A new pair of disposable polyethylene (PE) gloves was used for each sample to avoid cross-contamination. All samples were collected during the morning to early afternoon to minimize the influence of high temperatures or strong winds on sample freshness. Samples were stored at −80 °C until further sequencing analysis. All animal sampling procedures in this study were approved by the Animal Ethics Committee of the Northwest Institute of Plateau Biology, Chinese Academy of Sciences, and were conducted in accordance with relevant wildlife protection regulations.
Metagenomic sequencing and annotation
Total genomic DNA was extracted from each sample using the FastPure Feces DNA Isolation Kit (Vazyme Biotech Co., Ltd., Nanjing, China) following the manufacturer’s instructions. DNA concentration and purity were assessed using NanoDrop 2000, and DNA quality was verified as 1% agarose gel. DNA was then fragmented to an average size of approximately 350 bp using a Covaris M220 (Gene Company Limited, China) for paired-end library construction. Paired-end libraries were prepared using the NEXTFLEX Rapid DNA-Seq (Bioo Scientific, Austin, TX, USA). Adapters containing the full set of sequencing primer hybridization sites were ligated to the blunt ends of the fragments. Paired-end sequencing was conducted on an Illumina NovaSeq platform (Illumina Inc., San Diego, CA, USA) at Majorbio Bio-Pharm Technology Co., Ltd. (Shanghai, China) using the NovaSeq 6000 S4 Reagent Kit v1.5 (300 cycles), according to the manufacturer’s instructions (www.illumina.com).
Raw sequencing data were quality-controlled using fastp84 (https://github.com/OpenGene/fastp, version 0.20.0) to trim adapter sequences from both the 3′ and 5′ ends of reads and to remove low-quality reads (length < 50 bp, quality value < 20, or containing N bases). Reads were aligned to the host genome using BWA85 (http://bio-bwa.sourceforge.net, version 0.7.9a), and any aligned reads and their mate pairs were discarded. Metagenomic data were assembled using MEGAHIT86 (https://github.com/voutcn/megahit, version 1.1.2), which utilizes succinct de Bruijn graphs. Contigs with a length ≥ 300 bp were selected for further gene prediction and annotation.
ORFs from each assembled contig were predicted using Prodigal87 (https://github.com/hyattpd/Prodiga, version 2.6.3). ORFs ≥ 100 bp were retrieved and translated into amino acid sequences. A non-redundant gene catalog was created using CD-HIT88,89 (http://www.bioinformatics.org/cd-hit/, version 4.6.1) with 90% sequence identity and 90% coverage. High-quality reads were then aligned to the non-redundant gene catalog to calculate gene abundance with 95% identity using SOAPaligner90 (http://soap.genomics.org.cn/, version 2.21).
Representative sequences from the non-redundant gene catalog were aligned to the NCBI non-redundant protein (NR) database with an e-value cutoff of 1e-5 using Diamond91 (http://www.diamondsearch.org/index.php, version 0.8.35) for taxonomic annotation. KEGG annotation92,93 was performed using the same Diamond tool against the KEGG database (http://www.genome.jp/keeg/) with an e-value cutoff of 1e-5. Finally, antibiotic resistance annotations were performed using Diamond (http://www.diamondsearch.org/index.php, version 0.8.35) against the Comprehensive Antibiotic Resistance Database94 (https://card.mcmaster.ca/home) with the same e-value cutoff (1e-5). Through these annotations, the species, functional, and resistance gene abundances at various levels were obtained, and statistical analyses were performed based on the abundance data.
Dietary sequencing and annotation
DNA was first extracted from fecal samples using the cetyltrimethylammonium bromide method. The concentration, purity, and contamination of the extracted DNA were assessed using a NanoDrop 2000 (Thermo Fisher Scientific, USA). Qualified samples were assessed for DNA integrity using 1% agarose gel electrophoresis. The trnL (UAA) g-h gene fragment of plants95,96 was then amplified via PCR using the primers (g: A49425) 5′-GGGCAATCCTGAGCCAA-3′ and (h: B49466) 5′-CCATTGAGTCTCTGCACCTATC-3′. The PCR conditions were as follows: 98 °C for 2 min of pre-denaturation, followed by 25 cycles (denaturation at 98 °C for 15 s, annealing at 55 °C for 30 s, and extension at 72 °C for 30 s), and a final extension at 72 °C for 5 min. The amplification products were purified using a magnetic bead method and quantified using the Quant-iT PicoGreen dsDNA Assay (Invitrogen, USA). Subsequently, sequencing libraries were constructed using the TruSeq Nano DNA LT Library Prep Kit (Illumina Bioo Scientific, USA) and sequenced using Illumina NovaSeq 6000 platform for PE150 sequencing. DNA extraction, library construction, and sequencing were performed by Shanghai Personal Biotechnology Co., Ltd.
Raw sequencing data were processed using the QIIME2 platform97 (https://docs.qiime2.org). Primer sequences were first removed using the cutadapt plugin98, allowing for one mismatch; sequences that failed to match the primers were discarded. Subsequent filtering and denoising were conducted in R using the DADA2 pipeline99, with the following parameters: truncQ = 2, maxEE = c(1,1), minLen = 10, maxN = 0, and pseudo-pooling mode was applied to enhance the detection of low-abundance taxa. Chimeric sequences were removed using the consensus method implemented in DADA2. Quality control of the raw ASV sequences and abundance table was performed using QIIME2. ASVs with a relative abundance lower than 0.000001 and present in fewer than two samples were filtered out using the feature-table filter-features-conditionally plugin, to reduce noise from extremely low-abundance taxa. To standardize sequencing depth across samples, we applied rarefaction using the feature-table rarefy function in QIIME2, with the rarefaction threshold set to the minimum sequencing depth observed in both groups. Rarefaction curves were generated to evaluate sequencing saturation, and all curves plateaued, indicating that the sequencing depth was sufficient to capture most of the plant taxonomic diversity (Supplementary Fig. 1). Finally, ASVs were annotated using the self-built database of the Qinghai–Tibet Plateau Animal Diversity Conservation and Management Innovation Team at the Northwest Institute of Plateau Biology, Chinese Academy of Sciences to obtain taxonomic information for each ASV. Taxonomic assignment was performed with a confidence threshold of 0.8, and downstream dietary analyses were conducted at the family level.
Statistical data analysis
All statistical analyses were performed on biologically independent samples. A total of 18 fecal samples were collected from wild individuals in the field, including 9 Tibetan argali and 9 blue sheep. Each sample represented a single biological replicate. Sample sizes (n = 9 per group) were determined based on field accessibility and prior published studies of wild ungulates. All statistical analyses were conducted using R (Version 4.3.2) and GraphPad Prism9.5. First, we performed Wilcoxon rank-sum test and Welch’s t-test to analyze intergroup differences in the gut microbiota at the phylum, genus, and functional gene levels, with significance set at P < 0.05. Next, we used the mothur, boot, and stats packages to calculate the diversity of microbiota, KEGG functions, and antibiotic resistance genes, and performed Welch’s t-test on the Shannon–Wiener index and Pielou evenness index. For β-diversity analysis, we calculated the Bray-Curtis distance between samples using the vegan package, followed by ANOSIM analysis (999 permutations) to assess intergroup differences. Principal Coordinates Analysis and Non-metric Multidimensional Scaling (NMDS) analyses were performed for visualizing community structure differences100.
We used LEfSe software (http://galaxy.biobakery.org/) for differential analysis of KEGG pathways and gut microbiota101, and Linear Discriminant Analysis (LDA) to estimate the impact of each differential species or function on the intergroup differences. Additionally, we calculated Spearman correlation coefficients using the Hmisc package to analyze correlations between dominant gut microbiota genera and functional genes, dominant genera and diet composition, and dominant phyla and antibiotic resistance genes. P values were adjusted using the false discovery rate method. To avoid multicollinearity, we used Variance Inflation Factor (VIF) analysis to exclude antibiotic resistance genes with VIF values greater than 10, followed by RDA72 to explore the relationships between resistance genes and gut microbiota.
Dietary diversity was analyzed using the vegan package, and Wilcoxon rank-sum test was used to assess intergroup differences. AIC was used to select the best-fit model, and linear regression models (LM)102 were employed to evaluate the impact of dietary diversity on the gut microbiota of Tibetan argali and blue sheep. Moreover, rfPermute package was used to calculate the importance of food variables based on the random forest model, and permutation tests were performed to obtain the significance P values of each variable. We used Mental test to analyze the relationship between dominant genera of gut microbiota and the main plant families in the diet (relative abundance >1%). Finally, based on Spearman correlation coefficients, we selected variables with P < 0.05 and |r| > 0.7, and constructed a co-occurrence network between dietary composition and gut microbiota KEGG functions using Gephi (V0.9.2) (https://gephi.org) for visualization.
Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
Data availability
The Illumina sequencing data used in this study have been submitted to the National Genomics Data Center (NGDC) of China103 (https://ngdc.cncb.ac.cn). Bioproject: PRJCA034754 and Bioproject: PRJCA027325. Source data supporting the findings of this study, including numerical data used to generate all figures, are provided in the Supplementary Data 1 and 2 files.
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Acknowledgements
This work was supported by the Natural Science Foundation of Qinghai Province (2023-ZJ-901T).
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M.Z., F.J., Z.Y.C. and T.Z.Z. conceived and designed the study. M.Z., C.B.L., B.L. and H.F.Gu. collected the samples. M.Z., P.F.S., Z.Y.C. and H.M.Gao. performed data analysis. M.Z. wrote the original draft. M.Z., F.J. and T.Z.Z. revised and finalized the manuscript. T.Z.Z. secured funding for the project. All authors reviewed and approved the final version of the manuscript.
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Zhang, M., Liang, C., Li, B. et al. Gut microbiome and diet contribute to ecological niche differentiation between argali (Ovis ammon hodgsoni) and blue sheep (Pseudois nayaur) on the Qinghai-Tibet Plateau. Commun Biol 8, 930 (2025). https://doi.org/10.1038/s42003-025-08185-7
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DOI: https://doi.org/10.1038/s42003-025-08185-7
