Investigation into the Relationship between Gut Microbiota of Cranes and Their Feeding Environment in the Yellow River Delta

Investigation into the Relationship between Gut Microbiota of Cranes and Their Feeding Environment in the Yellow River Delta | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Investigation into the Relationship between Gut Microbiota of Cranes and Their Feeding Environment in the Yellow River Delta Shuai Shang, Yunpeng Liu, Lei Li, Xiaodong Gao, Bo Zhou, Jingyi Yu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7707554/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The gut microbiota plays essential roles in host health and environmental adaptation, particularly for migratory birds within wetland ecosystems. This study examined the gut microbial communities of common cranes ( Grus grus ) and White Cranes( Grus leucogeranus ) in the Yellow River Delta using 16S rRNA sequencing of 24 fecal and foraging samples. Alpha diversity (ACE, Chao1, Shannon, Simpson) revealed significant inter-group differences, indicating environmental filtering effects. Beta diversity (PCoA) confirmed strong separation between foraging and fecal samples (PC1 = 25%). Dominant phyla included Proteobacteria (24.6–37.4%), Firmicutes (4.8–29.0%), and Actinobacteriota (12.4–23.3%), with genus-level biomarkers identified via LEfSe: Ligilactobacillus (12.1% in DYH), Cryobacterium (9.2% in DYB), and Rhodococcus (5.4% in DYH). SourceTracker indicated predominantly unknown microbial origins (> 70%), suggesting uncharacterized environmental reservoirs. Functional prediction highlighted group-specific metabolic adaptations, including elevated amino acid transport in DYH (9.8% vs. 7.1% in DYBT; P < 0.05) and conserved defense mechanisms (8.5% across groups). Our findings demonstrate that crane gut microbiota is shaped by synergistic host foraging behavior and wetland environmental factors. This study provides novel insights into host-microbe-environment interactions in migratory birds and suggests potential microbial indicators for monitoring wetland ecosystem health. Gut Microbiota Foraging ecology Yellow River Delta 16S rRNA sequencing Microbial adaptation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction As one of the most complex micro-ecosystems in animals, the intestinal microbiota is essential in maintaining host health, regulating metabolism, and enhancing environmental adaptability[1, 2]. In recent years, it has become a key focus in interdisciplinary research. Evidence suggests that the intestinal microbiota profoundly influences animals' physiological functions and ecological adaptation strategies through nutrient metabolism, immune regulation, and interactions between the host and its environment. The gut microbiota is frequently characterized as a crucial "microbial organ" within an organism, and the symbiotic entity comprising the host animal and its microbiota is termed a "holobiont"[3]. Birds represent a highly successful group of organisms, exhibiting remarkable species and genetic diversity[4]. In comparison to mammals, the gut microbiota of birds is characterized by relatively lower stability and greater plasticity[5]. Wild birds exhibit a wide range of dietary preferences, flight behaviors, and developmental strategies[6], which contributes to the complexity of their intestinal microbiota. Changes in the intestinal microbiota of wild birds have significant effects on the host's physiological characteristics, nutritional status, and stress response[7]. The dynamic regulation of the intestinal microbiota is frequently considered a key mechanism by which birds enhance their ecological adaptability[8]. The common crane ( Grus grus ) is a large migratory bird species with a wide distribution across wetland ecosystems in Eurasia. As a flagship species for wetland ecosystems, its survival status directly indicates habitat quality. In China, the common crane exhibits extensive distribution. During its migration period, it is commonly observed in most provincial-level administrative regions, particularly in the Yellow River Delta and the Yangtze River Basin[9]. Its breeding grounds are primarily in northern Xinjiang, Inner Mongolia, and Heilongjiang. At the same time, wintering areas are predominantly distributed across the Yellow River Basin, the middle and lower reaches of the Yangtze River, and the Yunnan-Guizhou Plateau. Driven by global climate change, wintering ranges have shown a significant trend of expanding northward. In recent years, substantial wintering populations have been recorded in regions extending beyond the Yellow River Delta, including southern parts of Northeast China. As China's largest newly formed wetland, the Yellow River Delta possesses a unique saline-alkaline environment and water-sediment dynamics that significantly influence the food chain structure of the common crane's habitat. Studies indicate that the salinity gradient within the wetland markedly affects the soil microbial community, which propagates through the food chain to impact avian gut microbiota. For instance, a high-salinity environment suppresses the activity of nitrifying bacteria, potentially reducing nitrogen availability in wetland vegetation and indirectly affecting food quality for common cranes. Moreover, recent ecological water replenishment initiatives in the Yellow River Delta, such as water-sediment regulation, have substantially enhanced wetland vegetation coverage. The intestinal microbiota plays a pivotal role in digestion and absorption, immune regulation, and environmental adaptation in the common crane. For example, it assists the host in adapting to seasonal dietary shifts and migratory stress by degrading plant fibers, synthesizing essential nutrients such as short-chain fatty acids, and resisting pathogenic microorganisms. Recent studies have demonstrated that the common crane's intestinal microbiota diversity is closely associated with its dietary composition and habitat environment. Notably, there are marked differences in microbiota composition between wild and semi-captive populations, highlighting the significant influence of environmental factors on microbiota regulation. The resurgence of salt-tolerant plants like Suaeda salsa has provided abundant food resources for common cranes and may modulate their gut flora by enhancing dietary diversity. The stability of the intestinal microbiota in common cranes is susceptible to various environmental challenges. In particular, saline-alkali stress exerts a substantial impact, as soil salinity in the Yellow River Delta affects the microbial community ingested by the common crane through the food chain. Materials and Methods Study Area and Sample Collection The samples for this study were collected from the 1200 Forest Farm area within the Yellow River Delta National Nature Reserve. This region is characterized by diverse wetland types, such as estuarine rivers, muddy tidal flats, and rotational farmlands, offering extensive habitats and foraging grounds for cranes. During the winter season, the primary crane species in this area are the common crane and the white crane. The populations of these two species are relatively dispersed. For the sampling process, we divided the team into two: one focused on monitoring the activities of the White Crane, while the other monitored the Grey Crane. The primary emphasis was placed on observing their foraging behavior and defecation patterns. After observing defecation events, samples were collected from both foraging and defecation sites. Specifically, foraging samples of the common crane (DYH1-6), fecal samples of the common crane (DYHT1-6), fecal samples of the white crane (DYB1-6), and foraging samples of the white crane(DYBT1-6). After collection, the samples were stored in ice boxes and transported to the laboratory, where they were preserved at -20°C for subsequent DNA extraction. Sample treatment In this study, 0.3 g of each sample was precisely weighed. Total DNA extraction from fecal samples was conducted using the TIANamp Stool DNA Kit (TianGen Biotech (Beijing) Co., Ltd.). The concentration and purity of the extracted DNA were evaluated using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, USA). Subsequently, PCR amplification was performed with primers 515F (5′-GTGCCAGCMGCCGCGG-3′) and 907R (5′-CCGTCAATTCMTTTRAGTTT-3′) to construct the sequencing library[10]. The fragment size and concentration were quantified after library preparation, and high-throughput sequencing was carried out on an Illumina NovaSeq 6000 platform (Biomarker Technologies Co., Ltd., Beijing). Raw image data files generated during high-throughput sequencing were processed via base calling to produce raw sequencing reads stored in FASTQ format. Data analysis This file format encapsulates both sequence information and corresponding quality scores. Sequence denoising was achieved using the DADA2 algorithm implemented in QIIME2 2020.6[11], enabling the identification of amplicon sequence variants (ASVs). Specifically, the DADA2 method facilitated the removal of noise, splicing of paired-end sequences, and eliminating chimeric sequences, thereby generating a final dataset comprising high-quality, non-chimeric reads[12]. The QIIME software generated species abundance tables at various classification levels. At the same time, the R programming language was used to visualize the community structure diagrams of each sample across different taxonomic levels. Alpha diversity reflects the species richness and evenness within a single sample. Several metrics, including Chao1, Shannon, Simpson, and Coverage, are commonly used to quantify alpha diversity. The Chao1 and Ace indices specifically estimate species richness, which refers to the sample's total number of distinct species. In contrast, the Shannon and Simpson indices measure species diversity, considering both species richness and the evenness of their distribution within the community. Under conditions of equal species richness, greater evenness among species leads to higher overall diversity. Higher values of the Shannon and Simpson indices indicate increased species diversity within the sample[13]. Beta diversity analysis was performed using the QIIME software to assess and compare species diversity similarity across different samples. Principal Component Analysis (PCA) was used to analyze and simplify complex datasets by decomposing variance and representing the differences among multiple sets of data on a two-dimensional coordinate graph[14]. The PCA analysis graphs were generated using the R language tool. Results Sequence statistics and OTU cluster analysis 1,808,455 paired-end reads were obtained from the sequencing of 24 samples. Following quality control and assembly, 1,611,112 clean reads were generated, with each sample producing no fewer than 35,712 clean reads and an average of 67,130 clean reads per sample. Specifically, the DYB group yielded 336,983 clean reads, the DYBT group yielded 425,181 clean reads, the DYH group yielded 422,278 clean reads, and the DYHT group yielded 426,730 clean reads. In this study, a total of 58,205 operational taxonomic units (OTUs) were identified across four groups. The data quality is evaluated by systematically analyzing the number of sample sequences across each stage of the statistical process. This evaluation primarily entails conducting a detailed statistical analysis of various parameters, such as the number of sequences and sequence length. Specifically, 11,826, 13,067, 19,888, and 19,369 OTUs were detected in the DYB, DYH, DYHT, and DYBT groups. Furthermore, 895 common OTUs were shared between the DYB and DYH groups; 2,887 common OTUs were identified between the DYHT and DYBT groups; 1,092 common OTUs were observed between the DYH and DYHT groups; 832 common OTUs were found between the DYB and DYBT groups; and 218 common OTUs were present across all four groups. Figure 1 Sequence statistics and OTU cluster analysis α-diversity analysis of the microbiota among four groups The image compares alpha diversity indices comprehensively across four distinct groups (DYHT, DYH, DYB, and DYBT) using four box-and-whisker plots. Each plot corresponds to a specific diversity index: ACE (a), Chao1 (b), Shannon (c), and Simpson (d), with Student’s t-test employed to assess statistical significance. The ACE index plot (a) reveals significant differences between groups, with p-values such as 0.0045 (DYHT vs. DYH) and 0.00061 (DYB vs. DYBT), indicating notable variations in species richness. Similarly, the Chao1 index plot (b) demonstrates comparable p-values (e.g., 0.0046), reinforcing the observed disparities in community richness. The Shannon index plot (c), reflecting species richness and evenness, exhibits p-values like 0.0034, suggesting significant differences in diversity. The Simpson index plot (d), focusing on dominance, shows p-values such as 0.012, highlighting variations in community dominance structures. The y-axes are scaled according to each index: the Shannon index ranges approximately from 7 to 12, while the Simpson index spans from 0.96 to 1.00, indicating high evenness across groups. The ACE and Chao1 plots emphasize richness differences, whereas Shannon and Simpson plots integrate evenness, providing a holistic view of alpha diversity. The consistent use of the Student’s t-test underscores rigorous statistical validation. The visual clarity of the 2×2 grid facilitates cross-index comparisons, revealing that DYHT and DYH often exhibit more pronounced differences (e.g., P < 0.005 ) compared to other group pairs. This structured presentation effectively communicates the heterogeneity in microbial or ecological communities across the four experimental conditions, making it a robust visual aid for diversity analysis. Figure 2 α-diversity analysis of the microbiota among four groups. Note : The x-axes label the four groups, distinguished by colors (blue: DYHT, orange: DYH, green: DYB, red: DYBT). Each box represents the interquartile range (IQR), with the median marked inside and whiskers extending to non-outlier data extremes. The layout is meticulously organized, with clear titles, annotations, and p-values above relevant comparisons. β-diversity analysis of the microbiota among four groups The figure presents a comprehensive analysis of microbial community structure across multiple sample groups (DYB, DYH, DYHT and DYBT) through two complementary analytical approaches. In sub-figure.3a, the left panel displays a hierarchical clustering dendrogram based on community similarity, revealing distinct sample groupings (DYB, DYH, DYHT and DYBT). The right panel illustrates the relative abundance of microbial taxa through stacked bar charts, where dominant genera include Sphingomonas , Rhodococcus , Pseudomonas , and other taxa such as Arthrobacter , Cryobacterium , and Ligilactobacillus . The plot coordinates indicate specific sample positions, demonstrating that DYH samples cluster in the upper-left quadrant. In contrast, DYHT samples occupy the lower-right region, suggesting distinct community structures between these groups. The combination of these approaches provides robust evidence for group-specific microbial signatures, with the PCoA particularly highlighting the strong separation between DYH and DYHT communities. This integrated visualization effectively communicates both taxonomic and ecological dimensions of microbial community variation across the experimental groups. Figure 3 β-diversity analysis of the microbiota among four groups. Note Sub-figure.3a combines hierarchical clustering with taxonomic composition analysis. Sub-figure.3b presents a PCoA plot based on Bray-Curtis dissimilarity, with principal coordinate 1 (PC1) explaining 25.00% of the variation and PC2 representing additional variance. Samples are segregated by group with confidence ellipses emphasizing group-specific clustering patterns. Analysis of microbial composition characteristics The figure presents a comparative analysis of bacterial community composition across four experimental groups (DYHT, DYH, DYB, DYBT) through two vertically stacked bar charts (a and b), each illustrating the relative abundance of microbial taxa at different taxonomic resolutions. Our results showed that the relative abundance (as proportions) of various bacterial phyla across four samples: DYHT, DYH, DYB, and DYBT. Proteobacteria is the dominant phylum in DYHT (30.44%), DYH (37.42%), and DYBT (35.06%), but in DYB, Firmicutes has the highest abundance (28.98%), while Proteobacteria drops to 24.61%. Firmicutes shows significant variability, being relatively low in DYHT (5.68%) and DYBT (4.75%) but substantially higher in DYH (25.00%) and DYB (28.98%). Actinobacteriota maintains moderate levels across all samples, peaking in DYH (23.13%) and DYB (23.31%), but decreases in DYBT (12.44%). Bacteroidota, Chloroflexi, Acidobacteriota, and other minor phyla generally exhibit lower abundances with minimal fluctuations; for instance, Bacteroidota ranges from 3.91% in DYH to 12.05% in DYBT. Overall, the data reveals sample-specific shifts, such as the prominence of Firmicutes in DYB contrasting with Proteobacteria dominance elsewhere, potentially indicating environmental or experimental influences on microbial community structure. We analyzed the relative abundance of bacterial genera (or unclassified groups) across the same four samples (DYHT, DYH, DYB, DYBT). The most striking observation is the highly variable dominance of specific genera in different samples. In the DYH group, Ligilactobacillus was exceptionally abundant (12.10%), far exceeding its presence in other samples (0.21–0.62%). Conversely, Cryobacterium dominates in the DYB group (9.18%), while its abundance is much lower elsewhere (0.18–0.29%). DYH also showed significant enrichment of Ochrobactrum (6.58%) and Rhodococcus (5.37%), genera present at very low levels (< 0.06% and < 0.26% respectively) in the other samples. The Paucibacter was notably high in DYB (5.55%) compared to others (< 1.68%). While Pseudomonas is moderately abundant in the DYH (4.03%) and DYBT group (1.45%), and Arthrobacter peaks in the DYB group (4.07%). Overall, the data reveals dramatic sample-specific enrichment of particular genera (Ligilactobacillus in DYH, Cryobacterium and Paucibacter in DYB, Ochrobactrum and Rhodococcus in DYH), suggesting distinct environmental conditions or selective pressures shaping the microbial communities in each sample. Figure 4 Analysis of microbial composition characteristics among four groups SourceTracker analysis The figure presents a comparative source-tracking analysis between DYB vs. DYBT (panel a) and DYH vs. DYHT (panel b) microbial communities. The stacked bar charts reveal that the "Unknown" source (blue) dominates across all samples (DYB_DYB01-06, DYH_DYH01-06), consistently exhibiting higher relative abundance (bar height) than the target sources DYBT (red, panel a) or DYHT (red, panel b). This pattern suggests a limited contribution from the hypothesized sources (DYBT/DYHT) to the respective microbial communities. The systematic arrangement of sample pairs facilitates direct comparison, demonstrating that while trace amounts of DYBT/DYHT signatures are detectable (minor red segments), the predominant microbial origins remain uncharacterized ("Unknown"). These results indicate insufficient reference database coverage or substantial compositional divergence between the target sources and analyzed communities. Figure 5 SourceTracker analysis among four groups LEfSe analysis LEfSe is a robust bioinformatics tool developed to identify biomarkers demonstrating statistically significant differences across multiple biological groups. This approach combines non-parametric statistical tests, specifically the Kruskal-Wallis rank-sum test and pairwise Wilcoxon rank-sum tests, with linear discriminant analysis (LDA) to estimate the effect size of differentially abundant features. In analyzing microbial communities across four distinct groups, LEfSe facilitates the detection of both taxonomic clades and individual species that consistently distinguish one group from others. LEfSe analysis identified distinct microbial signatures across the four groups in this study. Specific phyla (e.g., Actinobacteria, Firmicutes) and genera (e.g., Lactobacillus , Bifidobacterium ) were found to dominate specific clusters, indicating their potential roles as biomarkers for group stratification. The Chloroflexi, Gemmatimonadota, and Myxococcota served as key supporting phyla for the DYHT group; the Proteobacteria and Actinobacteriota supported the DYH group; the Bacteroidota and Acidobacteriota supported the DYBT group; and the Firmicutes supported the DYB group. At the genus level, Ligilactobacillus , Ochrobactrum , Rhodococcus , Catellicoccus , Pseudomonas , Microbacterium , Microbulbifer , and Pseudorhizobium were important supporting genera for the DYH group; Lysobacter was a key supporting genus for the DYBT group; Paucibacter , Enterococcus , Arthrobacter , and Bacillus were critical supporting genera for the DYB group. Figure 6 LEfSe analysis among four groups. Functional predictive analysis The figure presents a comprehensive comparative analysis of functional gene category distributions across four experimental groups (DYH, DYB, DYHT, DYBT) through three systematically organized panels (a-c), each employing a dual-plot design to visualize relative abundances and statistically significant differences simultaneously. Panel (a) contrasts DYH (blue bars) and DYB groups, revealing that "Function unknown" represents the most abundant category (15.2% mean proportion), followed by "Translation, ribosomal structure, and biogenesis" (12.8%), with statistically significant differences observed in "Signal transduction mechanisms" and "Coenzyme transport and metabolism." The right-side dot plots with 95% confidence intervals quantitatively demonstrate these inter-group variations, where positive values indicate DYH predominance and negative values reflect DYB dominance in specific functional categories. Panel (b) compares DYHT and DYH groups, showing distinctive metabolic profiles with "Secondary metabolites biosynthesis" exhibiting the most pronounced difference and "Cell wall/membrane biogenesis" showing substantial variation. Notably, "Defense mechanisms" display a consistent 8.5% mean proportion across groups, while "Carbohydrate transport and metabolism" maintains stable abundance, suggesting conserved metabolic functions. The statistical annotations reveal moderate evidence for differences, with confidence intervals spanning 1.5–3.2% difference magnitudes. Panel (c) provides critical insights into DYB versus DYBT comparisons, where "Posttranslational modification" emerges as the dominant functional category (18.3% mean proportion), followed by "Carbohydrate transport and metabolism" (10.1%). The most statistically robust difference occurs in "Energy production and conversion" (p = 7.02e-03), with DYBT showing enhanced functionality. The visualization employs a standardized y-axis (0–20% proportion range) across all panels, facilitating cross-comparison of 18 + functional categories, including "Nucleotide transport and metabolism" (6.2–7.5%) and "Intracellular trafficking" (4.8–5.3%). The minimalist color scheme (blue for proportions, grey for differences) and consistent layout enhance interpretability, while the integrated presentation of mean proportions and statistical significance (p < 0.05 indicated for 7/18 categories) provides a robust framework for understanding how experimental conditions shape functional potential in microbial communities. This analytical approach effectively reveals group-specific metabolic signatures, particularly the elevated "Amino acid transport" in DYH (9.8%) versus DYBT (7.1%) and specialized functions like "Chromatin structure and dynamics" showing differential expression patterns. Figure 7 Functional predictive analysis among different groups. Discussion​​ As a complex micro-ecosystem, intestinal microbiota plays a crucial role in maintaining host health, modulating metabolism, and enhancing environmental adaptability. The establishment of intestinal microbiota in wild birds is influenced by a variety of factors, including both biotic and abiotic components[ 15 ]. These factors encompass dietary composition, anthropogenic impacts[ 16 ], seasonal variations[ 17 ], geographical distribution, and environmental microbial interactions[ 18 ]. This study investigates the gut microbiota of the common and white crane in the Yellow River Delta, aiming to elucidate its characteristics and identify influencing factors. The findings deepen our understanding of avian intestinal microbiota and offer valuable insights for wetland ecosystem conservation and avian species protection. Alpha diversity analysis revealed significant differences in the ACE and Chao1 indices between the DYHT and DYH groups (p = 0.0045 and p = 0.0046, respectively), suggesting that environmental factors exert substantial selective pressure on the intestinal microbial community of common cranes. This conclusion aligns with prior research indicating that the intestinal microbiota of birds exhibits greater plasticity compared to mammals[ 5 , 19 ]. The high Shannon and Simpson indices observed across all groups reflect high species diversity and evenness within the crane gut microbiota, potentially attributable to their varied dietary habits and intricate wetland habitats. The beta diversity analysis revealed a significant separation of microbial communities among different groups, especially between foraging samples (DYH/DYB) and fecal samples (DYHT/DYBT). The PCoA plot showed that PC1 explained 25.00% of the variation, indicating that the spatial distribution of microbial communities is closely related to cranes' foraging and digestive processes. This suggests that the intestinal microbiota is not only affected by the host's genetic background but also shaped by environmental factors such as diet and habitat. At the phylum level, Proteobacteria, Actinobacteriota, Firmicutes, and Bacteroidota were the dominant phyla in cranes' intestinal microbiota, consistent with previous studies on avian intestinal microbiota[ 20 , 21 ]. Members of the Proteobacteria phylum exhibit highly diverse morphologies and versatile physiological traits, providing them with a significant competitive advantage across various ecological niches. Studies investigating the interaction between Proteobacteria and their hosts have demonstrated that the host can modulate its intestinal microbiota via epigenetic mechanisms. These mechanisms include the regulation of immune gene expression, intestinal barrier function, and histone and DNA modifications mediated by non-coding RNA. Such interactions contribute to the maintenance of intestinal homeostasis and may potentially influence the host's adaptive traits[ 22 ]. Previous study found that the phylum Firmicutes is prevalent in the intestinal microbiota and plays a crucial role in assisting the host with the breakdown of complex carbohydrates, polysaccharides, and fats, thereby enhancing energy metabolism[ 23 ]. Actinomycetes possess the ability to degrade organic matter and play a pivotal role in material cycling within soil and various environmental settings. These microorganisms are capable of breaking down complex organic compounds into simpler forms, thus promoting the cycling of materials and nutrients in ecosystems. Additionally, they are widely distributed within the intestinal microbiota of numerous bird species[ 20 , 24 ]. Thus, the high abundance of Proteobacteria may be related to their ability to adapt to various environmental stresses[ 25 ], while Actinobacteriota and Firmicutes play essential roles in nutrient metabolism and immune regulation. At the genus level, Ligilactobacillus , Rhodococcus , Pseudomonas , and Sphingomonas were identified as key genera in different groups. For example, the Ligilactobacillus was a key biomarker for the DYH group, possibly related to the common crane's adaptation to a plant-rich diet. Rhodococcus has been reported to be able to degrade hydrocarbons and other pollutants, suggesting that it may play a role in detoxifying environmental toxins in the intestinal tract of cranes. The LEfSe analysis further identified specific microbial signatures associated with each group. For example, Chloroflexi, Gemmatimonadota, and Myxococcota were identified as key supporting phyla for the DYHT group, whereas Proteobacteria and Actinobacteriota were predominant in the DYH group. These results indicate that different microbial taxa play specialized roles in adapting to distinct ecological niches, which is critical for the survival and reproduction of cranes in complex wetland ecosystems. Functional predictive analysis revealed that the intestinal microbiota of cranes exhibits a wide range of metabolic functions, including amino acid transport, energy production and conversion, and secondary metabolite biosynthesis. The elevated proportion of "Amino acid transport" in the DYH group (9.8% compared to 7.1% in DYBT) may fulfill the increased protein demands of common cranes during the wintering period. The consistent proportion of "Defense mechanisms" (8.5 ± 0.3%) across groups suggests that the intestinal microbiota plays a conserved role in pathogen resistance, which is vital for cranes inhabiting wetland environments where they are frequently exposed to diverse pathogens. Recent ecological water replenishment initiatives in the Yellow River Delta, such as water-sediment regulation, have enhanced wetland vegetation coverage, which may provide more diverse food resources for cranes and promote the diversity of their intestinal microbiota. The resurgence of salt-tolerant plants like Suaeda salsa offers abundant food for cranes and modulates their gut flora by enhancing dietary diversity. The intestinal microbiota's sensitivity to environmental changes suggests that microbial monitoring could serve as an early warning system for habitat degradation in wetland ecosystems. By tracking changes in the intestinal microbiota of cranes, we can assess the health of wetland ecosystems and the potential impact of environmental changes on bird populations. Integrating microbiome science with traditional conservation strategies can enhance the protection of wetland species. For instance, habitat restoration initiatives that foster the growth of salt-tolerant plants can provide essential food resources for cranes while indirectly supporting the health of their gut microbiota. This integrative approach will ensure cranes' long-term survival and reproductive success in the context of global environmental changes. While this study offers valuable insights into the gut microbiota of cranes, several methodological limitations warrant acknowledgment. The results' relatively high proportion of unclassified bacteria (mean 12.4%) highlights existing gaps in avian microbiome databases, particularly for wetlands-associated taxa. This limitation may have influenced the precision of the SourceTracker analysis, as many environmentally acquired microbes remain uncultured and insufficiently characterized. The DADA2 pipeline utilized for sequence denoising effectively mitigated sequencing artifacts but might have inadvertently collapsed rare yet ecologically significant amplicon sequence variants (ASVs). Future investigations should explore the application of more comprehensive sequencing methodologies, such as metagenomics, to fully capture intestinal microbiota diversity and enhance the precision of functional predictions. In conclusion, this study offers a detailed characterization of the gut microbiota in wild cranes inhabiting the Yellow River Delta, elucidating the intricate interplay among host, microbiota, and environment. These findings deepen our comprehension of avian microbiome ecology and furnish a scientific foundation for the conservation and management of wetland ecosystems and crane populations. Additional research is warranted to comprehensively decipher the mechanisms underpinning these interactions and devise effective conservation strategies in light of global environmental change. Summary​​ This study provides the most comprehensive characterization of gut microbiota in wild cranes inhabiting the Yellow River Delta to date. Through multi-faceted analyses of 24 samples across four groups, we demonstrate that (1) crane gut ecosystems exhibit remarkable group-specific structuring at both taxonomic (phylum to genus) and functional levels; (2) environmental acquisition and host selection jointly shape microbial communities; and (3) predicted metabolic capabilities reflect adaptations to challenging wetland conditions. Identifying Ligilactobacillus , Rhodococcus , and other biomarkers establishes baseline data for future monitoring, while functional predictions reveal conserved roles in nutrient metabolism and host defense. Methodologically, we validate paired foraging-fecal sampling as an effective strategy to track microbial turnover. Ecologically, our findings underscore how wetland modifications (e.g., salinity changes from water regulation) may indirectly impact crane health through microbial pathways. These insights fundamentally advance the understanding of avian microbiome ecology while providing science-based guidance for conservation practices in dynamic wetland ecosystems. Future research should prioritize temporal sampling, culturomic validation, and experimental manipulations to fully unravel the complex interplay between cranes, their microbiota, and rapidly changing wetland environments. Declarations Ethics approval and consent to participate This study did not require ethical approval because it involved only the collection of plant samples without causing harm or distress. Consent for publication Not applicable. Competing interests Statement The authors declare no conflict of interest. Funding The Chongqing Technological Innovation and Application Development Special Project (CSTB2024TIAD-LDX0019). National Key Research and Development Program of China (2023YFF1305000), and the National Natural Science Foundation of China (32271557). Author Contribution S.S. and Y.L.: conceptualization. methodology, investigation, and writing—original draft preparation. L.L.: software. X.G.: validation. B.Z.: formal analysis. J.Y.: resources. S.S: data curation. Q.W. and J.W.: writing, review, and editing. Q.W.: supervision, project administration, and funding. acquisition. Acknowledgement Not applicable Data Availability All sequences analyzed in the present study can be assessed in the SRA database under the accession number SAMN50429475-50429498. References Baeckhed F, Ley RE, Sonnenburg JL, Peterson DA, Gordon JI. Host-Bacterial Mutualism in the Human Intestine. Science. 2005;307(5717):1915–20. Davidson GL, Wiley N, Cooke AC, Johnson CN, Fouhy F, Reichert M et al. Diet induces parallel changes to the gut microbiota and problem solving performance in a wild bird. Sci Rep. 2019;10. Shapira M. Gut Microbiotas and Host Evolution: Scaling Up Symbiosis. Trends in Ecology & Evolution; 2016. Waite DW, Taylor MW. Characterizing the avian gut microbiota: membership, driving influences, and potential function. Front Microbiol. 2014;5(223):223. Hird SM. Evolutionary Biology Needs Wild Microbiomes. Front Microbiol. 2017;8:725. García-Amado MA, Shin H, Sanz V, Lentino M, Domínguez-Bello MG. Comparison of gizzard and intestinal microbiota of wild neotropical birds. PLoS ONE. 2018;13(3):e0194857. Laviad-Shitrit S, Izhaki I, Lalzar M, Halpern M. Comparative Analysis of Intestine Microbiota of Four Wild Waterbird Species. Front Microbiol. 2019;10. Bodawatta KH, Kleková I, Kleka J, Puejová K, Koane B, Poulsen M et al. Specific gut bacterial responses to natural diets of tropical birds. Sci Rep. 2022;12. Li ZT, Duan TF, Wang L, Wu JW, Meng YJ, Bao DL, et al. Comparative analysis of the gut bacteria and fungi in migratory demoiselle cranes (Grus virgo) and common cranes (Grus grus) in the Yellow River Wetland, China. Front Microbiol. 2024;15. 10.3389/fmicb.2024.1341512 . Liu JP, Li LQ, Shao Y, Li WJ, Li YZ, Wang HG, et al. Comparative analysis of intestinal flora communitycomposition and diversity ofAnas platyrhynchosandAnser fabalisin the Yellow River Delta. Anim Biology. 2024;74(4):435–51. 10.1163/15707563-bja10149 . Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA et al. vol 37, pg 852,. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2 (2019). Nature biotechnology. 2019;(9):37. Callahan BJ, Mcmurdie PJ, Rosen MJ, Han AW, Johnson A, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;(13):581–3. Grice EA, Kong HH, Conlan S, Deming CB, Davis J, Young AC, et al. Topographical and temporal diversity of the human skin microbiome. Science. 2009;324(5931):1190–2. 10.1126/science.1171700 . Dubois PC, Trynka G, Franke L, Hunt KA, Romanos J, Curtotti A, et al. Multiple common variants for celiac disease influencing immune gene expression. Nat Genet. 2010;42(4):295–302. 10.1038/ng.543 . Hird SM, Carstens BC, Cardiff SW, Dittmann DL, Brumfield RT. Sampling locality is more detectable than taxonomy or ecology in the gut microbiota of the brood-parasitic Brown-headed Cowbird (Molothrus ater). Peerj. 2014;2:e321. Matthew F, Veit R, Megan H et al. Effects of urbanization on the foraging ecology and microbiota of the generalist seabird Larus argentatus. PLoS ONE. 2018. Dong Y, Xiang X, Zhao G, Song Y, Zhou L. Variations in gut bacterial communities of hooded crane (Grus monacha) over spatial-temporal scales. PeerJ. 2019;7:e7045. Xingjia X, Fengling, Zhang, Rong, Fu et al. Significant Differences in Bacterial and Potentially Pathogenic Communities Between Sympatric Hooded Crane and Greater White-Fronted Goose. Front Microbiol. 2019. Song SJ, Sanders JG, Metcalf J, Amato K, Taylor MW, Mazel F, et al. Comparative Analyses of Vertebrate Gut Microbiomes Reveal Convergence between Birds and Bats. mBio. 2020;11(1):e02901–19. Zhao JS, Wang YJ, Zhang M, Yao YF, Tian H, Sang ZL, et al. Structural changes in the gut microbiota community of the black-necked crane (Grus nigricollis) in the wintering period. Arch Microbiol. 2021;203(10):6203–14. 10.1007/s00203-021-02587-x . Davidson GL, Somers SE, Wiley N, Johnson CN, Reichert MS, Ross RP, et al. A time-lagged association between the gut microbiome, nestling growth and nestling survival in wild great tits. Cold Spring Harbor Laboratory; 2020. Pepke ML, Hansen SB, Limborg MT. Unraveling host regulation of gut microbiota through the epigenome-microbiome axis. Trends Microbiol. 2024;32(12):1229–40. 10.1016/j.tim.2024.05.006 . Flint HJ, Bayer EA, Rincon MT, Lamed R, White BA. Polysaccharide utilization by gut bacteria: potential for new insights from genomic analysis. Nat Rev Microbiol. 2008;6(2):121–31. Wu H, Wu FT, Zhou QH, Zhao DP. Comparative Analysis of Gut Microbiota in Captive and Wild Oriental White Storks: Implications for Conservation Biology. Front Microbiol. 2021;12:652. Cho H, Lee WY. Interspecific comparison of the fecal microbiota structure in three Arctic migratory bird species. Ecol Evol. 2020;10(12):5582–94. 10.1002/ece3.6299 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7707554","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":528200610,"identity":"5edad4cd-8a30-4424-b10a-54688ad7ca26","order_by":0,"name":"Shuai Shang","email":"","orcid":"","institution":"Shandong University of Aeronautics","correspondingAuthor":false,"prefix":"","firstName":"Shuai","middleName":"","lastName":"Shang","suffix":""},{"id":528200611,"identity":"f89b275f-514f-4b1c-9f15-354b60555ce3","order_by":1,"name":"Yunpeng Liu","email":"","orcid":"","institution":"Shandong University of 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1","display":"","copyAsset":false,"role":"figure","size":254146,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSequence statistics and OTU cluster analysis\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-7707554/v1/d0c2db4e7d6aa88ea1e5b7d7.png"},{"id":93623874,"identity":"1c5d1977-aafd-4331-adca-7e2e6520f147","added_by":"auto","created_at":"2025-10-15 18:38:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":326460,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eα-diversity analysis of the microbiota among four groups. Note: \u003c/strong\u003eThe x-axes label the four groups, distinguished by colors (blue: DYHT, orange: DYH, green: DYB, red: DYBT). Each box represents the interquartile range (IQR), with the median marked inside and whiskers extending to non-outlier data extremes. The layout is meticulously organized, with clear titles, annotations, and p-values above relevant comparisons.\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-7707554/v1/babe93dbd0e731af656ba68f.png"},{"id":93623885,"identity":"ad88aa78-3b7d-497b-9809-da040d8db0a6","added_by":"auto","created_at":"2025-10-15 18:38:35","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":315391,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eβ-diversity analysis of the microbiota among four groups. Note:\u003c/strong\u003e Sub-figure.3a combines hierarchical clustering with taxonomic composition analysis. Sub-figure.3b presents a PCoA plot based on Bray-Curtis dissimilarity, with principal coordinate 1 (PC1) explaining 25.00% of the variation and PC2 representing additional variance. Samples are segregated by group with confidence ellipses emphasizing group-specific clustering patterns.\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-7707554/v1/20dec7f3bd10b60d0b11ab13.png"},{"id":93623879,"identity":"3c83f963-6b1f-4be4-b12e-21dd8bff2df2","added_by":"auto","created_at":"2025-10-15 18:38:35","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":256642,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of microbial composition characteristics among four groups\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-7707554/v1/4485e3d6e0ebc568c0b29938.png"},{"id":93624084,"identity":"969f9566-a8ea-4cff-89bf-e15a42dafce8","added_by":"auto","created_at":"2025-10-15 18:46:35","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1531471,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSourceTracker analysis among four groups\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-7707554/v1/24681620ab7264cecd56345c.png"},{"id":93623886,"identity":"9adefc64-f3a0-4358-9c7d-c8a4d8590550","added_by":"auto","created_at":"2025-10-15 18:38:35","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2034342,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLEfSe analysis among four groups.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig.6.png","url":"https://assets-eu.researchsquare.com/files/rs-7707554/v1/137b74491552a7ad7b68f795.png"},{"id":93623882,"identity":"07c53167-7056-4391-abca-7a5a85a8b78d","added_by":"auto","created_at":"2025-10-15 18:38:35","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":722742,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional predictive analysis among different groups.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig.7.png","url":"https://assets-eu.researchsquare.com/files/rs-7707554/v1/6134eb17afe177edc18a3201.png"},{"id":94824556,"identity":"0765c72a-dc62-40e9-b674-0b714c83a59f","added_by":"auto","created_at":"2025-10-31 06:49:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6388647,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7707554/v1/ebc3f5e8-37ca-4afd-aae7-c2c8086cb069.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Investigation into the Relationship between Gut Microbiota of Cranes and Their Feeding Environment in the Yellow River Delta","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAs one of the most complex micro-ecosystems in animals, the intestinal microbiota is essential in maintaining host health, regulating metabolism, and enhancing environmental adaptability[1, 2]. In recent years, it has become a key focus in interdisciplinary research. Evidence suggests that the intestinal microbiota profoundly influences animals' physiological functions and ecological adaptation strategies through nutrient metabolism, immune regulation, and interactions between the host and its environment. The gut microbiota is frequently characterized as a crucial \"microbial organ\" within an organism, and the symbiotic entity comprising the host animal and its microbiota is termed a \"holobiont\"[3]. Birds represent a highly successful group of organisms, exhibiting remarkable species and genetic diversity[4]. In comparison to mammals, the gut microbiota of birds is characterized by relatively lower stability and greater plasticity[5]. Wild birds exhibit a wide range of dietary preferences, flight behaviors, and developmental strategies[6], which contributes to the complexity of their intestinal microbiota. Changes in the intestinal microbiota of wild birds have significant effects on the host's physiological characteristics, nutritional status, and stress response[7]. The dynamic regulation of the intestinal microbiota is frequently considered a key mechanism by which birds enhance their ecological adaptability[8].\u003c/p\u003e\n\u003cp\u003eThe common crane (\u003cem\u003eGrus grus\u003c/em\u003e) is a large migratory bird species with a wide distribution across wetland ecosystems in Eurasia. As a flagship species for wetland ecosystems, its survival status directly indicates habitat quality. In China, the common crane exhibits extensive distribution. During its migration period, it is commonly observed in most provincial-level administrative regions, particularly in the Yellow River Delta and the Yangtze River Basin[9]. Its breeding grounds are primarily in northern Xinjiang, Inner Mongolia, and Heilongjiang. At the same time, wintering areas are predominantly distributed across the Yellow River Basin, the middle and lower reaches of the Yangtze River, and the Yunnan-Guizhou Plateau. Driven by global climate change, wintering ranges have shown a significant trend of expanding northward. In recent years, substantial wintering populations have been recorded in regions extending beyond\u0026nbsp;the Yellow River Delta, including southern parts of Northeast China. As China's largest newly formed wetland, the Yellow River Delta possesses a unique saline-alkaline environment and water-sediment dynamics that significantly influence the food chain structure of the common crane's habitat. Studies indicate that the salinity gradient within the wetland markedly affects the soil microbial community, which propagates through the food chain to impact avian gut microbiota. For instance, a high-salinity environment suppresses the activity of nitrifying bacteria, potentially reducing nitrogen availability in wetland vegetation and indirectly affecting food quality for common cranes. Moreover, recent ecological water replenishment initiatives in the Yellow River Delta, such as water-sediment regulation, have substantially enhanced wetland vegetation coverage.\u003c/p\u003e\n\u003cp\u003eThe intestinal microbiota plays a pivotal role in digestion and absorption, immune regulation, and environmental adaptation in the common crane. For example, it assists the host in adapting to seasonal dietary shifts and migratory stress by degrading plant fibers, synthesizing essential nutrients such as short-chain fatty acids, and resisting pathogenic microorganisms. Recent studies have demonstrated that the common crane's intestinal microbiota diversity is closely associated with its dietary composition and habitat environment. Notably, there are marked differences in microbiota composition between wild and semi-captive populations, highlighting the significant influence of environmental factors on microbiota regulation. The resurgence of salt-tolerant plants like \u003cem\u003eSuaeda salsa\u003c/em\u003e has provided abundant food resources for common cranes and may modulate their gut flora by enhancing dietary diversity. The stability of the intestinal microbiota in common cranes is susceptible to various environmental challenges. In particular, saline-alkali stress exerts a substantial impact, as soil salinity in the Yellow River Delta affects the microbial community ingested by the common crane through the food chain.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Area and Sample Collection\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe samples for this study were collected from the 1200 Forest Farm area within the Yellow River Delta National Nature Reserve. This region is characterized by diverse wetland types, such as estuarine rivers, muddy tidal flats, and rotational farmlands, offering extensive habitats and foraging grounds for cranes. During the winter season, the primary crane species in this area are the common crane and the white crane. The populations of these two species are relatively dispersed. For the sampling process, we divided the team into two: one focused on monitoring the activities of the White Crane, while the other monitored the Grey Crane. The primary emphasis was placed on observing their foraging behavior and defecation patterns. After observing defecation events, samples were collected from both foraging and defecation sites. Specifically, foraging samples of the common crane (DYH1-6), fecal samples of the common crane (DYHT1-6), fecal samples of the white crane (DYB1-6), and foraging samples of the white crane(DYBT1-6). After collection, the samples were stored in ice boxes and transported to the laboratory, where they were preserved at -20°C\u0026nbsp;for subsequent DNA extraction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSample treatment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, 0.3 g of each sample was precisely weighed. Total DNA extraction from fecal samples was conducted using the TIANamp Stool DNA Kit (TianGen Biotech (Beijing) Co., Ltd.). The concentration and purity of the extracted DNA were evaluated using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, USA). Subsequently, PCR amplification was performed with primers 515F (5′-GTGCCAGCMGCCGCGG-3′) and 907R (5′-CCGTCAATTCMTTTRAGTTT-3′) to construct the sequencing library[10]. The fragment size and concentration were quantified after library preparation, and high-throughput sequencing was carried out on an Illumina NovaSeq 6000 platform (Biomarker Technologies Co., Ltd., Beijing). Raw image data files generated during high-throughput sequencing were processed via base calling to produce raw sequencing reads stored in FASTQ format.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis file format encapsulates both sequence information and corresponding quality scores. Sequence denoising was achieved using the DADA2 algorithm implemented in QIIME2 2020.6[11], enabling the identification of amplicon sequence variants (ASVs). Specifically, the DADA2 method facilitated the removal of noise, splicing of paired-end sequences, and eliminating chimeric sequences, thereby generating a final dataset comprising high-quality, non-chimeric reads[12]. The QIIME software generated species abundance tables at various classification levels. At the same time, the R programming language was used to visualize the community structure diagrams of each sample across different taxonomic levels. Alpha diversity reflects the species richness and evenness within a single sample. Several metrics, including Chao1, Shannon, Simpson, and Coverage, are commonly used to quantify alpha diversity. The Chao1 and Ace indices specifically estimate species richness, which refers to the sample's total number of distinct species. In contrast, the Shannon and Simpson indices measure species diversity, considering both species richness and the evenness of their distribution within the community. Under conditions of equal species richness, greater evenness among species leads to higher overall diversity. Higher values of the Shannon and Simpson indices indicate increased species diversity within the sample[13]. Beta diversity analysis was performed using the QIIME software to assess and compare species diversity similarity across different samples. Principal Component Analysis (PCA) was used to analyze and simplify complex datasets by decomposing variance and representing the differences among multiple sets of data on a two-dimensional coordinate graph[14]. The PCA analysis graphs were generated using the R language tool.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003eSequence statistics and OTU cluster analysis\u003c/h2\u003e\u003cp\u003e1,808,455 paired-end reads were obtained from the sequencing of 24 samples. Following quality control and assembly, 1,611,112 clean reads were generated, with each sample producing no fewer than 35,712 clean reads and an average of 67,130 clean reads per sample. Specifically, the DYB group yielded 336,983 clean reads, the DYBT group yielded 425,181 clean reads, the DYH group yielded 422,278 clean reads, and the DYHT group yielded 426,730 clean reads. In this study, a total of 58,205 operational taxonomic units (OTUs) were identified across four groups. The data quality is evaluated by systematically analyzing the number of sample sequences across each stage of the statistical process. This evaluation primarily entails conducting a detailed statistical analysis of various parameters, such as the number of sequences and sequence length.\u003c/p\u003e\u003cp\u003eSpecifically, 11,826, 13,067, 19,888, and 19,369 OTUs were detected in the DYB, DYH, DYHT, and DYBT groups. Furthermore, 895 common OTUs were shared between the DYB and DYH groups; 2,887 common OTUs were identified between the DYHT and DYBT groups; 1,092 common OTUs were observed between the DYH and DYHT groups; 832 common OTUs were found between the DYB and DYBT groups; and 218 common OTUs were present across all four groups.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFigure\u0026nbsp;1 Sequence statistics and OTU cluster analysis\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eα-diversity analysis of the microbiota among four groups\u003c/h2\u003e\u003cp\u003eThe image compares alpha diversity indices comprehensively across four distinct groups (DYHT, DYH, DYB, and DYBT) using four box-and-whisker plots. Each plot corresponds to a specific diversity index: ACE (a), Chao1 (b), Shannon (c), and Simpson (d), with Student\u0026rsquo;s t-test employed to assess statistical significance. The ACE index plot (a) reveals significant differences between groups, with p-values such as 0.0045 (DYHT vs. DYH) and 0.00061 (DYB vs. DYBT), indicating notable variations in species richness. Similarly, the Chao1 index plot (b) demonstrates comparable p-values (e.g., 0.0046), reinforcing the observed disparities in community richness. The Shannon index plot (c), reflecting species richness and evenness, exhibits p-values like 0.0034, suggesting significant differences in diversity. The Simpson index plot (d), focusing on dominance, shows p-values such as 0.012, highlighting variations in community dominance structures. The y-axes are scaled according to each index: the Shannon index ranges approximately from 7 to 12, while the Simpson index spans from 0.96 to 1.00, indicating high evenness across groups. The ACE and Chao1 plots emphasize richness differences, whereas Shannon and Simpson plots integrate evenness, providing a holistic view of alpha diversity. The consistent use of the Student\u0026rsquo;s t-test underscores rigorous statistical validation. The visual clarity of the 2\u0026times;2 grid facilitates cross-index comparisons, revealing that DYHT and DYH often exhibit more pronounced differences (e.g., \u003cem\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.005\u003c/em\u003e) compared to other group pairs. This structured presentation effectively communicates the heterogeneity in microbial or ecological communities across the four experimental conditions, making it a robust visual aid for diversity analysis.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFigure\u0026nbsp;2 α-diversity analysis of the microbiota among four groups. Note\u003c/b\u003e: The x-axes label the four groups, distinguished by colors (blue: DYHT, orange: DYH, green: DYB, red: DYBT). Each box represents the interquartile range (IQR), with the median marked inside and whiskers extending to non-outlier data extremes. The layout is meticulously organized, with clear titles, annotations, and p-values above relevant comparisons.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eβ-diversity analysis of the microbiota among four groups\u003c/h3\u003e\n\u003cp\u003eThe figure presents a comprehensive analysis of microbial community structure across multiple sample groups (DYB, DYH, DYHT and DYBT) through two complementary analytical approaches. In sub-figure.3a, the left panel displays a hierarchical clustering dendrogram based on community similarity, revealing distinct sample groupings (DYB, DYH, DYHT and DYBT). The right panel illustrates the relative abundance of microbial taxa through stacked bar charts, where dominant genera include \u003cem\u003eSphingomonas\u003c/em\u003e, \u003cem\u003eRhodococcus\u003c/em\u003e, \u003cem\u003ePseudomonas\u003c/em\u003e, and other taxa such as \u003cem\u003eArthrobacter\u003c/em\u003e, \u003cem\u003eCryobacterium\u003c/em\u003e, and \u003cem\u003eLigilactobacillus\u003c/em\u003e. The plot coordinates indicate specific sample positions, demonstrating that DYH samples cluster in the upper-left quadrant. In contrast, DYHT samples occupy the lower-right region, suggesting distinct community structures between these groups. The combination of these approaches provides robust evidence for group-specific microbial signatures, with the PCoA particularly highlighting the strong separation between DYH and DYHT communities. This integrated visualization effectively communicates both taxonomic and ecological dimensions of microbial community variation across the experimental groups.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eFigure\u0026nbsp;3 β-diversity analysis of the microbiota among four groups. Note\u003c/strong\u003e\u003cp\u003eSub-figure.3a combines hierarchical clustering with taxonomic composition analysis. Sub-figure.3b presents a PCoA plot based on Bray-Curtis dissimilarity, with principal coordinate 1 (PC1) explaining 25.00% of the variation and PC2 representing additional variance. Samples are segregated by group with confidence ellipses emphasizing group-specific clustering patterns.\u003c/p\u003e\u003c/p\u003e\n\u003ch3\u003eAnalysis of microbial composition characteristics\u003c/h3\u003e\n\u003cp\u003eThe figure presents a comparative analysis of bacterial community composition across four experimental groups (DYHT, DYH, DYB, DYBT) through two vertically stacked bar charts (a and b), each illustrating the relative abundance of microbial taxa at different taxonomic resolutions. Our results showed that the relative abundance (as proportions) of various bacterial phyla across four samples: DYHT, DYH, DYB, and DYBT. Proteobacteria is the dominant phylum in DYHT (30.44%), DYH (37.42%), and DYBT (35.06%), but in DYB, Firmicutes has the highest abundance (28.98%), while Proteobacteria drops to 24.61%. Firmicutes shows significant variability, being relatively low in DYHT (5.68%) and DYBT (4.75%) but substantially higher in DYH (25.00%) and DYB (28.98%). Actinobacteriota maintains moderate levels across all samples, peaking in DYH (23.13%) and DYB (23.31%), but decreases in DYBT (12.44%). Bacteroidota, Chloroflexi, Acidobacteriota, and other minor phyla generally exhibit lower abundances with minimal fluctuations; for instance, Bacteroidota ranges from 3.91% in DYH to 12.05% in DYBT. Overall, the data reveals sample-specific shifts, such as the prominence of Firmicutes in DYB contrasting with Proteobacteria dominance elsewhere, potentially indicating environmental or experimental influences on microbial community structure. We analyzed the relative abundance of bacterial genera (or unclassified groups) across the same four samples (DYHT, DYH, DYB, DYBT). The most striking observation is the highly variable dominance of specific genera in different samples. In the DYH group, \u003cem\u003eLigilactobacillus\u003c/em\u003e was exceptionally abundant (12.10%), far exceeding its presence in other samples (0.21\u0026ndash;0.62%). Conversely, \u003cem\u003eCryobacterium\u003c/em\u003e dominates in the DYB group (9.18%), while its abundance is much lower elsewhere (0.18\u0026ndash;0.29%). DYH also showed significant enrichment of \u003cem\u003eOchrobactrum\u003c/em\u003e (6.58%) and \u003cem\u003eRhodococcus\u003c/em\u003e (5.37%), genera present at very low levels (\u0026lt;\u0026thinsp;0.06% and \u0026lt;\u0026thinsp;0.26% respectively) in the other samples. The \u003cem\u003ePaucibacter\u003c/em\u003e was notably high in DYB (5.55%) compared to others (\u0026lt;\u0026thinsp;1.68%). While \u003cem\u003ePseudomonas\u003c/em\u003e is moderately abundant in the DYH (4.03%) and DYBT group (1.45%), and \u003cem\u003eArthrobacter\u003c/em\u003e peaks in the DYB group (4.07%). Overall, the data reveals dramatic sample-specific enrichment of particular genera (Ligilactobacillus in DYH, Cryobacterium and Paucibacter in DYB, Ochrobactrum and Rhodococcus in DYH), suggesting distinct environmental conditions or selective pressures shaping the microbial communities in each sample.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFigure\u0026nbsp;4 Analysis of microbial composition characteristics among four groups\u003c/b\u003e\u003c/p\u003e\n\u003ch3\u003eSourceTracker analysis\u003c/h3\u003e\n\u003cp\u003eThe figure presents a comparative source-tracking analysis between DYB vs. DYBT (panel a) and DYH vs. DYHT (panel b) microbial communities. The stacked bar charts reveal that the \"Unknown\" source (blue) dominates across all samples (DYB_DYB01-06, DYH_DYH01-06), consistently exhibiting higher relative abundance (bar height) than the target sources DYBT (red, panel a) or DYHT (red, panel b). This pattern suggests a limited contribution from the hypothesized sources (DYBT/DYHT) to the respective microbial communities. The systematic arrangement of sample pairs facilitates direct comparison, demonstrating that while trace amounts of DYBT/DYHT signatures are detectable (minor red segments), the predominant microbial origins remain uncharacterized (\"Unknown\"). These results indicate insufficient reference database coverage or substantial compositional divergence between the target sources and analyzed communities.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFigure\u0026nbsp;5 SourceTracker analysis among four groups\u003c/b\u003e\u003c/p\u003e\n\u003ch3\u003eLEfSe analysis\u003c/h3\u003e\n\u003cp\u003eLEfSe is a robust bioinformatics tool developed to identify biomarkers demonstrating statistically significant differences across multiple biological groups. This approach combines non-parametric statistical tests, specifically the Kruskal-Wallis rank-sum test and pairwise Wilcoxon rank-sum tests, with linear discriminant analysis (LDA) to estimate the effect size of differentially abundant features. In analyzing microbial communities across four distinct groups, LEfSe facilitates the detection of both taxonomic clades and individual species that consistently distinguish one group from others. LEfSe analysis identified distinct microbial signatures across the four groups in this study. Specific phyla (e.g., Actinobacteria, Firmicutes) and genera (e.g., \u003cem\u003eLactobacillus\u003c/em\u003e, \u003cem\u003eBifidobacterium\u003c/em\u003e) were found to dominate specific clusters, indicating their potential roles as biomarkers for group stratification. The Chloroflexi, Gemmatimonadota, and Myxococcota served as key supporting phyla for the DYHT group; the Proteobacteria and Actinobacteriota supported the DYH group; the Bacteroidota and Acidobacteriota supported the DYBT group; and the Firmicutes supported the DYB group. At the genus level, \u003cem\u003eLigilactobacillus\u003c/em\u003e, \u003cem\u003eOchrobactrum\u003c/em\u003e, \u003cem\u003eRhodococcus\u003c/em\u003e, \u003cem\u003eCatellicoccus\u003c/em\u003e, \u003cem\u003ePseudomonas\u003c/em\u003e, \u003cem\u003eMicrobacterium\u003c/em\u003e, \u003cem\u003eMicrobulbifer\u003c/em\u003e, and \u003cem\u003ePseudorhizobium\u003c/em\u003e were important supporting genera for the DYH group; Lysobacter was a key supporting genus for the DYBT group; \u003cem\u003ePaucibacter\u003c/em\u003e, \u003cem\u003eEnterococcus\u003c/em\u003e, \u003cem\u003eArthrobacter\u003c/em\u003e, and \u003cem\u003eBacillus\u003c/em\u003e were critical supporting genera for the DYB group.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFigure\u0026nbsp;6 LEfSe analysis among four groups.\u003c/b\u003e\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eFunctional predictive analysis\u003c/h2\u003e\u003cp\u003eThe figure presents a comprehensive comparative analysis of functional gene category distributions across four experimental groups (DYH, DYB, DYHT, DYBT) through three systematically organized panels (a-c), each employing a dual-plot design to visualize relative abundances and statistically significant differences simultaneously. Panel (a) contrasts DYH (blue bars) and DYB groups, revealing that \"Function unknown\" represents the most abundant category (15.2% mean proportion), followed by \"Translation, ribosomal structure, and biogenesis\" (12.8%), with statistically significant differences observed in \"Signal transduction mechanisms\" and \"Coenzyme transport and metabolism.\" The right-side dot plots with 95% confidence intervals quantitatively demonstrate these inter-group variations, where positive values indicate DYH predominance and negative values reflect DYB dominance in specific functional categories.\u003c/p\u003e\u003cp\u003ePanel (b) compares DYHT and DYH groups, showing distinctive metabolic profiles with \"Secondary metabolites biosynthesis\" exhibiting the most pronounced difference and \"Cell wall/membrane biogenesis\" showing substantial variation. Notably, \"Defense mechanisms\" display a consistent 8.5% mean proportion across groups, while \"Carbohydrate transport and metabolism\" maintains stable abundance, suggesting conserved metabolic functions. The statistical annotations reveal moderate evidence for differences, with confidence intervals spanning 1.5\u0026ndash;3.2% difference magnitudes.\u003c/p\u003e\u003cp\u003ePanel (c) provides critical insights into DYB versus DYBT comparisons, where \"Posttranslational modification\" emerges as the dominant functional category (18.3% mean proportion), followed by \"Carbohydrate transport and metabolism\" (10.1%). The most statistically robust difference occurs in \"Energy production and conversion\" (p\u0026thinsp;=\u0026thinsp;7.02e-03), with DYBT showing enhanced functionality. The visualization employs a standardized y-axis (0\u0026ndash;20% proportion range) across all panels, facilitating cross-comparison of 18\u0026thinsp;+\u0026thinsp;functional categories, including \"Nucleotide transport and metabolism\" (6.2\u0026ndash;7.5%) and \"Intracellular trafficking\" (4.8\u0026ndash;5.3%). The minimalist color scheme (blue for proportions, grey for differences) and consistent layout enhance interpretability, while the integrated presentation of mean proportions and statistical significance (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicated for 7/18 categories) provides a robust framework for understanding how experimental conditions shape functional potential in microbial communities. This analytical approach effectively reveals group-specific metabolic signatures, particularly the elevated \"Amino acid transport\" in DYH (9.8%) versus DYBT (7.1%) and specialized functions like \"Chromatin structure and dynamics\" showing differential expression patterns.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFigure\u0026nbsp;7 Functional predictive analysis among different groups.\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion​​","content":"\u003cp\u003eAs a complex micro-ecosystem, intestinal microbiota plays a crucial role in maintaining host health, modulating metabolism, and enhancing environmental adaptability. The establishment of intestinal microbiota in wild birds is influenced by a variety of factors, including both biotic and abiotic components[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. These factors encompass dietary composition, anthropogenic impacts[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], seasonal variations[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], geographical distribution, and environmental microbial interactions[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. This study investigates the gut microbiota of the common and white crane in the Yellow River Delta, aiming to elucidate its characteristics and identify influencing factors. The findings deepen our understanding of avian intestinal microbiota and offer valuable insights for wetland ecosystem conservation and avian species protection. Alpha diversity analysis revealed significant differences in the ACE and Chao1 indices between the DYHT and DYH groups (p\u0026thinsp;=\u0026thinsp;0.0045 and p\u0026thinsp;=\u0026thinsp;0.0046, respectively), suggesting that environmental factors exert substantial selective pressure on the intestinal microbial community of common cranes. This conclusion aligns with prior research indicating that the intestinal microbiota of birds exhibits greater plasticity compared to mammals[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The high Shannon and Simpson indices observed across all groups reflect high species diversity and evenness within the crane gut microbiota, potentially attributable to their varied dietary habits and intricate wetland habitats. The beta diversity analysis revealed a significant separation of microbial communities among different groups, especially between foraging samples (DYH/DYB) and fecal samples (DYHT/DYBT). The PCoA plot showed that PC1 explained 25.00% of the variation, indicating that the spatial distribution of microbial communities is closely related to cranes' foraging and digestive processes. This suggests that the intestinal microbiota is not only affected by the host's genetic background but also shaped by environmental factors such as diet and habitat.\u003c/p\u003e\u003cp\u003eAt the phylum level, Proteobacteria, Actinobacteriota, Firmicutes, and Bacteroidota were the dominant phyla in cranes' intestinal microbiota, consistent with previous studies on avian intestinal microbiota[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Members of the Proteobacteria phylum exhibit highly diverse morphologies and versatile physiological traits, providing them with a significant competitive advantage across various ecological niches. Studies investigating the interaction between Proteobacteria and their hosts have demonstrated that the host can modulate its intestinal microbiota via epigenetic mechanisms. These mechanisms include the regulation of immune gene expression, intestinal barrier function, and histone and DNA modifications mediated by non-coding RNA. Such interactions contribute to the maintenance of intestinal homeostasis and may potentially influence the host's adaptive traits[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Previous study found that the phylum Firmicutes is prevalent in the intestinal microbiota and plays a crucial role in assisting the host with the breakdown of complex carbohydrates, polysaccharides, and fats, thereby enhancing energy metabolism[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Actinomycetes possess the ability to degrade organic matter and play a pivotal role in material cycling within soil and various environmental settings. These microorganisms are capable of breaking down complex organic compounds into simpler forms, thus promoting the cycling of materials and nutrients in ecosystems. Additionally, they are widely distributed within the intestinal microbiota of numerous bird species[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Thus, the high abundance of Proteobacteria may be related to their ability to adapt to various environmental stresses[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], while Actinobacteriota and Firmicutes play essential roles in nutrient metabolism and immune regulation.\u003c/p\u003e\u003cp\u003eAt the genus level, \u003cem\u003eLigilactobacillus\u003c/em\u003e, \u003cem\u003eRhodococcus\u003c/em\u003e, \u003cem\u003ePseudomonas\u003c/em\u003e, and \u003cem\u003eSphingomonas\u003c/em\u003e were identified as key genera in different groups. For example, the \u003cem\u003eLigilactobacillus\u003c/em\u003e was a key biomarker for the DYH group, possibly related to the common crane's adaptation to a plant-rich diet. \u003cem\u003eRhodococcus\u003c/em\u003e has been reported to be able to degrade hydrocarbons and other pollutants, suggesting that it may play a role in detoxifying environmental toxins in the intestinal tract of cranes.\u003c/p\u003e\u003cp\u003eThe LEfSe analysis further identified specific microbial signatures associated with each group. For example, Chloroflexi, Gemmatimonadota, and Myxococcota were identified as key supporting phyla for the DYHT group, whereas Proteobacteria and Actinobacteriota were predominant in the DYH group. These results indicate that different microbial taxa play specialized roles in adapting to distinct ecological niches, which is critical for the survival and reproduction of cranes in complex wetland ecosystems. Functional predictive analysis revealed that the intestinal microbiota of cranes exhibits a wide range of metabolic functions, including amino acid transport, energy production and conversion, and secondary metabolite biosynthesis. The elevated proportion of \"Amino acid transport\" in the DYH group (9.8% compared to 7.1% in DYBT) may fulfill the increased protein demands of common cranes during the wintering period. The consistent proportion of \"Defense mechanisms\" (8.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3%) across groups suggests that the intestinal microbiota plays a conserved role in pathogen resistance, which is vital for cranes inhabiting wetland environments where they are frequently exposed to diverse pathogens.\u003c/p\u003e\u003cp\u003eRecent ecological water replenishment initiatives in the Yellow River Delta, such as water-sediment regulation, have enhanced wetland vegetation coverage, which may provide more diverse food resources for cranes and promote the diversity of their intestinal microbiota. The resurgence of salt-tolerant plants like \u003cem\u003eSuaeda salsa\u003c/em\u003e offers abundant food for cranes and modulates their gut flora by enhancing dietary diversity. The intestinal microbiota's sensitivity to environmental changes suggests that microbial monitoring could serve as an early warning system for habitat degradation in wetland ecosystems. By tracking changes in the intestinal microbiota of cranes, we can assess the health of wetland ecosystems and the potential impact of environmental changes on bird populations.\u003c/p\u003e\u003cp\u003eIntegrating microbiome science with traditional conservation strategies can enhance the protection of wetland species. For instance, habitat restoration initiatives that foster the growth of salt-tolerant plants can provide essential food resources for cranes while indirectly supporting the health of their gut microbiota. This integrative approach will ensure cranes' long-term survival and reproductive success in the context of global environmental changes. While this study offers valuable insights into the gut microbiota of cranes, several methodological limitations warrant acknowledgment. The results' relatively high proportion of unclassified bacteria (mean 12.4%) highlights existing gaps in avian microbiome databases, particularly for wetlands-associated taxa. This limitation may have influenced the precision of the SourceTracker analysis, as many environmentally acquired microbes remain uncultured and insufficiently characterized.\u003c/p\u003e\u003cp\u003eThe DADA2 pipeline utilized for sequence denoising effectively mitigated sequencing artifacts but might have inadvertently collapsed rare yet ecologically significant amplicon sequence variants (ASVs). Future investigations should explore the application of more comprehensive sequencing methodologies, such as metagenomics, to fully capture intestinal microbiota diversity and enhance the precision of functional predictions. In conclusion, this study offers a detailed characterization of the gut microbiota in wild cranes inhabiting the Yellow River Delta, elucidating the intricate interplay among host, microbiota, and environment. These findings deepen our comprehension of avian microbiome ecology and furnish a scientific foundation for the conservation and management of wetland ecosystems and crane populations. Additional research is warranted to comprehensively decipher the mechanisms underpinning these interactions and devise effective conservation strategies in light of global environmental change.\u003c/p\u003e"},{"header":"Summary​​","content":"\u003cp\u003eThis study provides the most comprehensive characterization of gut microbiota in wild cranes inhabiting the Yellow River Delta to date. Through multi-faceted analyses of 24 samples across four groups, we demonstrate that (1) crane gut ecosystems exhibit remarkable group-specific structuring at both taxonomic (phylum to genus) and functional levels; (2) environmental acquisition and host selection jointly shape microbial communities; and (3) predicted metabolic capabilities reflect adaptations to challenging wetland conditions. Identifying \u003cem\u003eLigilactobacillus\u003c/em\u003e, \u003cem\u003eRhodococcus\u003c/em\u003e, and other biomarkers establishes baseline data for future monitoring, while functional predictions reveal conserved roles in nutrient metabolism and host defense. Methodologically, we validate paired foraging-fecal sampling as an effective strategy to track microbial turnover. Ecologically, our findings underscore how wetland modifications (e.g., salinity changes from water regulation) may indirectly impact crane health through microbial pathways. These insights fundamentally advance the understanding of avian microbiome ecology while providing science-based guidance for conservation practices in dynamic wetland ecosystems. Future research should prioritize temporal sampling, culturomic validation, and experimental manipulations to fully unravel the complex interplay between cranes, their microbiota, and rapidly changing wetland environments.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cp\u003e This study did not require ethical approval because it involved only the collection of plant samples without causing harm or distress.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eCompeting interests Statement\u003c/strong\u003e\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThe Chongqing Technological Innovation and Application Development Special Project (CSTB2024TIAD-LDX0019). National Key Research and Development Program of China (2023YFF1305000), and the National Natural Science Foundation of China (32271557).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eS.S. and Y.L.: conceptualization. methodology, investigation, and writing\u0026mdash;original draft preparation. L.L.: software. X.G.: validation. B.Z.: formal analysis. J.Y.: resources. S.S: data curation. Q.W. and J.W.: writing, review, and editing. Q.W.: supervision, project administration, and funding. acquisition.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eNot applicable\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll sequences analyzed in the present study can be assessed in the SRA database under the accession number SAMN50429475-50429498.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBaeckhed F, Ley RE, Sonnenburg JL, Peterson DA, Gordon JI. Host-Bacterial Mutualism in the Human Intestine. Science. 2005;307(5717):1915\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDavidson GL, Wiley N, Cooke AC, Johnson CN, Fouhy F, Reichert M et al. Diet induces parallel changes to the gut microbiota and problem solving performance in a wild bird. Sci Rep. 2019;10.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShapira M. Gut Microbiotas and Host Evolution: Scaling Up Symbiosis. Trends in Ecology \u0026amp; Evolution; 2016.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWaite DW, Taylor MW. Characterizing the avian gut microbiota: membership, driving influences, and potential function. Front Microbiol. 2014;5(223):223.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHird SM. Evolutionary Biology Needs Wild Microbiomes. Front Microbiol. 2017;8:725.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGarc\u0026iacute;a-Amado MA, Shin H, Sanz V, Lentino M, Dom\u0026iacute;nguez-Bello MG. Comparison of gizzard and intestinal microbiota of wild neotropical birds. PLoS ONE. 2018;13(3):e0194857.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLaviad-Shitrit S, Izhaki I, Lalzar M, Halpern M. Comparative Analysis of Intestine Microbiota of Four Wild Waterbird Species. Front Microbiol. 2019;10.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBodawatta KH, Klekov\u0026aacute; I, Kleka J, Puejov\u0026aacute; K, Koane B, Poulsen M et al. Specific gut bacterial responses to natural diets of tropical birds. Sci Rep. 2022;12.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi ZT, Duan TF, Wang L, Wu JW, Meng YJ, Bao DL, et al. Comparative analysis of the gut bacteria and fungi in migratory demoiselle cranes (Grus virgo) and common cranes (Grus grus) in the Yellow River Wetland, China. Front Microbiol. 2024;15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fmicb.2024.1341512\u003c/span\u003e\u003cspan address=\"10.3389/fmicb.2024.1341512\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu JP, Li LQ, Shao Y, Li WJ, Li YZ, Wang HG, et al. Comparative analysis of intestinal flora communitycomposition and diversity ofAnas platyrhynchosandAnser fabalisin the Yellow River Delta. Anim Biology. 2024;74(4):435\u0026ndash;51. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1163/15707563-bja10149\u003c/span\u003e\u003cspan address=\"10.1163/15707563-bja10149\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA et al. vol 37, pg 852,. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2 (2019). Nature biotechnology. 2019;(9):37.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCallahan BJ, Mcmurdie PJ, Rosen MJ, Han AW, Johnson A, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;(13):581\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGrice EA, Kong HH, Conlan S, Deming CB, Davis J, Young AC, et al. Topographical and temporal diversity of the human skin microbiome. Science. 2009;324(5931):1190\u0026ndash;2. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1126/science.1171700\u003c/span\u003e\u003cspan address=\"10.1126/science.1171700\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDubois PC, Trynka G, Franke L, Hunt KA, Romanos J, Curtotti A, et al. Multiple common variants for celiac disease influencing immune gene expression. Nat Genet. 2010;42(4):295\u0026ndash;302. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/ng.543\u003c/span\u003e\u003cspan address=\"10.1038/ng.543\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHird SM, Carstens BC, Cardiff SW, Dittmann DL, Brumfield RT. Sampling locality is more detectable than taxonomy or ecology in the gut microbiota of the brood-parasitic Brown-headed Cowbird (Molothrus ater). Peerj. 2014;2:e321.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMatthew F, Veit R, Megan H et al. Effects of urbanization on the foraging ecology and microbiota of the generalist seabird Larus argentatus. PLoS ONE. 2018.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDong Y, Xiang X, Zhao G, Song Y, Zhou L. Variations in gut bacterial communities of hooded crane (Grus monacha) over spatial-temporal scales. PeerJ. 2019;7:e7045.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXingjia X, Fengling, Zhang, Rong, Fu et al. Significant Differences in Bacterial and Potentially Pathogenic Communities Between Sympatric Hooded Crane and Greater White-Fronted Goose. Front Microbiol. 2019.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSong SJ, Sanders JG, Metcalf J, Amato K, Taylor MW, Mazel F, et al. Comparative Analyses of Vertebrate Gut Microbiomes Reveal Convergence between Birds and Bats. mBio. 2020;11(1):e02901\u0026ndash;19.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhao JS, Wang YJ, Zhang M, Yao YF, Tian H, Sang ZL, et al. Structural changes in the gut microbiota community of the black-necked crane (Grus nigricollis) in the wintering period. Arch Microbiol. 2021;203(10):6203\u0026ndash;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00203-021-02587-x\u003c/span\u003e\u003cspan address=\"10.1007/s00203-021-02587-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDavidson GL, Somers SE, Wiley N, Johnson CN, Reichert MS, Ross RP, et al. A time-lagged association between the gut microbiome, nestling growth and nestling survival in wild great tits. Cold Spring Harbor Laboratory; 2020.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePepke ML, Hansen SB, Limborg MT. Unraveling host regulation of gut microbiota through the epigenome-microbiome axis. Trends Microbiol. 2024;32(12):1229\u0026ndash;40. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.tim.2024.05.006\u003c/span\u003e\u003cspan address=\"10.1016/j.tim.2024.05.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFlint HJ, Bayer EA, Rincon MT, Lamed R, White BA. Polysaccharide utilization by gut bacteria: potential for new insights from genomic analysis. Nat Rev Microbiol. 2008;6(2):121\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWu H, Wu FT, Zhou QH, Zhao DP. Comparative Analysis of Gut Microbiota in Captive and Wild Oriental White Storks: Implications for Conservation Biology. Front Microbiol. 2021;12:652.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCho H, Lee WY. Interspecific comparison of the fecal microbiota structure in three Arctic migratory bird species. Ecol Evol. 2020;10(12):5582\u0026ndash;94. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/ece3.6299\u003c/span\u003e\u003cspan address=\"10.1002/ece3.6299\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Gut Microbiota, Foraging ecology, Yellow River Delta, 16S rRNA sequencing, Microbial adaptation","lastPublishedDoi":"10.21203/rs.3.rs-7707554/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7707554/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe gut microbiota plays essential roles in host health and environmental adaptation, particularly for migratory birds within wetland ecosystems. This study examined the gut microbial communities of common cranes (\u003cem\u003eGrus grus\u003c/em\u003e) and White Cranes(\u003cem\u003eGrus leucogeranus\u003c/em\u003e) in the Yellow River Delta using 16S rRNA sequencing of 24 fecal and foraging samples. Alpha diversity (ACE, Chao1, Shannon, Simpson) revealed significant inter-group differences, indicating environmental filtering effects. Beta diversity (PCoA) confirmed strong separation between foraging and fecal samples (PC1\u0026thinsp;=\u0026thinsp;25%). Dominant phyla included Proteobacteria (24.6\u0026ndash;37.4%), Firmicutes (4.8\u0026ndash;29.0%), and Actinobacteriota (12.4\u0026ndash;23.3%), with genus-level biomarkers identified via LEfSe: \u003cem\u003eLigilactobacillus\u003c/em\u003e (12.1% in DYH), \u003cem\u003eCryobacterium\u003c/em\u003e (9.2% in DYB), and \u003cem\u003eRhodococcus\u003c/em\u003e (5.4% in DYH). SourceTracker indicated predominantly unknown microbial origins (\u0026gt;\u0026thinsp;70%), suggesting uncharacterized environmental reservoirs. Functional prediction highlighted group-specific metabolic adaptations, including elevated amino acid transport in DYH (9.8% vs. 7.1% in DYBT; P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and conserved defense mechanisms (8.5% across groups). Our findings demonstrate that crane gut microbiota is shaped by synergistic host foraging behavior and wetland environmental factors. This study provides novel insights into host-microbe-environment interactions in migratory birds and suggests potential microbial indicators for monitoring wetland ecosystem health.\u003c/p\u003e","manuscriptTitle":"Investigation into the Relationship between Gut Microbiota of Cranes and Their Feeding Environment in the Yellow River Delta","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-15 18:38:30","doi":"10.21203/rs.3.rs-7707554/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dee58ed7-eb59-4d10-a25a-ac0647b6da0d","owner":[],"postedDate":"October 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-30T11:53:59+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-15 18:38:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7707554","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7707554","identity":"rs-7707554","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}