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Article

First Record on Diet and Intestinal Microbiota of Oriental Storks in Non-Traditional Overwintering Habitats

by
Yifan Zhou
,
Menglin Sun
,
Hong Wu
* and
Dapeng Zhao
*
Tianjin Key Laboratory of Conservation and Utilization of Animal Diversity, College of Life Sciences, Tianjin Normal University, Tianjin 300387, China
*
Authors to whom correspondence should be addressed.
Diversity 2026, 18(2), 64; https://doi.org/10.3390/d18020064
Submission received: 18 December 2025 / Revised: 20 January 2026 / Accepted: 22 January 2026 / Published: 26 January 2026
(This article belongs to the Section Microbial Diversity and Culture Collections)
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Abstract

Understanding diet–health linkages in endangered oriental storks (Ciconia boyciana) occupying non-traditional overwintering habitats is imperative for conservation prioritization. Integrated high-throughput sequencing and microscopic analyses revealed their food composition, gut microbiome profile, and critical associations between diet selection and health status. Dominant gut microbial phyla included Firmicutes (64.62%), Fusobacteriota (24.08%), and Bacteroidota (4.10%), with Clostridium_sensu_stricto_1 (21.93%), Paeniclostridium (14.21%), and Fusobacterium (12.58%) at genus level. Microscopic examination of fecal samples identified six plant species (three families, six genera), while sequencing detected five plant-derived and two animal-derived food families. Both methods confirmed Poaceae, Cannabaceae, and Apocynaceae; sequencing uniquely revealed Malvaceae and Leguminosae. There was a significant negative correlation between Cyprinidae and Bacteroidota (0.01 < p ≤ 0.05). Both diet composition and gut microbial structure of the Ciconia boyciana in this study reflect flexible adaptation in response to winter thermoregulation and local food availability, providing a scientific basis for evidence-based conservation of this endangered species as well as other ecologically similar species. This work offers practical guidance for habitat restoration and dietary supplementation in non-traditional wintering sites while informing conservation strategies for ecologically similar species. Due to the limited sample size, future efforts will expand sampling to more accurately characterize population-level dietary patterns and gut microbiota profiles, thereby strengthening conservation decision-making.

1. Introduction

The overwintering survival of migratory birds fundamentally depends on their adaptive responses to harsh environmental conditions and limited food resources, which directly impact individual fitness and population dynamics [1]. Faced with low temperatures and reduced food availability, these birds undergo significant behavioral, physiological, and microbial adjustments to maintain energy balance and thermoregulation [2,3]. Notably, dietary shifts in winter can reshape gut microbiota composition, a key mediator of metabolic adaptation. For example, genera such as Bacteroides, Lachnospira, and Alistipes have been linked to elevated resting metabolic rate during winter, suggesting a role in microbial-assisted heat production [4]. This microbiota-mediated metabolic plasticity enables dynamic energy reallocation, which is crucial for adapting to environmental variability. However, for species utilizing non-traditional or newly colonized overwintering sites, the interplay between diet, gut microbiota, and host health remains poorly understood. Dietary changes in novel habitats may alter microbial structure and function [5], raising critical questions about the nutritional challenges and physiological adaptability of migratory birds. Investigating these diet–health relationships is therefore essential for assessing the viability of populations, especially those that are threatened or endangered.
Dietary ecology serves as one of the cornerstones of avian conservation biology [6]. It informs critical conservation applications including dietary niche characterization [7], species protection strategies [8], and habitat management optimization [9]. Research methodologies for dietary analysis encompass both macroscopic (e.g., emetic technique [10], direct observation [11], dissection [12]) and microscopic approaches (e.g., microscopic examination technology [13], DNA barcoding [14], and high-throughput sequencing [15]). Given limitations of single methods (e.g., sample size constraints, taxonomic resolution), integrated approaches are essential for robust dietary confirmation [16]. Health assessment is particularly vital for endangered species conservation. Together, integrated diet analysis and microbiota analyses based on non-invasive sampling can elucidate the adaptive capacity of birds, thereby offering a stronger scientific basis for protecting migratory species in rapidly changing environments.
The oriental stork (Ciconia boyciana) has been classified as a first-class protected animal in China and Endangered on the IUCN Red List [17], with a range spanning China, Russia, Japan, North Korea, and South Korea. Its traditional breeding grounds are concentrated in Northeast China’s Amur–Ussuri river basins [18], with primary overwintering sites located in the middle-lower Yangtze floodplain [19]. Recent studies have documented expansions in both breeding and overwintering ranges, with some individuals utilizing stopover sites for breeding or overwintering and others adopting single locations for both life stages [20,21]. While current research has largely focused on traditional overwintering sites such as Poyang Lake [22], research in newly occupied regions including Tianjin City [23] remains relatively limited. Tianjin is situated along the East Asian–Australasian Flyway and serves as a crucial stopover site for endangered migratory species such as the oriental stork. Given the complexity of the species’ life history, which involves multiple regions and dynamic residency patterns, effective conservation requires multi-dimensional strategies. Previous work in established habitats has prioritized population dynamics [24], migration routes [21], spatial distribution [19], and behavioral characteristics [25]. However, there is still a lack of research on the impact of building new overwintering habitats. For migratory endangered bird species—taking oriental storks as the representative example—considering that their life history involves multiple regions and the types of residence in a given region are diverse, it is crucial to carry out multifaceted conservation. We hypothesize that the change of habitat drives specific shifts in the winter diet of oriental storks, and that the gut microbiota maintain core stability through functional redundancy and compositional plasticity, thereby buffering dietary disturbances as a key adaptive mechanism. Such conservation could be achieved not only through integrative research involving multiple research directions such as diet choice and health evaluation, but also relying on coordinated scientific management across regions that combines time and space.
This study aimed to characterize the diet and gut microbiota of the endangered oriental stork in newly utilized, non-traditional overwintering habitats, and to establish functional links between dietary intake and microbial community structure within this wild population. By employing an integrated methodology, combining non-invasive fecal sampling, microscopic examination, and high-throughput sequencing, this work provides the first comprehensive ecological baseline for the species in an emerging overwintering area. The utility of the findings lies in their direct application to evidence-based conservation, offering concrete insights into the oriental stork’s foraging adaptations and physiological status. These results are critical for refining local habitat management and for informing proactive conservation strategies for this and other migratory species occupying novel overwintering grounds under environmental change.

2. Materials and Methods

2.1. Study Area

Based on GPS tracking initiated in December 2021 following the release of a rehabilitated individual, an overwintering aggregation of oriental storks was identified in the Jinghai District of Tianjin, China, in January 2025. Field surveys confirmed the tracked bird had formed a stable flock with local wild storks, exhibiting prolonged residency consistent with overwintering behavior. The flock comprised approximately 40 oriental storks. This study identified and delineated residency patterns and overwintering ranges using GPS tracking technology.
Fresh fecal samples for this study were collected in a concentrated area of artificial breeding ponds and their adjacent areas in Xiaoqiuzhuang Village, Jinghai District, Tianjin City, China. The main characteristic of this location was its concentration of artificial breeding ponds, and the surrounding terrestrial habitat was mainly covered by herbaceous plants and scattered shrubs. During the sampling period, the water level in the pond dropped significantly, exposing a large area of the pond bed. One overwintering population of wild oriental storks was observed foraging in and around these ponds.

2.2. Sample Collection and Processing

From this population, eleven fresh fecal samples (Y1–Y11) were collected non-invasively. Using the GPS signal to locate individuals, defecation events were visually confirmed with binoculars. Samples were collected only after the birds had departed, ensuring minimal disturbance. Sample authenticity was rigorously verified based on the precise defecation location, characteristic fecal coloration, and the presence of species-specific footprints nearby. Each sample was collected in a 5 mL sterile tube, and placed in an insulated container with dry ice for transport. All samples were stored at −80 °C upon arrival at the laboratory to preserve microbial integrity. During sampling, concurrent habitat data were recorded, including vegetation identification within the oriental storks’ activity range.

2.3. DNA Extraction and Sequencing Genomic

DNA was extracted from fecal specimens (approximately 0.3 g per sample) using the TIANamp Stool DNA Kit (TIANGEN, Beijing, China) per manufacturer protocols. DNA concentration and purity were quantified via NanoDrop 2000 spectrophotometry (Thermo Fisher Scientific, Wilmington, DE, USA). The V3−V4 hypervariable region of bacterial 16S rRNA genes was amplified using universal primers: 338F (5′-ACTCCTACGGGAGGCAGCA-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′). The PCR protocol comprised an initial denaturation at 95 °C for 3 min, followed by 27 cycles of 95 °C for 30 s, 55 °C for 30 s, and 72 °C for 45 s, with a final extension at 72 °C for 10 min. PCR products were purified and sequenced on the Illumina MiSeq platform by Majorbio Bio-Pharm Technology Co., Ltd. (Shanghai, China).

2.4. Bioinformatics Analysis

Raw sequencing data were processed using the QIIME2 (v2021.4) pipeline. After quality filtering and denoising, operational taxonomic units (OTUs) were clustered at a 97% similarity threshold with UPARSE. Taxonomic annotation was performed via the RDP classifier (v2.13) against the SILVA 138 16S rRNA bacterial database.
Alpha diversity indices were calculated using mothur (version v.1.30.2 https://mothur.org/wiki/calculators/ (accessed on 11 September 2025)). Species richness was quantified via Ace and Chao estimators, while Simpson and Shannon indices assessed species diversity. Sobs represented the empirically observed richness, and Good’s coverage assessed the adequacy of sequencing depth for capturing microbial diversity within samples.
Microbial functional profiles were predicted with PICRUSt2 [26], and metabolic pathways were annotated based on the KEGG database [27]. Potential pathogenic traits and their contributing taxa were evaluated using BugBase (https://bugbase.cs.umn.edu/) [28].
Venn diagrams illustrated core and unique OTUs across all samples. Stacked bar plots depicted microbial composition at phylum/genus levels alongside predicted pathogenic potential. Heatmaps visualized predicted functional potential abundance patterns.

2.5. Microscopic Observation

Dietary composition of oriental storks was analyzed through microscopic examination of fecal samples. Plant specimens (including leaves, stems, roots, flowers, fruits, and seeds) were collected and separately wrapped in labeled aluminum foil. Fecal samples were similarly sealed in aluminum foil upon collection. All samples were dried at 60 °C in an electric thermostatic drying oven (BluePard, Shanghai, China) until completely dehydrated. The dried materials were then ground into fine powder using a mortar. Plant powders were treated with 5% sodium hypochlorite for 3–5 h at room temperature, rinsed through a 200-mesh sieve under distilled water, and stained with 0.01% methylene blue. Reference slides were prepared from the treated plant material and observed under the upright fluorescence microscope at 100× magnification to document the morphological characteristics of epidermal fragments.
For fecal analysis, four replicate slides were prepared per sample. Plant taxa were identified based on the cuticular structures observed across 10 non-overlapping microscopic (Leica, Wetzlar, Germany) fields per slide. Identifiable plant species and their occurrence frequencies were recorded and quantified following established protocols [29]. To minimize observer bias, the identification of plant fragments from microscopic slides was performed independently by two trained researchers.

2.6. High-Throughput Sequencing Analysis of Diet

The V4 hypervariable region of the 18S rRNA gene was amplified with universal eukaryotic primers TAReuk454FWD1 (5′-CCAGCASCYGCGGTAATTCC-3′) and TAReukREV3 (5′-ACTTTCGTTCTTGATYRA-3′). The PCR protocol comprised an initial denaturation at 94 °C for 3 min, followed by 27 cycles of 94 °C for 30 s, 55 °C for 30 s, and 72 °C for 30 s, with a final extension at 72 °C for 5 min. Amplicons were purified using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA) and sequenced on an Illumina MiSeq platform by Majorbio Bio-Pharm Technology Co., Ltd. (Shanghai, China).
Raw sequencing reads were preprocessed to exclude host-derived sequences (oriental storks), as well as those originating from fungi and parasites. Operational taxonomic units (OTUs) were clustered at a 97% sequence similarity threshold using the UPARSE algorithm, and chimeric sequences were detected and eliminated via the UCHIME tool. Taxonomic assignment was performed using the RDP classifier against the SILVA 138/18S eukaryota database and the NCBI non-redundant nucleotide database. Rarefaction curves for α-diversity assessment were constructed using mothur (version v.1.30.2 https://mothur.org/wiki/calculators/ (accessed on 8 December 2025)).

2.7. Statistical Analysis

A dedicated statistical analysis was performed to examine the relationships between diet, microbiota, and habitat variables. Associations between specific dietary items and microbial taxa (at the phylum and genus levels) were evaluated using Spearman’s rank correlation [29], chosen for its robustness to non-normal data distributions and ability to detect monotonic trends. All analyses were conducted by Origin 2022 with the significance level as follows: * 0.01 < p ≤ 0.05, ** 0.001< p ≤ 0.01, and *** p ≤ 0.001.

3. Results

3.1. Gut Microbiota Diversity and Function Prediction Analysis

3.1.1. Species Composition

Bioinformatic analysis of total fecal samples generated 586,743 quality-filtered reads (mean length: 409 bp) following Illumina MiSeq. Operational taxonomic units (OTUs) clustered at a 97% sequence similarity threshold resulted in the identification of 701 microbial OTUs, among which five were shared across all samples (Figure 1a). Venn diagrams illustrating shared and unique taxa across additional taxonomic ranks (phylum to species) were presented in Figure S1. Venn analysis revealed limited shared microbiota across the 11 samples, indicating substantial inter-individual variation. Taxonomic assignment identified 24 phyla, 68 classes, 136 orders, 222 families, 357 genera, and 488 species. Rarefaction curves for observed species (Sobs) and Good’s coverage indices reached a plateau at sufficient sequencing depth (Figure 1b,c), with Good’s coverage values exceeding 99%. Rank-abundance distributions displayed gentle slopes (Figure 1d), indicating high community evenness. Notably, samples Y4 and Y5 exhibited broader curve distributions, reflecting higher microbial species richness. Collectively, these results confirm that the sequencing data reached sufficient saturation, indicating that the current sample size is adequate for subsequent bioinformatics analysis and statistical evaluation.
At the phylum level, the top ten phyla included Firmicutes (64.62%), Fusobacteriota (24.08%), Bacteroidota (4.10%), Campilobacterota (3.62%), Actinobacteriota (1.44%), Cyanobacteria (0.95%), Proteobacteria (0.63%), Chloroflexi (0.19%), Desulfobacterota (0.15%), and Patescibacteria (0.08%) (Figure 2a). At the genus level, the top ten genera included Clostridium_sensu_stricto_1 (21.93%), Paeniclostridium (14.21%), Fusobacterium (12.58%), Cetobacterium (11.49%), Catellicoccus (10.15%), Clostridium_sensu_stricto_13 (6.26%), Lactobacillus (5.12%), norank_f__Barnesiellaceae (3.45%), Helicobacter (3.13%), and Peptostreptococcus (1.64%) (Figure 2b). The relative abundances of bacterial classes, orders, and families were shown in Figure S2.

3.1.2. Alpha Diversity Analysis

Analysis of gut microbiota richness via Ace and Chao indices revealed that samples Y4 (Ace: 385.04; Chao: 381.69) and Y5 (Ace: 393.81; Chao: 381.75) exhibited the highest microbial composition richness; analysis of gut microbiota diversity using Simpson and Shannon indices revealed that samples Y4 (Simpson: 0.20; Shannon: 2.26) and Y10 (Simpson: 0.23; Shannon: 2.13) exhibited the highest microbial diversity (Table 1).

3.1.3. Function Prediction and Pathogenicity Prediction

Functional prediction of gut microbiota via PICRUSt2 identified 46 KEGG level-2 pathways, with high-abundance functions including global and overview maps, carbohydrate metabolism, amino acid metabolism, metabolism of cofactors and vitamins, and membrane transport (Figure 3a). Functional predictions across additional hierarchical levels were collectively demonstrated in Figure S3. Based on bioinformatics analysis, the top five bacterial phyla and genera contributing to potential pathogenicity were predicted. At the phylum level, the highest relative abundance of pathogenic bacteria occurred in Firmicutes (27.24%), Bacteroidota (4.82%), and Proteobacteria (0.89%) (Figure 3b). At the genus level, the highest pathogenic potential was detected in Clostridium_sensu_stricto_1 (21.52%), Clostridium_sensu_stricto_13 (4.25%), and norank_f__ Barnesiellaceae (4.21%) (Figure 3c).

3.2. Dietary Composition Analysis

3.2.1. Fecal Dietary Microscopy

Microscopic analysis of the diet of oriental storks via fecal samples identified six plant species, belonging to three families and six genera, through comparative histological analysis with reference flora (Table 2). Among them, Cynanchum chinense accounted for 43.90% of the total dietary composition.

3.2.2. Comparison of Microscopic Examination Results

High-throughput sequencing revealed dominant species during the overwintering period include Dinophyceae (24.68%), Trebouxiophyceae (22.48%), Chlorophyceae (13.09%), Thecofilosea (8.75%), and Mammalia (4.97%) (Figure 4). Plant composition analysis showed Cannabaceae (76.00%) and Malvaceae (10.00%) predominating (Table 2). Both methods detected Poaceae, Cannabaceae, and Apocynaceae with abundance discrepancies: microscopy (Poaceae 19.52%, Cannabaceae 3.66%, Apocynaceae 43.90%) vs. sequencing (Poaceae 8.00%, Cannabaceae 76.00%, Apocynaceae 4.00%); sequencing uniquely identified Malvaceae and Leguminosae. Relative abundances of dietary taxa at the kingdom and phylum levels were visualized in Figure S4.
Spearman’s correlation analysis between the top five microbial phyla/genera and plant/animal dietary families demonstrated significant negative correlation between Cyprinidae consumption and Bacteroidota abundance (0.01 < p ≤ 0.05) (Figure 5). The potential correlations between the top three food items (family level) and the top three bacterial phyla and genera were shown in Figure S5.

4. Discussion

In this study, 16S rRNA high-throughput sequencing revealed that Firmicutes, Fusobacteriota, and Bacteroidota represented the three dominant bacterial phyla in the gut microbiota of overwintering Ciconia boyciana. Comparative analysis demonstrated a consistent enrichment pattern of Firmicutes across multiple avian taxa, including Gruiformes (Fulica atra [30]; Grus nigricollis [31]; Grus monacha [32]), Charadriiformes (Larus relictus [33]), and Anseriformes (Cygnus cygnus [34]; Anser fabalis [35]). This cross-taxonomic consistency suggests that Firmicutes may play a pivotal role in the cold adaptation of waterbirds by regulating energy metabolism. Birds experience significantly elevated energy expenditure in cold environments [36,37], and gut microbiota have been identified as a critical complementary mechanism for overwintering adaptation, alongside traditional strategies such as behavioral adjustments [38], plumage modifications [39], metabolic intensification [40], and thermoregulation [41,42]. Specifically, Firmicutes possesses the functional capacity to degrade polysaccharides, lipids, and carbohydrates [43,44], thereby providing essential energy supply for the host, an observation supported by previous studies showing increased abundance of this phylum in Arborophila rufipectus [45].
Notwithstanding this convergence, significant divergence exists: Ciconia boyciana and Fulica atra [30] exhibit Fusobacteriota enrichment, which produces immunomodulatory short-chain fatty acids for fat accumulation and immune enhancement [46,47], crucial for thermogenesis and energy storage. Additionally, Ciconia boyciana and Cygnus cygnus [34] share Bacteroidota dominance for digesting complex polymers [48], whereas Grus nigricollis [31], Grus monacha [32], Larus relictus [33], and Anser fabalis [35] display Proteobacteria/Actinobacteriota enrichment. Collectively, waterbirds employ differential microbial consortia to meet energy, thermoregulation, and immune demands during overwintering.
Inter-species divergence in dominant avian gut phyla arises not only from genetic factors [30,48] but also habitat heterogeneity [49] and behavioral ecology [47]. Microbial transmission from prey to predator [50] drives dietary-dependent microbiota variation. Environmental microbiota further shape gut communities, as evidenced by stopover-site water microbiota altering shorebird gut composition [51], implying intra-individual shifts across environments. Discrepancies were observed when comparing our findings with previous research. Wu et al. reported Firmicutes, Proteobacteria, and Actinobacteriota as the dominant phyla in Ciconia boyciana at the Qilihai Wetland in Tianjin [23]. Key factors contributing to these differences include variations in study sites and sampling months (December in Wu et al.’s study versus January in this study [23]). Notably, our data revealed a significantly higher Firmicutes/Bacteroidota (F/B) ratio compared to the prior work. Given that elevated F/B ratios enhance energy harvest efficiency from limited food resources [52,53], this shift likely represents an adaptive strategy for Ciconia boyciana to survive the harsher January conditions via optimized energy extraction. Collectively, these results indicate that restructuring of gut microbial phyla reflects a strategic adaptation of oriental storks to extreme overwintering environments.
At the genus level, Clostridium_sensu_stricto_1 and Clostridium_sensu_stricto_13 could produce short-chain fatty acids, providing rapid energy in cold conditions. These two genera and Lactobacillus reduce intestinal inflammation [54,55,56], crucially maintaining gut immune homeostasis. Additionally, Lactobacillus activates thermogenic signaling under cold stress, promoting brown adipose thermogenesis [57]. Catellicoccus is dominant in multiple waterbirds (Larus relictus [33]; Grus leucogeranus [58]; Grus grus [58]), with the species contributing to nutrient transport and bile acid hydrolysis [59]. This indicates gut microbes enhance winter adaptation in Ciconia boyciana through improved energy metabolism, thermogenesis, immunity, and nutrient conversion. Paeniclostridium and Peptostreptococcus are conditionally pathogenic [60,61], as are Fusobacterium (phylum Fusobacteriota [62] and Helicobacter (phylum Proteobacteria [63]). Cetobacterium (Fusobacteriota) enhances carbohydrate utilization in fish [64]; however, its ecological role in avian hosts remains unclear, and it may be acquired through piscivory.
Compared to plant-derived foods, fish provide higher levels of protein and lipids. Studies have demonstrated that dietary lipids and animal protein can reduce the abundance of Bacteroidota in the gut microbiota [65,66]. This suppression is primarily mediated through two interrelated pathways. First, dietary lipids stimulate bile acid production, and the resulting deoxycholic acid exhibits bactericidal effects specifically against Bacteroidota [67]. Second, a diet richer in animal protein typically contains less fiber, which limits the metabolic substrates available to Bacteroidota. Although this bacterial group can produce short-chain fatty acids from carbohydrates and amino acids, increased animal protein intake reduces fiber availability, thereby constraining their growth and function [65]. Supporting this mechanistic understanding, Zhu et al. documented lower Bacteroidota abundance in fish-fed mice compared to plant-fed controls [65]—a pattern also observed across bird species, where piscivorous waterbirds generally harbor lower Bacteroidota levels than herbivorous arboreal birds [47]. This divergence appears to be driven more by dietary animal protein content than by broad interspecific differences. Consequently, the significant negative correlation observed between Cyprinidae fish consumption and Bacteroidota abundance in our study is likely attributable to the higher lipid and protein composition of fish in the diet.
Previous dietary studies of overwintering Ciconia boyciana have concentrated on the middle-lower Yangtze River floodplain (e.g., Jiangxi Province [25]), where Cyprinidae fish were detected field observation [25]. Despite variations in methodology and study location, a consistent dietary profile emerges, indicating a stable winter foraging strategy for the species. Like many birds, Ciconia boyciana exhibits dietary plasticity and adjusts its prey selection in response to local resource availability [68]. Ciconia boyciana primarily feeds on fish, yet during winter faces food resource scarcity, increased difficulty in fish acquisition, and decline in natural plant resources. This species exhibits significant dietary plasticity, prioritizing consumption of natural vegetation while opportunistically utilizing residual crops and Muridae species as supplementary food sources.
Microscopic analysis identified plant remains from Poaceae (19.52%), Cannabaceae (3.66%), and Apocynaceae (43.90%), representing three families, six genera, and six species, alongside unclassified fish scales. In contrast, 18S rRNA sequencing revealed greater dietary diversity, detecting plant-derived foods (Poaceae 8.00%, Malvaceae 10.00%, Cannabaceae 76.00%, Leguminosae 2.00%, Apocynaceae 4.00%) and animal-derived foods (Cyprinidae and Muridae). This enhanced detection power of molecular methods aligns with findings in other bird species such as Larus relictus [15] and Phalacrocorax carbo sinensis [69]. Discrepancies in the identified plant taxa and their estimated proportions between the two methods are noteworthy. Microscopy relies on the morphological recognition of undigested fragments, where differential digestibility of plant tissues can skew the observed proportions relative to actual dietary intake [70]. Thus, the higher relative abundance of Apocynaceae and Poaceae observed under the microscope likely reflects a digestibility bias, although the specific digestion efficiency of Ciconia boyciana for these plant families remains unquantified. Therefore, the observed diet–microbiota relationships are presented as a case study for this species and habitat, and their applicability to other species or populations requires further investigation. When high digestibility reduces plant material to microscopic or non-morphologically distinct fragments, visual identification fails. 18S rRNA sequencing overcomes this limitation, as 18S rRNA-based analysis can accurately identify plant material even when it is highly fragmented or partially degraded via specific gene sequences, which likely explains the methodological divergence observed. The dietary data, obtained from 11 fecal samples, reflect the short-term food intake of Ciconia boyciana in a non-traditional wintering site. Although this sample size does not allow for definitive characterization of the population’s long-term foraging strategy, it reliably demonstrates considerable individual variation in dietary composition within the sampled group. The high quality of these samples supports their use in establishing preliminary dietary baselines and in generating hypotheses concerning diet–microbiota interactions. This study provides the first integrated profile of diet and gut microbiota for an overwintering aggregation of oriental storks in a non-traditional habitat. However, several limitations should be considered. First, the spatial and temporal scope is constrained, as sampling was conducted at a single site during one winter period, following the trajectory of a single tracked individual. Expanding monitoring to multiple sites and across seasons will be essential to understand broader ecological patterns and seasonal dynamics. Second, our analysis primarily describes correlations between dietary intake and microbial community structure; the functional roles of key bacterial taxa in mediating the host’s adaptation to novel resources remain to be validated through metagenomic or culture-based approaches. Finally, while we focused on flock-level patterns, individual factors such as age, health status, and genetic variation may further shape diet–microbiota interactions and were not addressed here. Future studies incorporating these variables will enhance the mechanistic understanding and conservation applicability of diet–microbiota research in wild migratory birds.

5. Conclusions

This study used 16S rRNA high-throughput sequencing to characterize the gut microbiota of overwintering oriental storks. The microbial community was dominated by the phyla Firmicutes, Fusobacteriota, and Bacteroidota. At the genus level, taxa within Firmicutes were particularly predominant, a pattern that may reflect physiological adaptations to winter stressors. Dietary analysis was performed by combining microscopic examination with 18S rDNA sequencing. However, discrepancies in plant species composition and relative proportions were observed between the two methods. Due to primer limitations, taxonomic resolution of dietary items reached only the family level for both plant and animal prey. Future studies should optimize DNA extraction protocols and employ taxon-specific primers to improve species-level identification. Moreover, the current microscopic protocol detected only plant tissues; methodological improvements are needed to recover animal-derived dietary components. Each approach carries inherent biases, so integrating multiple methods will reduce uncertainty in future dietary assessments. Sampling was limited to 11 fecal specimens; expanding sample sizes in subsequent research will improve the resolution of diet–microbiota dynamics. Strategic food supplementation during critical winter periods could further support the survival of this threatened population.
In summary, this work provides the first integrated profile of diet and gut microbiota in a non-traditional overwintering population of Ciconia boyciana, demonstrating how microbial community structure is linked to both host phylogeny and local dietary resources. These findings offer a physiological and ecological basis for evidence-based conservation of this endangered species under changing winter habitat conditions.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/d18020064/s1. Figure S1. Venn analysis: Venn diagram of oriental storks at phylum level (a), class level (b), order level (c), family level (d), genus level (e), and species level (f). Figure S2. The relative abundance of top 10 classes (a), orders (b), and families (c). Figure S3. Function prediction: (a) KEGG pathway distribution at Level 1 categories, (b) KEGG pathway distribution at Level 3 categories, and (c) COG function classification. Figure S4. High-throughput sequencing technology of the 18S rRNA at kingdom-level (a) and phylum-level (b). Figure S5. Fitted curves showing potential correlations between the top three food items (family level) and the top three bacterial phyla and genera.

Author Contributions

Formal analysis, Y.Z., M.S. and H.W.; Funding acquisition, H.W. and D.Z.; Investigation, Y.Z., M.S. and H.W.; Methodology, Y.Z., M.S., H.W. and D.Z.; Resources, Y.Z., M.S. and D.Z.; Software, Y.Z. and M.S.; Supervision, D.Z.; Writing—original draft, Y.Z., H.W. and D.Z.; Writing—review and editing, Y.Z., M.S., H.W. and D.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This work is funded by Tianjin Bureau of Planning and Natural Resources, Tianjin Enterprise Science and Technology Commissioner Program (24YDTPJC00210), and Tianjin Municipal Education Commission Scientific Research Program (2023KJ183).

Institutional Review Board Statement

The research did not involve any animal experiments, and was carried out in respect of the law.

Data Availability Statement

The datasets for this study can be found in the NCBI Sequence Read Archive (SRA) under accession numbers PRJNA1378443 and PRJNA1378493.

Acknowledgments

We thank the editors and reviewers of Diversity for their professional suggestions to improving the manuscript.

Conflicts of Interest

The authors declare no conflicts of interest.

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Diversity 18 00064 g001
Figure 1. Microbial community diversity and composition in fecal samples of overwintering oriental storks. (a) Venn diagram showing the number of shared and unique operational taxonomic units (OTUs) across samples. (b) Good’s coverage curve demonstrating high sequencing completeness across samples. (c) Rarefaction curve (Sobs index) illustrating the sufficiency of sequencing depth for capturing microbial diversity. (d) Rank-abundance distribution reflecting community evenness and dominance structure.
Figure 1. Microbial community diversity and composition in fecal samples of overwintering oriental storks. (a) Venn diagram showing the number of shared and unique operational taxonomic units (OTUs) across samples. (b) Good’s coverage curve demonstrating high sequencing completeness across samples. (c) Rarefaction curve (Sobs index) illustrating the sufficiency of sequencing depth for capturing microbial diversity. (d) Rank-abundance distribution reflecting community evenness and dominance structure.
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Figure 2. Taxonomic composition of the gut microbiota in overwintering oriental storks. (a) Relative abundance of bacterial communities at the phylum level. (b) Relative abundance of bacterial communities at the genus level (top 10 genera shown).
Figure 2. Taxonomic composition of the gut microbiota in overwintering oriental storks. (a) Relative abundance of bacterial communities at the phylum level. (b) Relative abundance of bacterial communities at the genus level (top 10 genera shown).
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Figure 3. Functional profile and pathogenic potential of the gut microbiota in overwintering oriental storks. (a) Predicted metagenomic functions based on 16S rRNA data using PICRUSt2, categorized by KEGG pathway level 2. (b) Relative contribution of bacterial phyla to predicted pathogenic potential. (c) Relative contribution of bacterial genera to predicted pathogenic potential.
Figure 3. Functional profile and pathogenic potential of the gut microbiota in overwintering oriental storks. (a) Predicted metagenomic functions based on 16S rRNA data using PICRUSt2, categorized by KEGG pathway level 2. (b) Relative contribution of bacterial phyla to predicted pathogenic potential. (c) Relative contribution of bacterial genera to predicted pathogenic potential.
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Figure 4. Class-level relative abundance of dietary in overwintering oriental storks via high-throughput sequencing.
Figure 4. Class-level relative abundance of dietary in overwintering oriental storks via high-throughput sequencing.
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Figure 5. Relationship between dietary composition and gut microbiota in oriental storks. * indicates a significant difference (0.01 < p ≤ 0.05).
Figure 5. Relationship between dietary composition and gut microbiota in oriental storks. * indicates a significant difference (0.01 < p ≤ 0.05).
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Table 1. Analysis of α-diversity in the gut microbiota of oriental storks.
Table 1. Analysis of α-diversity in the gut microbiota of oriental storks.
SampleSobsAceChaoSimpsonShannonCoverage
Y199.00128.75130.000.371.230.9993
Y2142.00195.23193.040.271.700.9988
Y350.0055.2755.250.421.320.9998
Y4359.00385.04381.690.202.260.9988
Y5364.00393.81381.750.401.910.9988
Y6148.00197.47202.470.730.870.9989
Y7145.00169.88162.100.501.150.9993
Y8157.00181.55175.370.531.330.9993
Y9121.00131.41127.950.720.830.9996
Y10165.00171.26168.890.232.130.9997
Y11127.00151.50146.890.461.470.9993
Table 2. Overwintering diet composition based on microscopy and high-throughput sequencing.
Table 2. Overwintering diet composition based on microscopy and high-throughput sequencing.
PhylumClassOrderFamilyDetected MethodSpecies NameDetected Proportion (%)
AngiospermaeMagnoliopsidaPoalesPoaceaeMicroscopySetaria viridis3.66
Echinochloa crusgalli8.54
Digitaria sanguinalis3.66
Zea mays3.66
High-throughput sequencing/0.49/8.00
GentianalesApocynaceaeMicroscopyCynanchum chinense43.90
High-throughput sequencing/0.25/4.00
MalvalesMalvaceaeHigh-throughput sequencing/0.62/10.00
FabalesLeguminosaeHigh-throughput sequencing/0.12/2.00
DicotyledoneaeUrticalesCannabaceaeMicroscopyHumulus scandens3.66
High-throughput sequencing/4.69/76.00
ChordataActinopterygiiCypriniformesCyprinidaeHigh-throughput sequencing/44.20/47.11
MammaliaRodentiaMuridaeHigh-throughput sequencing/49.63/52.89 1
1 Microscopy plant proportions represent identifiable plants/total plant matter (including unidentifiable fragments), whereas high-throughput sequencing proportions before ‘/’ indicate plant- or animal-derived foods/total food items, and after ‘/’ represent specific plant food/total plant food or specific animal food/total animal food.
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Zhou, Y.; Sun, M.; Wu, H.; Zhao, D. First Record on Diet and Intestinal Microbiota of Oriental Storks in Non-Traditional Overwintering Habitats. Diversity 2026, 18, 64. https://doi.org/10.3390/d18020064

AMA Style

Zhou Y, Sun M, Wu H, Zhao D. First Record on Diet and Intestinal Microbiota of Oriental Storks in Non-Traditional Overwintering Habitats. Diversity. 2026; 18(2):64. https://doi.org/10.3390/d18020064

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Zhou, Yifan, Menglin Sun, Hong Wu, and Dapeng Zhao. 2026. "First Record on Diet and Intestinal Microbiota of Oriental Storks in Non-Traditional Overwintering Habitats" Diversity 18, no. 2: 64. https://doi.org/10.3390/d18020064

APA Style

Zhou, Y., Sun, M., Wu, H., & Zhao, D. (2026). First Record on Diet and Intestinal Microbiota of Oriental Storks in Non-Traditional Overwintering Habitats. Diversity, 18(2), 64. https://doi.org/10.3390/d18020064

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