Comparison of fecal microbiota of Little Egret (Egretta garzetta) inhabiting coastal and inland environment

Comparison of fecal microbiota of Little Egret (Egretta garzetta) inhabiting coastal and inland environment | 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 Comparison of fecal microbiota of Little Egret (Egretta garzetta) inhabiting coastal and inland environment Minghui Zhang, Yaoyin He, Gang Yang, Zhou Lu, Lijiang Yu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4026154/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 Background The digestive tract of birds contains diverse microbiota that are essential for their health and survival, and these gut microbiota are influenced by a variety of factors. To compare the diversity in fecal microbiota of the same bird species in different environments, this paper collected feces of Little Egret ( Egretta garzetta ) and environmental (soil and water) samples from coastal salt water and inland freshwater areas to explore how fecal microbiota responds to different environments using 16S rRNA high-throughput sequencing methods. Results The main fecal microbiota from two sampling sites were similar at the phylum level. In terms of genus level, the dominant genera in feces from salt-water habitats (SWF) were Escherichia-Shigella (14.448%), Enterococcus (10.064%), Vibrio (7.812%), whereas there were Sporosarcina (18.241%), Citrobacter (11.987%), Acinetobacter (6.201%), Kurthia (5.725%) in feces from freshwater habitats (FWF). A few ASVs were sharedamong the egrets and the environmental samples in the two regions. The fecal microbiota between the two sampling sites showed no significant differences in α-diversity and were significant in β-diversity; there were no significant differences between the environmental microbiota diversity of the two sites. In contrast, most of the parameters reflecting α-diversity and β-diversity were shown to be significantly different between the fecal and environmental microbiota. In composition of microbiota function, the fecal microbiota of little egret and the environment microbiota were similar in relative abundances in the proportion of kegg level 1 functional pathways. But there were significant differences in some level 2 and level 3 functional pathways between the egrets from two habitats. Moreover, a portion of ASVs classified as opportunistic pathogens were detected in the feces of the egrets. Conclusions The results of this study indicate that there are differences in the fecal microbiota of the little egrets in different environments, and the differences are less affected by soil and water. Considering that Vibrio which was more common in the guts of marine fish, it is hypothesized that regional differences in fecal microbiota composition are related to changes in food types. little egret environment fecal microbiota pathogenic bacteria 16S rRNA Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background The digestive tract of animals contains microbiota that consist of specific groups of bacteria with different abundances and functional characteristics (Jovel et al., 2016 ). Vertebrate microbiota are important in nutrition acquisition (Stanley et al., 2012 ), gut development (Rahimi et al., 2009 ), and regulation of host physiology (Meinl et al., 2009 ). Thus, understanding the diversity and function of the animal gut microbiota is essential and the study remains a relatively popular area. Integrating the animal microbiota into animal husbandry and wildlife conservation will be a difficult but rewarding direction for the future. With the accelerating decline of biodiversity, the burgeoning study of wildlife microbiotas can be of great assistance in the maintenance of endangered species (Dallas et al., 2023; Waite et al., 2014). Birds are widely distributed across the globe and contain a variety of microbiota in the digestive tract in different species like other vertebrates (Waite et al., 2014). Current studies have shown that the dominant microbiota in most avian gut consists mainly of Firmicutes and Proteobacteria, followed by Fusobacteria, Bacteriodetes, and Actinobacteria (van Veelen et al., 2018 ). Nowadays, the interest of study in gut microbiota of wild birds is increasing because the microbiota can be a source of human and many animal diseases through direct transmission or other ways (Tsiodras et al., 2008 ). In addition, birds can also act as vectors for long-distance pathogen transmission, and their fecal contamination has been well studied and confirmed (Ahmed et al., 2016 ). Therefore, Hird ( 2017 ) advised that we need to study their microbiota to gain a deeper understanding of the impact of birds on humans and other animals. The gut microbiota of birds is usually established immediately after birth and it is altered by various factors (Sommer et al., 2013). One important influenced factors is host phylogeny (Waite et al., 2014). A basic mechanism for host-specific gut microbiota is the formation of similar gut microbiota through parental vertical transfer (Ferretti et al., 2018 ). Second, diet has been identified as the main cause of differences in gut microbiota in most of the current studies, and the importance of gut microbiota for digestion has been recognized (Grond et al., 2018 ). Microbiota ingested with food may be one of the main pathways for microbial recruitment and establishment in the intestine of birds (Grond et al., 2017 ). Different diet can influence the composition of the gut microbiota (Liu et al., 2022 ). It had been estimated that wild woodrats ( Neotoma lepida ) and anole lizards ( Phymaturus Williams ) acquired 25% and 47% of their gut microbiota from ingested plant foods, respectively (Kohl et al., 2017 ; Kohl et al., 2014). Halpern and Senderovich ( 2015 ) had also proposed a concept of relation with predator and prey: some microbiota of the prey colonized the gut of the predator and become an part of the predator's gut microbiota. The study of Laviad-Shitrit et al. ( 2017 ) had also further demonstrated that the great cormorants ( Phalacrocorax carbo ) was infected Vibrio cholerae after consuming tilapias ( Oreochromis niloticus X Oreochromis aureus ) carrying pathogenic bacteria. As wildlife’s survival habitat, the environment also has an impact on the gut microbiota of animals. It had been shown that the urbanization had dramatically altered the gut bacterial communities of urban birds (Teyssier et al., 2018 ). Different gut microbiota had been found in the same species living in different areas of the city (Berlow et al., 2021 ). For migratory birds that constantly changed stopover sites during migration, there was also considerable evidence of variability in the gut microbiota of birds at different migratory stopovers (Hird, 2017 ). Gut microbiota of birds from different habitats with similar affinities can adjust quickly and show convergence after migrating to the same stopover site (Lewis et al., 2017 ). The gut microbiota of immigrant and native Silver-eared Mesia ( Leiothrix argentauris ) living in heavy-metal-contaminated mining environments did not differ significantly in composition, diversity, or function and evolved heavy-metal-contamination damage adaptations with a very high proportion of pathogenic bacteria (Zhou et al., 2023 ). Interestingly, there is no shortage of studies on birds migrating from the sea to inland, or from inland to the sea, but few articles have been dedicated to investigate the difference of the gut microbiota of the same species that lives in coastal and inland area. Currently, different environments are inconclusive on how to shape the avian gut microbiota. One suggestion is that microbiota may change when exposed to new bacteria environment (Risely et al., 2017 ). Birds are exposed to local environment (including water, soil, and nesting environments) after birth, and the microbes in these habitats then become the potential gut microbiota in birds (Grond et al., 2018 ). Environmental factors had been demonstrated to have influence on the gut microbiota of passerine birds, especially in newborns that have not yet developed a stable gut microbiota and whose gut microbiota structure is more susceptible to environmental factors (Hird et al., 2014 ). In addition, microbes from soil can successfully colonize the gut of sterile mice and even exceed the core gut microbiota in abundance (Seedorf et al., 2014 ). For mature wild hosts, it had also been shown to be possible to obtain considerable amounts of microbes from the environment. For example, a study of cloacal, feather, nest, and body surface microbiota in woodlark ( Lullula arborea ) and eurasian skylark ( Alauda arvensis ) showed that the microbiota in adult were influenced by local environmental microbiota (van Veelen et al., 2018 ). It is unclear whether obtaining microbiota from the environment is a characteristic of all wild hosts, including those with migratory behavior. Therefore, the avian gut microbiota may change when they move to new environments and are exposed to new microbiota. Grond et al. ( 2018 ) stated that to determine the effect of the environment on the gut microbiota of birds, it was necessary to sample both the birds and local environments, including soil sediments and water at foraging or nesting sites. Little egret ( Egretta garzetta ), one of the common wader of Ardeidae, is widespread in Africa, Europe, and Asia. They mainly feed on fish, shrimps, crabs, aquatic insects and living in many environments, such as ponds, marshes, lakes, coastal shallows. These make it an ideal model to study the effects of different environments on the gut microbiota of the same species. It has been experimentally demonstrated that feces produced by birds may contaminate crops, cultured fishs (Laviad-Shitrit et al., 2017 ), waters where humans play (Ryu et al., 2014 ), which would further affecting human production and activities. Little egret also overlaps considerably with human activity areas. Previous studies on the gut microbiota of little egrets are scarce (Laviad-Shitrit et al., 2019 ), so it is necessary to study the composition of their gut microbiota and their interactions with the environment. Therefore, the fecal and environmental samples collected from coastal and inland water were sequenced and analysed by 16S rRNA sequencing technology. We intend to investigate: (1) whether there are differences in the fecal microbiota of the little egret from different areas, (2) the microbial composition of the environmental factors, like water and soil, (3) whether the environmental microbiota influence the fecal microbiota of the little egret? The results of this study would be benefit to answer the question of environmental effects on gut microbiota and to provide reference on the potential risks that the little egrets' fecal microbiota may bring about. Material and method Sample locations We selected two different kinds of habitats for sampling. The first was the seaside salt water (SW) environment, where little egrets use grasslands, salt pans, mudflats, farmlands, and aquaculture ponds as their major activity areas. This environment is rich in marine fish (mainly in Perciformes , Pleuronectiformes , Clupeiformes ) (Zhang et al., 2022 ), mollusks, arthropods, copepods, and other food resources. The second was an inland freshwater environment (FW), where little egrets mainly roost in farmlands, grasslands, and ponds, with a certain number of freshwater fishes, primarily comprising tilapias ( Oreochromis mossambicus ), as well as a small number of amphibians, mollusks, and arthropods, and other food resources. Sample collection Although fecal samples only represent a component of gut microbial composition, they have been widely used as part of a non-invasive sampling method to study the gut microbiota in wild birds (Grond et al., 2018 ). We collected 30 samples, including 12 fecal samples (SWF) of little egrets, 2 saltwater (SWW) samples and 2 saltwater soil (SWS) samples from the saltwater area of the seashore, and 10 fecal samples (FWF) of little egrets, 2 freshwater (FWW) and 2 freshwater soil (FWS) samples from the freshwater areas. Before collecting the fecal samples, we observed and selected the areas where only little egrets were active to ensure only fresh feces were collected. We collected the samples at least 5 m apart to ensure that feces were from different individuals. Feces in contact with the ground were avoided. Afterward, the feces were stored in sterile freezing tubes. Although feces were sampled in the field, all samples were temporarily frozen in ice boxes and subsequently transported to the laboratory and stored at − 40°C until further processing. DNA extraction and PCR amplification The total microbiota DNA was extracted from fecal, soil, and water samples following the instructions provided in the E.Z.N.A.® Soil DNA Kit (Omega Bio-tek, Norcross, GA, U.S.). Microbiota structural studies were conducted via the polymerase chain reaction (PCR) amplification method using primers for the V3–V4 region of the 16s rRNA 341F 5'-CCTAYGGGRBGCASCAG-3' and 806R 5'-GGACTACNNGGGGTATCTAAT-3.' The amplification primers for each sample contained 8-base tag sequences to differentiate the samples. The PCR reaction mix contained 4 µL of 5×FastPfu buffer, 2 µL of 2.5 mM dNTPs, 0.8 µL of 5 µM of each primer, 0.4 µL of FastPfu polymerase, 10 ng of the DNA template, and double-distilled H 2 O in a total volume of 20 µL. The PCR amplification steps were as follows: 95℃ for 5 min; 28 cycles at 95℃ for 30 s, 55℃ for 30 s, 72℃ for 45 s; and final extension at 72°C for 10 min. The amplification products were purified by 2% agarose gel electrophoresis using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, US) according to the manufacturer’s instructions. Library construction and sequencing SMRTbell libraries were constructed from the amplified DNA according to the manufacturer’s instructions (Pacific Biosciences). Each sample was ligated with a specific barcode sequence and mixed in equal mass. The amplicon mixtures were used to construct sequencing libraries using the Pacific Biosciences SMRTbellTM Template Prep kit 1.0 and sequenced on a PacBio Sequel II. All amplicon sequencing was undertaken by Shanghai Lingen Biotechnology (Shanghai, China). The raw data were uploaded to the National Center for Biotechnology Information (NCBI) SRA database. Sequencing data processing PacBio raw reads were processed using the SMRT Link Analysis software version 6.0 to obtain cyclic congruent sequence (CCS) reads: parameters were set to a minimum number of passes (3) and minimum prediction accuracy (0.99). The raw reads were processed through the SMRT Portal to screen for sequence length ( 1500 bp) and quality. Further filtering included removing the barcodes, primer sequences, chimeras, and sequences containing 10 consecutive identical bases. The passed sequences were duplicated and subsequently, the DADA2 algorithm was used to identify the insertion deletions and substitutions (Callahan et al., 2016 ). This was performed on the double-ended data with a maximum of two expected errors per read (maxEE = 2). After splicing the sequences and chimera filtering, each 16S rRNA gene sequence (hereafter referred to as ASV) was analyzed by an RDP Classifier ( http://rdp.cme.msu.edu/ ) against the Silva (SSU132) 16S rRNA database using a confidence threshold of 70% (Amato et al., 2013 ) for phylogenetic relationships. Alpha and beta diversity analyses We used the rarefaction curve analysis based on Mothur v.1.21.1 (Schloss et al., 2009 ) software to reveal the diversity indices including Chao, Shannon diversity, and Pd_faith indices. Beta diversity analyses were performed using the Bray–Curtis and UniFrac (Lozupone et al., 2011 ) distance to compare the community differences. The primary analysis software was R ( https://www.r-project.org/ ). Mantel tests were performed using the vegan package in R to analyze Spearman’s correlations between little egret, water, and soil and bacterial communities similarities in different regions, using the Bray–Curtis distance matrix. Next, a multivariate analysis of variance (MANOVA) was performed to further confirm the observed differences. Spearman’s correlation coefficients were evaluated to determine the relationship between bacterial communities of little egrets, water, and soils in different regions. Correlation heatmaps and network diagrams were visualized using R (heatmap package) and Cytoscape ( http://www.cytoscape.org ). Redundancy analysis (RDA) was conducted to explore the relationship between environmental factors and bacterial communities. An analysis of variance (ANOVA) was used to assess the differences in diversity indices between samples, and differences were considered significant at p < 0.05. LEfSe differences analysis Linear discriminant analysis effect size (LEfSe) (Segata et al., 2011 ) analysis was used to identify the biomarker species distinguishing the two or more biological conditions (or taxa). The non-parametric factorial Kruskal–Wallis (KW) sum-rank test was first used to detect the taxa with significant abundance difference characteristics and identify the taxa differing significantly from the abundance. Finally, LEfSe used the linear discriminant analysis (LDA) to estimate the magnitude of the effect of each component’s (species’) abundance on the differential effect (Ijaz et al., 2018 ). Prediction of microbial gene function The Kyoto Encyclopedia of Genes and Genomes (KEGG) database was used along with the PICRUSt2 ( http://picrust.github.io/picrust/tutorials/genome_prediction.html ) software to predict the functional differences in microbiota in different samples and infer the functional alterations. The obtained ASV data were used to generate the BIOM files, which were formatted and set as an input to PICRUSt2 containing a make.biom script generation that can be used in Mothur. Results Microbiota composition After quality control, 12,060 ASVs were obtained. Rarefaction curves demonstrated that the sample sequencing depth was sufficient to reflect the vast majority of microbiota in each sample (Fig. 1 a). The SWF samples had 26 phyla, 58 classes, 130 orders, 219 families, 396 genera, and 165 species. The dominant phyla were Proteobacteria (49.550%), Firmicutes (37.091%), Bacteroidota (4.903%), and Fusobacteriota (4.811%). The SWS samples had 45 phyla, 75 classes, 174 orders, 201 families, 254 genera, and 30 species, with the dominant phyla being Proteobacteria (46.088%), Bacteroidota (24.205%), Chloroflexi (6.602%), and Desulfobacterota (5.679%). The SWW samples had 26 phyla, 41 classes, 103 orders, 139 families, 173 genera, and 33 species, and the dominant phyla were Proteobacteria (82.500%), Bacteroidota (7.112%), and Actinobacteria (6.293%) (Fig. 1 b). 21 phyla, 36 classes, 101 orders, 160 families, 276 genera, and 154 species were identified in the FWF samples. The dominant phyla were Firmicutes (51.098%), Proteobacteria (45.084%), Fusobacteriota (1.739%), and Bacteroidota (1.05%). FWS samples had 36 phyla, 73 classes, 150 orders, 200 families, 294 genera, and 36 species, and the dominant phyla were Proteobacteria (71.414%), Desulfobacteriota (6.071%), and Bacteroidota (5.346%), whereas the FWW samples had 33 phyla, 65 classes, 134 families, 175 families, 255 genera and 5.05 genera, and the dominant phyla were Proteobacteria (52.130%), Actinobacteriota (19.365%), Cyanobacteria (17.110%), and Bacteroidota (5.772%) (Fig. 1 b). At the genus level, the dominant genera in the SWF samples were Escherichia-Shigella (14.448%), Enterococcus (10.064%), Vibrio (7.812%), Catellicoccus (5.794%), Bradyrhizobium (5.301%), Cetobacterium (4.511%), Vibrionimonas (4.167%), Paraclostridium (4.075%), and Peptoclostridium (4.021%). The higher abundance of Phaeodactylibacter (16.420%) and Limibaculum (7.335%) was detected in the SWS samples. The SWW samples included Bradyrhizobium (31.803%) and Rhodanobacter (11.111%) (Fig. 1 c). The dominant genera in the FWF samples were Sporosarcina (18.241%), Citrobacter (11.987%), Acinetobacter (6.201%), and Kurthia (5.725%). Dechloromonas (13.325%) was high in FWS samples, whereas Rhodanobacter (14.367%), Cyanobium PCC-6307 (8.664%), and Bradyrhizobium (7.485%) were dominant in the FWW samples (Fig. 1 c). Microbiota diversity Analyses of α and β diversity were conducted on the fecal microbiota of little egrets living in different habitats. The differences in Chao1, Shannon, and Pd_faith indices were insignificant ( p > 0.05) between the two habitats (Fig. 2 a-c). In contrast, the results of the PCoA plot based on Weighted_Unifrac distances demonstrated significant differences in the little egret fecal microbiota between the two habitats ( p < 0.01; Fig. 2 d). Next, the microbiota of little egret feces and their habitats were compared to investigate the extent of the influence of the environment. Overall, most differences in α-diversity indices between the microbiota of little egret feces and the environment were significant ( p < 0.05), whereas β-diversity was significantly different ( p < 0.05) in both habitats (Fig. 2 a-d), representing a lesser impact of the environment on the microbiota of little egret feces. Differences in the β-diversity between the environmental samples from SW and FW regions were insignificant ( p > 0.05; Fig. 2 d), possibly representing microbial conservatism between the environments. Differences in microbiota composition For easy comparison, water and soil from the same habitat (SWW and SWS, FWW and FWS) were combined into environmental samples (SWE and FWE) for Venn diagram analysis. Only 26 shared ASVs between the little egrets and environmental samples, 141 shared ASVs between SWF and FWW, 73 shared ASVs between SWF and SWE, and 102 shared ASVs between FWW and FWE (Fig. 3 ). Significant differences existed in the dominant microbiota between little egrets in the two regions, as well as between little egrets and the environment. In terms of contribution of major microbiota differences, FWF microbiota had a significantly greater abundance of Bacillales , Bacilli , Planococcaceae , and Sporosarcina than SWF samples, whereas Alphaproteobacteria , Rhizobiales , and Clostridia were more abundant in SWF samples(Fig. 4 ). Analysis of functional differences in microbiota The microbiota functions in little egret feces and the two environments were similar in the relative abundance of the level 1 pathway–both dominated by metabolism (Fig. 5 a). The following functions were in the order of relative abundance: environmental information processing, cellular processes, genetic information processing, human diseases, and organismal systems. In the level 2 pathway, the global and overview maps, cell motility, replication and repair, folding, sorting and degradation, and metabolism of cofactors and vitamins of FWF were significantly higher than SWF ( p < 0.05), whereas xenobiotic biodegradation and metabolism, cellular community–prokaryotes, metabolism of terpenoids and polyketides, and biosynthesis of other secondary metabolites of SWF were significantly higher ( p < 0.05; Fig. 5 b). In the level 3 pathway, FWF samples were significantly higher in metabolic pathways, bacterial chemotaxis, and ribosomes ( p < 0.05), whereas SWF samples were higher in quorum sensing, microbial metabolism in diverse environments, ABC transporters, and environments ( p < 0.05; Fig. 5 c). Discussion Consistent with the data from previous studies on different bird species, we found that little egret fecal samples from both habitats were dominated by Firmicutes and Proteobacteria. Firmicutes are associated with the dissociation of complex carbohydrates, polysaccharides, glucose, and fatty acids, which are major sources of nutrients for animal hosts (Tap et al., 2009 ). Therefore, it is not surprising that Firmicutes were detected in such a high abundance in the little egret fecal samples. Compared to mammals or poultry, wild birds carry a higher proportion of Proteobacteria bacteria in their intestines. The Proteobacteria consists largely of pathogenic bacteria such as Campylobacter , Escherichia , and Vibrio (Diakou et al., 2016 ; Wallménius et al., 2014 ), an indicator of gut microbiota instability (Shin et al., 2015 ). The detection of a high abundance of Proteobacteria in little egret feces is noteworthy; however, it needs to be further explored as their function in birds is unknown. Despite being in different environments, the fecal microbiota of little egrets at the two sites differed; both had Firmicutes and Proteobacteria as the primary bacterial phyla, suggesting that the fecal microbiota of the little egrets were conserved at a certain extent. Fusobacteriota is a common group in the avian gut microbiota (Waite et al., 2015). In contrast to the results of our study, the relative abundance of Fusobacteriota in the intestinal tract of migratory little egrets in the Israeli region was higher than that of Proteobacteria. It was approximately 40% in the study conducted on the Israeli little egret intestine (Laviad-Shitrit et al., 2019 ), which was considerably more than in this study (less than 5%). This could be related to the difference in the geographic environment or residential type. Israel is located in the Middle East and has a Mediterranean climate, whereas the study area of the present research had a subtropical monsoon climate, with differences in the living environment (Tadmor-Levi et al., 2022 ) and food resources (Goren, 1974 ; Roni et al., 2022 ) between the two places. In addition, migratory or non-migratory status could be another influential factor. The published 16S rRNA gene data on migratory shorebirds demonstrated that most populations of migratory shorebirds that crossed different geographical areas (e.g., Australia, the United States, and the Arctic) typically carried a high proportion of Fusobacteria (average abundance of approximately 20%) (Zhang et al., 2021 ). The little egrets used for the study in the Israeli region were winter migrant birds; however, the little egrets were resident birds in our study, which were observed year-round in the study area. The genus with the highest abundance in the FWF samples was Sporosarcina . The members of this genus in the avian gut have not been comprehensively studied. Only a few species, such as Sporosarcina luteola and Sporosarcina pasteurii , have been detected, and certain studies have demonstrated the abundance of these bacteria in soil and were associated with the mineralization of metal ions in the soil (Li et al., 2024 ). However, bacteria of this genus were absent from the environmental samples, possibly predicting a low environmental influence on the fecal microbiota of little egrets. Next, the genera in abundance were Citrobacter and Acinetobacter , both of which contained opportunistic pathogenic bacteria (Mullineaux-Sanders et al., 2019 ). It could be a risk of infection to humans. Kurthia were associated with the dissociation of compounds (Dong et al., 2018 ) and could be beneficial for little egret digestion. The most abundant genus within the SWF group is Escherichia-Shigella , followed by Enterococcus , Vibrio , and Cetobacterium . Shigella were highly infectious enteric pathogens that invaded and caused deep inflammation and tissue damage in the colorectal epithelium (Carayol et al., 2013). It could prompt persistent inflammation (Small et al., 2013 ), whereas Enterococcus exerts an inhibitory effect on pathogenic bacteria (Hanchi et al., 2018 ), which could be beneficial to little egret survival. Although Vibrio and Cetobacterium were more prevalent in marine fishes (Verner-Jeffreys et al., 2003 ), these were rarely observed in the FWF samples. Therefore, it seemed like the seashore-living little egrets acquired indirectly by ingesting marine fish, which could confirm the predator–prey hypothesis (Halpern et al., 2015). The α and β diversities can reflect the richness, and diversity of microbiota within groups as well as between the groups. The results demonstrated that although the α-diversity of the microbiota of bird feces at the two habitats differed, it was insignificant. PCoA results based on the Unweighted_Unifrac and Weighted_Unifrac distances revealed that the microbiota of the feces of little egrets at the two habitats differed highly significantly ( p < 0.01). This suggested that the fecal microbiota of little egrets at the two sites differed considerably in relative abundance and phylogeny. However, compared with the Unweighted_Unifrac distance, the PCoA diagram obtained through the Weighted_Unifrac distance revealed less inter- and intra-group differences, suggesting that the relative abundance of core microbe in the fecal microbiota of little egrets at the two areas was more conserved and could be related to species phylogeny. The results of fecal microbiota diversity in common kestrel ( Falco tinnunculus ) before and after captivity (Zhang et al., 2022 ) were consistent with that reported in the present study, with non-significant differences in α-diversity and significant differences in β-diversity. Zhang et al. concluded that the captive environment exerted limited effects on the gut microbiota. They speculated that the food could change the abundance of fecal microbiota because of the large changes in the types of food before and after captivity. A study of functional composition revealed that the relative abundance of fecal microbial level 1 pathways was extremely similar between the little egret of the two habitats, with both being dominated by metabolism. It was consistent with the notion that metabolic pathways accounted for the highest proportion (48.35%) of all bacterial functions in the avian gut (Zhu et al., 2018 ), suggesting that the gut microbiota were important in building the host’s metabolic capacity. Little egret feces contain a considerable abundance of pathways associated with human diseases. A variety of birds such as raptors, songbirds, and shorebirds carry a wide range of pathogens (Keller et al., 2011 ; Ksoll et al., 2007 ). For example, we detected a certain abundance of Campylobacter ASVs in both FWF and SWF, whereas Escherichia-Shigella in SWF samples even displayed the highest abundance at the level of genus, and these bacterial genera, as well as Salmonella and others, had been identified as pathogens in humans and birds (Newman et al., 2012 ). These highly abundant pathogens had the potential to increase the risk of contaminating the environment and thus infecting humans. Although the two habitats were separated by a long distance with different water, the environmental microbiota in the two areas did not differ significantly in either α or β diversity, demonstrating a high degree of environmental microbial conservatism. Except for the non-significant difference in α-diversity between little egret feces and environmental microbiota in the SW region, differences in α-diversity between little egret feces and environmental microbiota in the FW region as well as the differences in β-diversity between the two habitats were significant. Previous studies have reported that environmental microbiota could colonize the gut of newborn chicks (Grond et al., 2017 ), whereas the fecal samples collected in the present study originated from adult little egrets, which could be less affected by environmental factors such as soil and water. The microbial sequencing results of water and soil collected from little egret’s foraging areas demonstrated that both soil and water samples were dominated by Proteobacteria. This is similar to the high levels of Proteobacteria in little egrets in both areas. In addition, we had discussed that Proteobacteria contained a higher number of pathogenic bacteria associated with intestinal instability, which could lead to diseases in little egrets. A study of bacterial functional pathways indicated that similar to the fecal microbiota of little egret, the microbiota of water and soil contained a certain abundance of pathways associated with human diseases, such as the detection of ASVs of Vibrio , Escherichia , Pseudomonas , and Campylobacter which had been categorized as opportunistic pathogens (Zhang et al., 2021 ). Little egrets were often found in urban parks, ponds, agricultural fields, and the seashore where humans often play, and the areas where they were active were close to human beings, thus posing a risk of infecting humans through feces. Therefore, direct contact between humans and little egrets should be avoided or reduced in both urban and seaside areas. Moreover, policies should be implemented to control free access to egrets and prohibit their hunting and eating. Conclusion This paper collected the feces of little egret and environmental samples from coastal and inland areas to explore how the fecal microbiota responds to different environments by using 16S rRNA high-throughput sequencing methods. The main fecal microbiota phyla of little egrets from two sampling sites were found to be similar, whereas there was a major difference in genus. The differences in α-diversity were insignificant between the little egrets, but the β-diversity was significant. Most of the parameters reflecting α-diversity and β-diversity were shown to be significantly different between the fecal and environmental microbiota. Since Vibrio, which are more abundant in marine fish than freshwater fish, were dominant within SWF but rare in FWF, with the limited influence of the environment on the fecal microbiota of little egrets, this paper speculates that the differences in the fecal microbiota from different regions may be more related to the food species than the environmental microbiota. In addition, since a portion of ASVs classified as opportunistic pathogens were detected in the little egret feces, it is suggested that the relevant authorities should carry out rational planning and zoning of urban and coastal wetland areas where birds and humans share to ensure their safety. We used non-invasive methods to obtain samples and observe little egrets foraging in the field. However, we did not dissect the gut contents of little egrets at the two sites to analyze the specific food intake of the little egrets. In the future, the differences in food types and the relationship between food and gut microbial differences of little egrets could be analyzed by dissecting the gut contents including food types and microbial composition of food of little egrets in the two areas. Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding The authors declare that no funding, grants, or other support were received during the preparation of this manuscript. Authors' contributions MZ collected samples and analyzed data, and was a major contributor in writing the manuscript. YH helped to search for information. GY, ZL helped with the experiment and sampling process. LY guided the experiment and manuscript writing. All authors read and approved the final manuscript. Acknowledgements Not applicable. Author details 1 Animal Science and Technology College, Guangxi University, Nanning 530004, Guangxi, China. 2. Guangxi Forest Inventory and Planning Institute, Nanning 530011, Guangxi, China. Minhui Zhang: [email protected] . Yaoyin He: [email protected] . Gang Yang: [email protected] . Zhou Lu: [email protected] . Lijiang Yu: [email protected] . References Ahmed, W., Harwood, V. J., Nguyen, K., Young, S., Hamilton, K., & Toze, S. (2016). Utility of Helicobacter spp . associated GFD markers for detecting avian fecal pollution in natural waters of two continents. Water Res, 88 , 613-622. doi:10.1016/j.watres.2015.10.050 Amato, K. R., Yeoman, C. J., Kent, A., Righini, N., Carbonero, F., Estrada, A., Gaskins, H. R., Stumpf, R. 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Adaptive evolution to a high purine and fat diet of carnivorans revealed by gut microbiomes and host genomes. Environ Microbiol, 20 (5), 1711-1722. doi:10.1111/1462-2920.14096 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4026154","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":278833820,"identity":"118641dd-a992-4685-bcb0-8ba1e9e0b9dc","order_by":0,"name":"Minghui Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyUlEQVRIiWNgGAWjYBAC+/PNBw7//Wcjx8beQKyeG8cSH/CwpRnz8xwgVsuBHGUDHrbDiTNnJBCpg7HhDJuEBM/hxA03H2+8wVBjE01QCzNz7zEJA4l04w2304otGI6l5TYQ0sLGcC5NIsHAWnbD7RwzCcaGw4S18DAAVR5IYGbccPMMkVokGHKMDRsOOCvOnMFDpBYDiWOJjxkbQIEM9EsCMX4x4AdGJWMDKCoPb7zxocaGsBZUGxNIUQ7RQqqOUTAKRsEoGBkAAMAhQSrfbFXiAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0002-7775-9624","institution":"Guangxi University College of Animal Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Minghui","middleName":"","lastName":"Zhang","suffix":""},{"id":278833821,"identity":"360f66c6-9ce3-4766-b6c6-f8b6c3a012d4","order_by":1,"name":"Yaoyin He","email":"","orcid":"","institution":"Guangxi University College of Animal Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Yaoyin","middleName":"","lastName":"He","suffix":""},{"id":278833822,"identity":"dbec435e-66e9-4350-9960-81fbb686b075","order_by":2,"name":"Gang Yang","email":"","orcid":"","institution":"Guangxi Forest Inventory and Planning Institute","correspondingAuthor":false,"prefix":"","firstName":"Gang","middleName":"","lastName":"Yang","suffix":""},{"id":278833823,"identity":"7ae2375f-d1e3-4403-9e1f-463f96bb9941","order_by":3,"name":"Zhou Lu","email":"","orcid":"","institution":"Guangxi University College of Animal Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Zhou","middleName":"","lastName":"Lu","suffix":""},{"id":278833824,"identity":"c0549413-6ded-4f8b-a4e0-81e1fae53d3a","order_by":4,"name":"Lijiang Yu","email":"","orcid":"https://orcid.org/0000-0001-7531-3843","institution":"Guangxi University College of Animal Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Lijiang","middleName":"","lastName":"Yu","suffix":""}],"badges":[],"createdAt":"2024-03-07 14:34:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4026154/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4026154/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52714406,"identity":"f085c59f-3510-440d-8daa-2aa9fc1959c0","added_by":"auto","created_at":"2024-03-14 21:02:18","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":175196,"visible":true,"origin":"","legend":"\u003cp\u003eRarefaction curves and relative abundance among samples. \u003cstrong\u003ea\u003c/strong\u003e Sample rarefaction curves. Relative abundance at the phylum level (\u003cstrong\u003eb\u003c/strong\u003e) and the genus level (\u003cstrong\u003ec\u003c/strong\u003e) among six groups.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4026154/v1/63dbffc104e8bb95f02521d8.png"},{"id":52714402,"identity":"c5fbf555-b117-45f8-97e3-42b8bc72049b","added_by":"auto","created_at":"2024-03-14 21:02:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":112626,"visible":true,"origin":"","legend":"\u003cp\u003eDiversity index of six groups. The letters on barplot represent significance between groups.\u003cstrong\u003e a\u003c/strong\u003e Chao1 index. \u003cstrong\u003eb\u003c/strong\u003e Shannon index. \u003cstrong\u003ec \u003c/strong\u003ePd_faith index. \u003cstrong\u003ed \u003c/strong\u003ePCoA based on Weighted-Unifrac distances.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4026154/v1/ed9dd7f9244c392c53b1622a.png"},{"id":52714403,"identity":"f9f28d99-4efd-4f3d-aae3-c9a79c61e437","added_by":"auto","created_at":"2024-03-14 21:02:18","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":87443,"visible":true,"origin":"","legend":"\u003cp\u003eVenn diagram among four groups. The number represents the amount of ASV.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4026154/v1/0c2393684b2c3cfdcbe742a2.png"},{"id":52714501,"identity":"07bf27ca-217b-47f1-9ea4-653ba13fba01","added_by":"auto","created_at":"2024-03-14 21:10:18","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":111635,"visible":true,"origin":"","legend":"\u003cp\u003eLEfSe_LDA of the fecal microbiota of little egrets in two areas.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4026154/v1/a8e84cb437cca2a907d2fb62.png"},{"id":52714405,"identity":"109c88dc-b36e-450e-aefc-29fbe7c8111c","added_by":"auto","created_at":"2024-03-14 21:02:18","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":298159,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of microbial functions between sample groups. \u003cstrong\u003ea\u003c/strong\u003e Relative abundance of kegg level 1 pathway among six groups. Functional difference between microbiota in feces of little egrets in two areas based on kegg level 2 pathway (\u003cstrong\u003eb\u003c/strong\u003e) and kegg level 3 pathway (\u003cstrong\u003ec\u003c/strong\u003e).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4026154/v1/e0fbc80883d885ea238076a9.png"},{"id":57841731,"identity":"6f1dde70-50a7-4d3d-bbb3-5cc4b98530d2","added_by":"auto","created_at":"2024-06-06 10:05:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1343534,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4026154/v1/c159037e-a184-4e35-8124-a5b87e5c9eab.pdf"}],"financialInterests":"","formattedTitle":"Comparison of fecal microbiota of Little Egret (Egretta garzetta) inhabiting coastal and inland environment","fulltext":[{"header":"Background","content":"\u003cp\u003eThe digestive tract of animals contains microbiota that consist of specific groups of bacteria with different abundances and functional characteristics (Jovel et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Vertebrate microbiota are important in nutrition acquisition (Stanley et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), gut development (Rahimi et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), and regulation of host physiology (Meinl et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Thus, understanding the diversity and function of the animal gut microbiota is essential and the study remains a relatively popular area. Integrating the animal microbiota into animal husbandry and wildlife conservation will be a difficult but rewarding direction for the future. With the accelerating decline of biodiversity, the burgeoning study of wildlife microbiotas can be of great assistance in the maintenance of endangered species (Dallas et al., 2023; Waite et al., 2014).\u003c/p\u003e \u003cp\u003eBirds are widely distributed across the globe and contain a variety of microbiota in the digestive tract in different species like other vertebrates (Waite et al., 2014). Current studies have shown that the dominant microbiota in most avian gut consists mainly of Firmicutes and Proteobacteria, followed by Fusobacteria, Bacteriodetes, and Actinobacteria (van Veelen et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Nowadays, the interest of study in gut microbiota of wild birds is increasing because the microbiota can be a source of human and many animal diseases through direct transmission or other ways (Tsiodras et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). In addition, birds can also act as vectors for long-distance pathogen transmission, and their fecal contamination has been well studied and confirmed (Ahmed et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Therefore, Hird (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) advised that we need to study their microbiota to gain a deeper understanding of the impact of birds on humans and other animals.\u003c/p\u003e \u003cp\u003eThe gut microbiota of birds is usually established immediately after birth and it is altered by various factors (Sommer et al., 2013). One important influenced factors is host phylogeny (Waite et al., 2014). A basic mechanism for host-specific gut microbiota is the formation of similar gut microbiota through parental vertical transfer (Ferretti et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Second, diet has been identified as the main cause of differences in gut microbiota in most of the current studies, and the importance of gut microbiota for digestion has been recognized (Grond et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Microbiota ingested with food may be one of the main pathways for microbial recruitment and establishment in the intestine of birds (Grond et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Different diet can influence the composition of the gut microbiota (Liu et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). It had been estimated that wild woodrats (\u003cem\u003eNeotoma lepida\u003c/em\u003e) and anole lizards (\u003cem\u003ePhymaturus Williams\u003c/em\u003e) acquired 25% and 47% of their gut microbiota from ingested plant foods, respectively (Kohl et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Kohl et al., 2014). Halpern and Senderovich (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) had also proposed a concept of relation with predator and prey: some microbiota of the prey colonized the gut of the predator and become an part of the predator's gut microbiota. The study of Laviad-Shitrit et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) had also further demonstrated that the great cormorants (\u003cem\u003ePhalacrocorax carbo\u003c/em\u003e) was infected \u003cem\u003eVibrio cholerae\u003c/em\u003e after consuming tilapias (\u003cem\u003eOreochromis niloticus\u003c/em\u003e X \u003cem\u003eOreochromis aureus\u003c/em\u003e) carrying pathogenic bacteria.\u003c/p\u003e \u003cp\u003eAs wildlife\u0026rsquo;s survival habitat, the environment also has an impact on the gut microbiota of animals. It had been shown that the urbanization had dramatically altered the gut bacterial communities of urban birds (Teyssier et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Different gut microbiota had been found in the same species living in different areas of the city (Berlow et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). For migratory birds that constantly changed stopover sites during migration, there was also considerable evidence of variability in the gut microbiota of birds at different migratory stopovers (Hird, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Gut microbiota of birds from different habitats with similar affinities can adjust quickly and show convergence after migrating to the same stopover site (Lewis et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The gut microbiota of immigrant and native Silver-eared Mesia (\u003cem\u003eLeiothrix argentauris\u003c/em\u003e) living in heavy-metal-contaminated mining environments did not differ significantly in composition, diversity, or function and evolved heavy-metal-contamination damage adaptations with a very high proportion of pathogenic bacteria (Zhou et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Interestingly, there is no shortage of studies on birds migrating from the sea to inland, or from inland to the sea, but few articles have been dedicated to investigate the difference of the gut microbiota of the same species that lives in coastal and inland area.\u003c/p\u003e \u003cp\u003eCurrently, different environments are inconclusive on how to shape the avian gut microbiota. One suggestion is that microbiota may change when exposed to new bacteria environment (Risely et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Birds are exposed to local environment (including water, soil, and nesting environments) after birth, and the microbes in these habitats then become the potential gut microbiota in birds (Grond et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Environmental factors had been demonstrated to have influence on the gut microbiota of passerine birds, especially in newborns that have not yet developed a stable gut microbiota and whose gut microbiota structure is more susceptible to environmental factors (Hird et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In addition, microbes from soil can successfully colonize the gut of sterile mice and even exceed the core gut microbiota in abundance (Seedorf et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). For mature wild hosts, it had also been shown to be possible to obtain considerable amounts of microbes from the environment. For example, a study of cloacal, feather, nest, and body surface microbiota in woodlark (\u003cem\u003eLullula arborea\u003c/em\u003e) and eurasian skylark (\u003cem\u003eAlauda arvensis\u003c/em\u003e) showed that the microbiota in adult were influenced by local environmental microbiota (van Veelen et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). It is unclear whether obtaining microbiota from the environment is a characteristic of all wild hosts, including those with migratory behavior. Therefore, the avian gut microbiota may change when they move to new environments and are exposed to new microbiota. Grond et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) stated that to determine the effect of the environment on the gut microbiota of birds, it was necessary to sample both the birds and local environments, including soil sediments and water at foraging or nesting sites.\u003c/p\u003e \u003cp\u003eLittle egret (\u003cem\u003eEgretta garzetta\u003c/em\u003e), one of the common wader of Ardeidae, is widespread in Africa, Europe, and Asia. They mainly feed on fish, shrimps, crabs, aquatic insects and living in many environments, such as ponds, marshes, lakes, coastal shallows. These make it an ideal model to study the effects of different environments on the gut microbiota of the same species. It has been experimentally demonstrated that feces produced by birds may contaminate crops, cultured fishs (Laviad-Shitrit et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), waters where humans play (Ryu et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), which would further affecting human production and activities. Little egret also overlaps considerably with human activity areas. Previous studies on the gut microbiota of little egrets are scarce (Laviad-Shitrit et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), so it is necessary to study the composition of their gut microbiota and their interactions with the environment. Therefore, the fecal and environmental samples collected from coastal and inland water were sequenced and analysed by 16S rRNA sequencing technology. We intend to investigate: (1) whether there are differences in the fecal microbiota of the little egret from different areas, (2) the microbial composition of the environmental factors, like water and soil, (3) whether the environmental microbiota influence the fecal microbiota of the little egret? The results of this study would be benefit to answer the question of environmental effects on gut microbiota and to provide reference on the potential risks that the little egrets' fecal microbiota may bring about.\u003c/p\u003e"},{"header":"Material and method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSample locations\u003c/h2\u003e \u003cp\u003eWe selected two different kinds of habitats for sampling. The first was the seaside salt water (SW) environment, where little egrets use grasslands, salt pans, mudflats, farmlands, and aquaculture ponds as their major activity areas. This environment is rich in marine fish (mainly in \u003cem\u003ePerciformes\u003c/em\u003e, \u003cem\u003ePleuronectiformes\u003c/em\u003e, \u003cem\u003eClupeiformes\u003c/em\u003e) (Zhang et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), mollusks, arthropods, copepods, and other food resources. The second was an inland freshwater environment (FW), where little egrets mainly roost in farmlands, grasslands, and ponds, with a certain number of freshwater fishes, primarily comprising tilapias (\u003cem\u003eOreochromis mossambicus\u003c/em\u003e), as well as a small number of amphibians, mollusks, and arthropods, and other food resources.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSample collection\u003c/h2\u003e \u003cp\u003eAlthough fecal samples only represent a component of gut microbial composition, they have been widely used as part of a non-invasive sampling method to study the gut microbiota in wild birds (Grond et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). We collected 30 samples, including 12 fecal samples (SWF) of little egrets, 2 saltwater (SWW) samples and 2 saltwater soil (SWS) samples from the saltwater area of the seashore, and 10 fecal samples (FWF) of little egrets, 2 freshwater (FWW) and 2 freshwater soil (FWS) samples from the freshwater areas. Before collecting the fecal samples, we observed and selected the areas where only little egrets were active to ensure only fresh feces were collected. We collected the samples at least 5 m apart to ensure that feces were from different individuals. Feces in contact with the ground were avoided. Afterward, the feces were stored in sterile freezing tubes. Although feces were sampled in the field, all samples were temporarily frozen in ice boxes and subsequently transported to the laboratory and stored at − 40°C until further processing.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eDNA extraction and PCR amplification\u003c/h2\u003e \u003cp\u003eThe total microbiota DNA was extracted from fecal, soil, and water samples following the instructions provided in the E.Z.N.A.® Soil DNA Kit (Omega Bio-tek, Norcross, GA, U.S.). Microbiota structural studies were conducted via the polymerase chain reaction (PCR) amplification method using primers for the V3–V4 region of the 16s rRNA 341F 5'-CCTAYGGGRBGCASCAG-3' and 806R 5'-GGACTACNNGGGGTATCTAAT-3.' The amplification primers for each sample contained 8-base tag sequences to differentiate the samples. The PCR reaction mix contained 4 µL of 5×FastPfu buffer, 2 µL of 2.5 mM dNTPs, 0.8 µL of 5 µM of each primer, 0.4 µL of FastPfu polymerase, 10 ng of the DNA template, and double-distilled H\u003csub\u003e2\u003c/sub\u003eO in a total volume of 20 µL. The PCR amplification steps were as follows: 95℃ for 5 min; 28 cycles at 95℃ for 30 s, 55℃ for 30 s, 72℃ for 45 s; and final extension at 72°C for 10 min. The amplification products were purified by 2% agarose gel electrophoresis using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, US) according to the manufacturer’s instructions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eLibrary construction and sequencing\u003c/h2\u003e \u003cp\u003eSMRTbell libraries were constructed from the amplified DNA according to the manufacturer’s instructions (Pacific Biosciences). Each sample was ligated with a specific barcode sequence and mixed in equal mass. The amplicon mixtures were used to construct sequencing libraries using the Pacific Biosciences SMRTbellTM Template Prep kit 1.0 and sequenced on a PacBio Sequel II. All amplicon sequencing was undertaken by Shanghai Lingen Biotechnology (Shanghai, China). The raw data were uploaded to the National Center for Biotechnology Information (NCBI) SRA database.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eSequencing data processing\u003c/h2\u003e \u003cp\u003ePacBio raw reads were processed using the SMRT Link Analysis software version 6.0 to obtain cyclic congruent sequence (CCS) reads: parameters were set to a minimum number of passes (3) and minimum prediction accuracy (0.99). The raw reads were processed through the SMRT Portal to screen for sequence length (\u0026lt; 700 or \u0026gt; 1500 bp) and quality. Further filtering included removing the barcodes, primer sequences, chimeras, and sequences containing 10 consecutive identical bases.\u003c/p\u003e \u003cp\u003eThe passed sequences were duplicated and subsequently, the DADA2 algorithm was used to identify the insertion deletions and substitutions (Callahan et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This was performed on the double-ended data with a maximum of two expected errors per read (maxEE = 2). After splicing the sequences and chimera filtering, each 16S rRNA gene sequence (hereafter referred to as ASV) was analyzed by an RDP Classifier (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://rdp.cme.msu.edu/\u003c/span\u003e\u003cspan address=\"http://rdp.cme.msu.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) against the Silva (SSU132) 16S rRNA database using a confidence threshold of 70% (Amato et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) for phylogenetic relationships.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAlpha and beta diversity analyses\u003c/h2\u003e \u003cp\u003eWe used the rarefaction curve analysis based on Mothur v.1.21.1 (Schloss et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) software to reveal the diversity indices including Chao, Shannon diversity, and Pd_faith indices. Beta diversity analyses were performed using the Bray–Curtis and UniFrac (Lozupone et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) distance to compare the community differences. The primary analysis software was R (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org/\u003c/span\u003e\u003cspan address=\"https://www.r-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Mantel tests were performed using the vegan package in R to analyze Spearman’s correlations between little egret, water, and soil and bacterial communities similarities in different regions, using the Bray–Curtis distance matrix. Next, a multivariate analysis of variance (MANOVA) was performed to further confirm the observed differences. Spearman’s correlation coefficients were evaluated to determine the relationship between bacterial communities of little egrets, water, and soils in different regions. Correlation heatmaps and network diagrams were visualized using R (heatmap package) and Cytoscape (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.cytoscape.org\u003c/span\u003e\u003cspan address=\"http://www.cytoscape.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Redundancy analysis (RDA) was conducted to explore the relationship between environmental factors and bacterial communities. An analysis of variance (ANOVA) was used to assess the differences in diversity indices between samples, and differences were considered significant at \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eLEfSe differences analysis\u003c/h2\u003e \u003cp\u003eLinear discriminant analysis effect size (LEfSe) (Segata et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) analysis was used to identify the biomarker species distinguishing the two or more biological conditions (or taxa). The non-parametric factorial Kruskal–Wallis (KW) sum-rank test was first used to detect the taxa with significant abundance difference characteristics and identify the taxa differing significantly from the abundance. Finally, LEfSe used the linear discriminant analysis (LDA) to estimate the magnitude of the effect of each component’s (species’) abundance on the differential effect (Ijaz et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003ePrediction of microbial gene function\u003c/h2\u003e \u003cp\u003eThe Kyoto Encyclopedia of Genes and Genomes (KEGG) database was used along with the PICRUSt2 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://picrust.github.io/picrust/tutorials/genome_prediction.html\u003c/span\u003e\u003cspan address=\"http://picrust.github.io/picrust/tutorials/genome_prediction.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) software to predict the functional differences in microbiota in different samples and infer the functional alterations. The obtained ASV data were used to generate the BIOM files, which were formatted and set as an input to PICRUSt2 containing a make.biom script generation that can be used in Mothur.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" type=\"Results\" class=\"Section2\"\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003ch2\u003eMicrobiota composition\u003c/h2\u003e\u003cp\u003eAfter quality control, 12,060 ASVs were obtained. Rarefaction curves demonstrated that the sample sequencing depth was sufficient to reflect the vast majority of microbiota in each sample (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea).\u003c/p\u003e\u003cp\u003eThe SWF samples had 26 phyla, 58 classes, 130 orders, 219 families, 396 genera, and 165 species. The dominant phyla were Proteobacteria (49.550%), Firmicutes (37.091%), Bacteroidota (4.903%), and Fusobacteriota (4.811%). The SWS samples had 45 phyla, 75 classes, 174 orders, 201 families, 254 genera, and 30 species, with the dominant phyla being Proteobacteria (46.088%), Bacteroidota (24.205%), Chloroflexi (6.602%), and Desulfobacterota (5.679%). The SWW samples had 26 phyla, 41 classes, 103 orders, 139 families, 173 genera, and 33 species, and the dominant phyla were Proteobacteria (82.500%), Bacteroidota (7.112%), and Actinobacteria (6.293%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb).\u003c/p\u003e\u003cp\u003e21 phyla, 36 classes, 101 orders, 160 families, 276 genera, and 154 species were identified in the FWF samples. The dominant phyla were Firmicutes (51.098%), Proteobacteria (45.084%), Fusobacteriota (1.739%), and Bacteroidota (1.05%). FWS samples had 36 phyla, 73 classes, 150 orders, 200 families, 294 genera, and 36 species, and the dominant phyla were Proteobacteria (71.414%), Desulfobacteriota (6.071%), and Bacteroidota (5.346%), whereas the FWW samples had 33 phyla, 65 classes, 134 families, 175 families, 255 genera and 5.05 genera, and the dominant phyla were Proteobacteria (52.130%), Actinobacteriota (19.365%), Cyanobacteria (17.110%), and Bacteroidota (5.772%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb).\u003c/p\u003e\u003cp\u003eAt the genus level, the dominant genera in the SWF samples were \u003cem\u003eEscherichia-Shigella\u003c/em\u003e (14.448%), \u003cem\u003eEnterococcus\u003c/em\u003e (10.064%), \u003cem\u003eVibrio\u003c/em\u003e (7.812%), \u003cem\u003eCatellicoccus\u003c/em\u003e (5.794%), \u003cem\u003eBradyrhizobium\u003c/em\u003e (5.301%), \u003cem\u003eCetobacterium\u003c/em\u003e (4.511%), \u003cem\u003eVibrionimonas\u003c/em\u003e (4.167%), \u003cem\u003eParaclostridium\u003c/em\u003e (4.075%), and \u003cem\u003ePeptoclostridium\u003c/em\u003e (4.021%). The higher abundance of \u003cem\u003ePhaeodactylibacter\u003c/em\u003e (16.420%) and \u003cem\u003eLimibaculum\u003c/em\u003e (7.335%) was detected in the SWS samples. The SWW samples included \u003cem\u003eBradyrhizobium\u003c/em\u003e (31.803%) and \u003cem\u003eRhodanobacter\u003c/em\u003e (11.111%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec).\u003c/p\u003e\u003cp\u003eThe dominant genera in the FWF samples were \u003cem\u003eSporosarcina\u003c/em\u003e (18.241%), \u003cem\u003eCitrobacter\u003c/em\u003e (11.987%), \u003cem\u003eAcinetobacter\u003c/em\u003e (6.201%), and \u003cem\u003eKurthia\u003c/em\u003e (5.725%). \u003cem\u003eDechloromonas\u003c/em\u003e (13.325%) was high in FWS samples, whereas \u003cem\u003eRhodanobacter\u003c/em\u003e (14.367%), \u003cem\u003eCyanobium PCC-6307\u003c/em\u003e (8.664%), and \u003cem\u003eBradyrhizobium\u003c/em\u003e (7.485%) were dominant in the FWW samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec).\u003c/p\u003e\u003ch2\u003eMicrobiota diversity\u003c/h2\u003e\u003cp\u003eAnalyses of α and β diversity were conducted on the fecal microbiota of little egrets living in different habitats. The differences in Chao1, Shannon, and Pd_faith indices were insignificant (\u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05) between the two habitats (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea-c). In contrast, the results of the PCoA plot based on Weighted_Unifrac distances demonstrated significant differences in the little egret fecal microbiota between the two habitats (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed).\u003c/p\u003e\u003cp\u003eNext, the microbiota of little egret feces and their habitats were compared to investigate the extent of the influence of the environment. Overall, most differences in α-diversity indices between the microbiota of little egret feces and the environment were significant (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05), whereas β-diversity was significantly different (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05) in both habitats (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea-d), representing a lesser impact of the environment on the microbiota of little egret feces.\u003c/p\u003e\u003cp\u003eDifferences in the β-diversity between the environmental samples from SW and FW regions were insignificant (\u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed), possibly representing microbial conservatism between the environments.\u003c/p\u003e\u003ch2\u003eDifferences in microbiota composition\u003c/h2\u003e\u003cp\u003eFor easy comparison, water and soil from the same habitat (SWW and SWS, FWW and FWS) were combined into environmental samples (SWE and FWE) for Venn diagram analysis. Only 26 shared ASVs between the little egrets and environmental samples, 141 shared ASVs between SWF and FWW, 73 shared ASVs between SWF and SWE, and 102 shared ASVs between FWW and FWE (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Significant differences existed in the dominant microbiota between little egrets in the two regions, as well as between little egrets and the environment.\u003c/p\u003e\u003cp\u003eIn terms of contribution of major microbiota differences, FWF microbiota had a significantly greater abundance of \u003cem\u003eBacillales\u003c/em\u003e, \u003cem\u003eBacilli\u003c/em\u003e, \u003cem\u003ePlanococcaceae\u003c/em\u003e, and \u003cem\u003eSporosarcina\u003c/em\u003e than SWF samples, whereas \u003cem\u003eAlphaproteobacteria\u003c/em\u003e, \u003cem\u003eRhizobiales\u003c/em\u003e, and \u003cem\u003eClostridia\u003c/em\u003e were more abundant in SWF samples(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003ch2\u003eAnalysis of functional differences in microbiota\u003c/h2\u003e\u003cp\u003eThe microbiota functions in little egret feces and the two environments were similar in the relative abundance of the level 1 pathway–both dominated by metabolism (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). The following functions were in the order of relative abundance: environmental information processing, cellular processes, genetic information processing, human diseases, and organismal systems. In the level 2 pathway, the global and overview maps, cell motility, replication and repair, folding, sorting and degradation, and metabolism of cofactors and vitamins of FWF were significantly higher than SWF (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05), whereas xenobiotic biodegradation and metabolism, cellular community–prokaryotes, metabolism of terpenoids and polyketides, and biosynthesis of other secondary metabolites of SWF were significantly higher (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). In the level 3 pathway, FWF samples were significantly higher in metabolic pathways, bacterial chemotaxis, and ribosomes (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05), whereas SWF samples were higher in quorum sensing, microbial metabolism in diverse environments, ABC transporters, and environments (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eConsistent with the data from previous studies on different bird species, we found that little egret fecal samples from both habitats were dominated by Firmicutes and Proteobacteria. Firmicutes are associated with the dissociation of complex carbohydrates, polysaccharides, glucose, and fatty acids, which are major sources of nutrients for animal hosts (Tap et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Therefore, it is not surprising that Firmicutes were detected in such a high abundance in the little egret fecal samples. Compared to mammals or poultry, wild birds carry a higher proportion of Proteobacteria bacteria in their intestines. The Proteobacteria consists largely of pathogenic bacteria such as \u003cem\u003eCampylobacter\u003c/em\u003e, \u003cem\u003eEscherichia\u003c/em\u003e, and \u003cem\u003eVibrio\u003c/em\u003e (Diakou et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Wallm\u0026eacute;nius et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), an indicator of gut microbiota instability (Shin et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The detection of a high abundance of Proteobacteria in little egret feces is noteworthy; however, it needs to be further explored as their function in birds is unknown. Despite being in different environments, the fecal microbiota of little egrets at the two sites differed; both had Firmicutes and Proteobacteria as the primary bacterial phyla, suggesting that the fecal microbiota of the little egrets were conserved at a certain extent.\u003c/p\u003e \u003cp\u003eFusobacteriota is a common group in the avian gut microbiota (Waite et al., 2015). In contrast to the results of our study, the relative abundance of Fusobacteriota in the intestinal tract of migratory little egrets in the Israeli region was higher than that of Proteobacteria. It was approximately 40% in the study conducted on the Israeli little egret intestine (Laviad-Shitrit et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), which was considerably more than in this study (less than 5%). This could be related to the difference in the geographic environment or residential type. Israel is located in the Middle East and has a Mediterranean climate, whereas the study area of the present research had a subtropical monsoon climate, with differences in the living environment (Tadmor-Levi et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and food resources (Goren, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1974\u003c/span\u003e; Roni et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) between the two places. In addition, migratory or non-migratory status could be another influential factor. The published 16S rRNA gene data on migratory shorebirds demonstrated that most populations of migratory shorebirds that crossed different geographical areas (e.g., Australia, the United States, and the Arctic) typically carried a high proportion of Fusobacteria (average abundance of approximately 20%) (Zhang et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The little egrets used for the study in the Israeli region were winter migrant birds; however, the little egrets were resident birds in our study, which were observed year-round in the study area.\u003c/p\u003e \u003cp\u003eThe genus with the highest abundance in the FWF samples was \u003cem\u003eSporosarcina\u003c/em\u003e. The members of this genus in the avian gut have not been comprehensively studied. Only a few species, such as \u003cem\u003eSporosarcina luteola\u003c/em\u003e and \u003cem\u003eSporosarcina pasteurii\u003c/em\u003e, have been detected, and certain studies have demonstrated the abundance of these bacteria in soil and were associated with the mineralization of metal ions in the soil (Li et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, bacteria of this genus were absent from the environmental samples, possibly predicting a low environmental influence on the fecal microbiota of little egrets. Next, the genera in abundance were \u003cem\u003eCitrobacter\u003c/em\u003e and \u003cem\u003eAcinetobacter\u003c/em\u003e, both of which contained opportunistic pathogenic bacteria (Mullineaux-Sanders et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). It could be a risk of infection to humans. \u003cem\u003eKurthia\u003c/em\u003e were associated with the dissociation of compounds (Dong et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and could be beneficial for little egret digestion.\u003c/p\u003e \u003cp\u003eThe most abundant genus within the SWF group is \u003cem\u003eEscherichia-Shigella\u003c/em\u003e, followed by \u003cem\u003eEnterococcus\u003c/em\u003e, \u003cem\u003eVibrio\u003c/em\u003e, and \u003cem\u003eCetobacterium\u003c/em\u003e. \u003cem\u003eShigella\u003c/em\u003e were highly infectious enteric pathogens that invaded and caused deep inflammation and tissue damage in the colorectal epithelium (Carayol et al., 2013). It could prompt persistent inflammation (Small et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), whereas \u003cem\u003eEnterococcus\u003c/em\u003e exerts an inhibitory effect on pathogenic bacteria (Hanchi et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), which could be beneficial to little egret survival. Although \u003cem\u003eVibrio\u003c/em\u003e and \u003cem\u003eCetobacterium\u003c/em\u003e were more prevalent in marine fishes (Verner-Jeffreys et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), these were rarely observed in the FWF samples. Therefore, it seemed like the seashore-living little egrets acquired indirectly by ingesting marine fish, which could confirm the predator\u0026ndash;prey hypothesis (Halpern et al., 2015).\u003c/p\u003e \u003cp\u003eThe α and β diversities can reflect the richness, and diversity of microbiota within groups as well as between the groups. The results demonstrated that although the α-diversity of the microbiota of bird feces at the two habitats differed, it was insignificant. PCoA results based on the Unweighted_Unifrac and Weighted_Unifrac distances revealed that the microbiota of the feces of little egrets at the two habitats differed highly significantly (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). This suggested that the fecal microbiota of little egrets at the two sites differed considerably in relative abundance and phylogeny. However, compared with the Unweighted_Unifrac distance, the PCoA diagram obtained through the Weighted_Unifrac distance revealed less inter- and intra-group differences, suggesting that the relative abundance of core microbe in the fecal microbiota of little egrets at the two areas was more conserved and could be related to species phylogeny.\u003c/p\u003e \u003cp\u003eThe results of fecal microbiota diversity in common kestrel (\u003cem\u003eFalco tinnunculus\u003c/em\u003e) before and after captivity (Zhang et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) were consistent with that reported in the present study, with non-significant differences in α-diversity and significant differences in β-diversity. Zhang et al. concluded that the captive environment exerted limited effects on the gut microbiota. They speculated that the food could change the abundance of fecal microbiota because of the large changes in the types of food before and after captivity.\u003c/p\u003e \u003cp\u003eA study of functional composition revealed that the relative abundance of fecal microbial level 1 pathways was extremely similar between the little egret of the two habitats, with both being dominated by metabolism. It was consistent with the notion that metabolic pathways accounted for the highest proportion (48.35%) of all bacterial functions in the avian gut (Zhu et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), suggesting that the gut microbiota were important in building the host\u0026rsquo;s metabolic capacity.\u003c/p\u003e \u003cp\u003eLittle egret feces contain a considerable abundance of pathways associated with human diseases. A variety of birds such as raptors, songbirds, and shorebirds carry a wide range of pathogens (Keller et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Ksoll et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). For example, we detected a certain abundance of \u003cem\u003eCampylobacter\u003c/em\u003e ASVs in both FWF and SWF, whereas \u003cem\u003eEscherichia-Shigella\u003c/em\u003e in SWF samples even displayed the highest abundance at the level of genus, and these bacterial genera, as well as \u003cem\u003eSalmonella\u003c/em\u003e and others, had been identified as pathogens in humans and birds (Newman et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). These highly abundant pathogens had the potential to increase the risk of contaminating the environment and thus infecting humans.\u003c/p\u003e \u003cp\u003eAlthough the two habitats were separated by a long distance with different water, the environmental microbiota in the two areas did not differ significantly in either α or β diversity, demonstrating a high degree of environmental microbial conservatism. Except for the non-significant difference in α-diversity between little egret feces and environmental microbiota in the SW region, differences in α-diversity between little egret feces and environmental microbiota in the FW region as well as the differences in β-diversity between the two habitats were significant. Previous studies have reported that environmental microbiota could colonize the gut of newborn chicks (Grond et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), whereas the fecal samples collected in the present study originated from adult little egrets, which could be less affected by environmental factors such as soil and water.\u003c/p\u003e \u003cp\u003eThe microbial sequencing results of water and soil collected from little egret\u0026rsquo;s foraging areas demonstrated that both soil and water samples were dominated by Proteobacteria. This is similar to the high levels of Proteobacteria in little egrets in both areas. In addition, we had discussed that Proteobacteria contained a higher number of pathogenic bacteria associated with intestinal instability, which could lead to diseases in little egrets. A study of bacterial functional pathways indicated that similar to the fecal microbiota of little egret, the microbiota of water and soil contained a certain abundance of pathways associated with human diseases, such as the detection of ASVs of \u003cem\u003eVibrio\u003c/em\u003e, \u003cem\u003eEscherichia\u003c/em\u003e, \u003cem\u003ePseudomonas\u003c/em\u003e, and \u003cem\u003eCampylobacter\u003c/em\u003e which had been categorized as opportunistic pathogens (Zhang et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Little egrets were often found in urban parks, ponds, agricultural fields, and the seashore where humans often play, and the areas where they were active were close to human beings, thus posing a risk of infecting humans through feces. Therefore, direct contact between humans and little egrets should be avoided or reduced in both urban and seaside areas. Moreover, policies should be implemented to control free access to egrets and prohibit their hunting and eating.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis paper collected the feces of little egret and environmental samples from coastal and inland areas to explore how the fecal microbiota responds to different environments by using 16S rRNA high-throughput sequencing methods. The main fecal microbiota phyla of little egrets from two sampling sites were found to be similar, whereas there was a major difference in genus. The differences in α-diversity were insignificant between the little egrets, but the β-diversity was significant. Most of the parameters reflecting α-diversity and β-diversity were shown to be significantly different between the fecal and environmental microbiota. Since Vibrio, which are more abundant in marine fish than freshwater fish, were dominant within SWF but rare in FWF, with the limited influence of the environment on the fecal microbiota of little egrets, this paper speculates that the differences in the fecal microbiota from different regions may be more related to the food species than the environmental microbiota. In addition, since a portion of ASVs classified as opportunistic pathogens were detected in the little egret feces, it is suggested that the relevant authorities should carry out rational planning and zoning of urban and coastal wetland areas where birds and humans share to ensure their safety. We used non-invasive methods to obtain samples and observe little egrets foraging in the field. However, we did not dissect the gut contents of little egrets at the two sites to analyze the specific food intake of the little egrets. In the future, the differences in food types and the relationship between food and gut microbial differences of little egrets could be analyzed by dissecting the gut contents including food types and microbial composition of food of little egrets in the two areas.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no funding, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMZ collected samples and analyzed data, and was a major contributor in writing the manuscript. YH helped to search for information. GY, ZL helped with the experiment and sampling process. LY guided the experiment and manuscript writing. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eAnimal Science and Technology\u0026nbsp;College, Guangxi\u0026nbsp;University,\u0026nbsp;Nanning 530004, Guangxi, China.\u0026nbsp;\u003csup\u003e2.\u003c/sup\u003eGuangxi Forest Inventory and Planning Institute, Nanning 530011, Guangxi, China. Minhui Zhang: [email protected]. Yaoyin He: [email protected]. Gang Yang: [email protected]. Zhou Lu: [email protected]. Lijiang Yu: [email protected].\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAhmed, W., Harwood, V. 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Adaptive evolution to a high purine and fat diet of carnivorans revealed by gut microbiomes and host genomes. \u003cem\u003eEnviron Microbiol, 20\u003c/em\u003e(5), 1711-1722. doi:10.1111/1462-2920.14096\u003c/li\u003e\n\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":"little egret, environment, fecal microbiota, pathogenic bacteria, 16S rRNA","lastPublishedDoi":"10.21203/rs.3.rs-4026154/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4026154/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe digestive tract of birds contains diverse microbiota that are essential for their health and survival, and these gut microbiota are influenced by a variety of factors. To compare the diversity in fecal microbiota of the same bird species in different environments, this paper collected feces of Little Egret (\u003cem\u003eEgretta garzetta\u003c/em\u003e) and environmental (soil and water) samples from coastal salt water and inland freshwater areas to explore how fecal microbiota responds to different environments using 16S rRNA high-throughput sequencing methods.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe main fecal microbiota from two sampling sites were similar at the phylum level. In terms of genus level, the dominant genera in feces from salt-water habitats (SWF) were \u003cem\u003eEscherichia-Shigella \u003c/em\u003e(14.448%), \u003cem\u003eEnterococcus \u003c/em\u003e(10.064%), \u003cem\u003eVibrio \u003c/em\u003e(7.812%), whereas there were \u003cem\u003eSporosarcina \u003c/em\u003e(18.241%), \u003cem\u003eCitrobacter \u003c/em\u003e(11.987%), \u003cem\u003eAcinetobacter \u003c/em\u003e(6.201%), \u003cem\u003eKurthia \u003c/em\u003e(5.725%) in feces from freshwater habitats (FWF). A few ASVs were sharedamong the egrets and the environmental samples in the two regions. The fecal microbiota between the two sampling sites showed no significant differences in α-diversity and were significant in β-diversity; there were no significant differences between the environmental microbiota diversity of the two sites. In contrast, most of the parameters reflecting α-diversity and β-diversity were shown to be significantly different between the fecal and environmental microbiota. In composition of microbiota function, the fecal microbiota of little egret and the environment microbiota were similar in relative abundances in the proportion of kegg level 1 functional pathways. But there were significant differences in some level 2 and level 3 functional pathways between the egrets from two habitats. Moreover, a portion of ASVs classified as opportunistic pathogens were detected in the feces of the egrets.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results of this study indicate that there are differences in the fecal microbiota of the little egrets in different environments, and the differences are less affected by soil and water. Considering that \u003cem\u003eVibrio\u003c/em\u003e which was more common in the guts of marine fish, it is hypothesized that regional differences in fecal microbiota composition are related to changes in food types.\u003c/p\u003e","manuscriptTitle":"Comparison of fecal microbiota of Little Egret (Egretta garzetta) inhabiting coastal and inland environment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-14 21:02:14","doi":"10.21203/rs.3.rs-4026154/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":"073fb3ce-433f-450b-a2af-73525ed9d9cc","owner":[],"postedDate":"March 14th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-06-06T09:57:47+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-14 21:02:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4026154","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4026154","identity":"rs-4026154","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}