Rong Fu, Xingjia Xiang, Yuanqiu Dong, Lei Cheng, Lizhi Zhou. 2020: Comparing the intestinal bacterial communies of sympatric wintering Hooded Crane (Grus monacha) and Domestic Goose (Anser anser domesticus). Avian Research, 11(1): 13. DOI: 10.1186/s40657-020-00195-9
Citation: Rong Fu, Xingjia Xiang, Yuanqiu Dong, Lei Cheng, Lizhi Zhou. 2020: Comparing the intestinal bacterial communies of sympatric wintering Hooded Crane (Grus monacha) and Domestic Goose (Anser anser domesticus). Avian Research, 11(1): 13. DOI: 10.1186/s40657-020-00195-9

Comparing the intestinal bacterial communies of sympatric wintering Hooded Crane (Grus monacha) and Domestic Goose (Anser anser domesticus)

Funds: 

the National Natural Science Foundation of China 31772485

the National Natural Science Foundation of China 31801989

More Information
  • Corresponding author:

    Lizhi Zhou, zhoulz@ahu.edu.cn

  • Received Date: 20 Dec 2019
  • Accepted Date: 05 Apr 2020
  • Available Online: 24 Apr 2022
  • Publish Date: 29 Apr 2020
  • Background 

    Gut microbiota play crucial roles in host health. Wild birds and domestic poultry often occupy sympatric habitats, which facilitate the mutual transmission of intestinal microbes. However, the distinct intestinal microbial communities between sympatric wild birds and poultry remain unknown. At present, the risk of interspecies transmission of pathogenic bacteria between wild and domestic host birds is also a research hotspot.

    Methods 

    This study compared the intestinal bacterial communities of the overwintering Hooded Crane (Grus monacha) and the Domestic Goose (Anser anser domesticus) at Shengjin Lake, China, using Illumina high-throughput sequencing technology (Mi-Seq platform).

    Results 

    Our results revealed that Firmicutes, Actinobacteria, Proteobacteria, Bacteroidetes and Chloroflexi were the dominant bacterial phyla in both hosts. The gut bacterial community composition differed significantly between sympatric Hooded Cranes and Domestic Geese. However, the hosts exhibited little variation in gut bacterial alpha-diversity. The relative abundance of Firmicutes was significantly higher in the guts of the Hooded Cranes, while the relative abundances of Actinobacteria, Proteobacteria, Bacteroidete and Chloroflexi were significantly higher in guts of Domestic Geese. Moreover, a total of 132 potential pathogenic operational taxonomic units (OTUs) were detected in guts of Hooded Cranes and Domestic Geese, and 13 pathogenic OTUs (9.8%) were found in both host guts. Pathogenic bacterial community composition and diversity differed significantly between hosts.

    Conclusions 

    The results showed that the gut bacterial community composition differs significantly between sympatric Hooded Cranes and Domestic Geese. In addition, potential pathogens were detected in the guts of both Hooded Cranes and Domestic Geese, with 13 pathogenic OTUs overlapping between the two hosts, suggesting that more attention should be paid to wild birds and poultry that might increase the risk of disease transmission in conspecifics and other mixed species.

  • In birds, coloration is one of the most important traits linked to social status, physiological state and sexual behavior (Hill and McGraw 2006). Parrots inhabiting different regions of the world constitute a group that has received special attention largely because of their colorful displays (del Hoyo et al. 1992). The wide variation in coloration observed in parrots is mainly the product of the nano-structure of their feathers (structural coloration) plus the presence of different kinds of pigments (melanins and a unique kind of pigment called psittacofulvins) (Stradi et al. 2001; McGraw and Nogare 2004; Berg and Bennett 2010). Furthermore, like all diurnal avian species, parrots perceive plumage coloration differently from mammals thanks to their tetrachromatic visual system, which includes a UV photoreceptor (Vorobyev et al. 1998). Because of this difference in the avian visual system, studies of plumage coloration relating to intra-specific signaling should assess coloration in a way that takes into account how color is perceived by other birds, especially in those species classified as sexually monochromatic according to the human visual system (del Hoyo et al. 1992). This is important since for a long time it was thought that sexual differences in plumage coloration and size tended to be weaker in monogamous than in polygamous species (Andersson 1994). Owens and Hartley (1998) demonstrated for the first time that size dimorphism and plumage color dimorphism in birds do not vary in the same way. They concluded that sexual differences in size are associated with the kind of mating system characteristic of the species and with sexual differences in parental care, that is, greater sexual differences in size at higher levels of polygamy and less parental care of the male. In terms of sexual differences in plumage coloration, they determined these to be associated with high levels of frequency of extra bond paternity exhibited by the bird species (Owens and Hartley 1998). Added to this, it has been reported that in the Psittacidae family, two South American species (Blue-fronted Amazon Amazona aestiva and the Burrowing Parrot Cyanoliseus patagonus) which are considered monogamous and without sexual size dimorphism, exhibit differences between sexes that are visually indistinguishable by humans (Santos et al. 2006; Masello et al. 2009). Although providing information on only two South American species, these studies suggest that incorporating the UV region into the discrimination analysis may reveal that sexual plumage color dimorphism could be more frequent than previously thought (Hausmann et al. 2003; Santos et al. 2006; Masello et al. 2009).

    In this context, the Monk Parakeet (Myiopsitta monachus), another Psittacidae family member native to South America, which has hitherto been considered to be sexually monomorphic, presents an interesting case for study of plumage color and size dimorphism (del Hoyo et al. 1992). The Monk Parakeet is a colonial parrot considered to be socially monogamous; however, recent genetic studies provide evidence of extra-pair paternity, breeding trios and cases of intra-brood parasitism (Martínez et al. 2013; Bucher et al. 2016). During summer, the Monk Parakeet uses sticks to build nests with multiple individual chambers occupied by different pairs and to a lesser extent by trios (del Hoyo et al. 1992; Spreyer et al. 1998; Forshaw 2010; Hobson et al. 2014, 2015).

    Like other parrots, the Monk Parakeet has a combination of structural and pigmentary coloration: to the human eye it looks green over most of its body, gray on its head and belly, and blue on the flight feathers. No studies have quantified plumage color dimorphism and little regarding to size dimorphism (Martinez et al. 2018) in this parrot. Based on the background data above, we aimed to determine whether adult Monk Parakeet males and females exhibit sexual plumage color dimorphism and size dimorphism in any of their body regions. To this end, we used spectrophotometry to objectively measure plumage reflectance across the visible spectrum of birds (300–700 nm) and performed morphometric analysis.

    Thirty-two adult wild male and thirty-six adult wild female Monk parakeets (Psittacidae family, Psittacinae subfamily, tribe Arini) were captured inside Córdoba Zoo, Argentina (31° 25′ 31.79″ S, 64° 10′ 29.92″ W) with passive traps, following the procedure described in Valdez and Benitez-Vieyra (2016) to trap doves. Captures were made during May and June 2017, thus avoiding the molting period in the Southern hemisphere (December to April) (Navarro et al. 1992). All animals were sacrificed using sodium pentobarbital and sexed by examination of the reproductive organs; the skins were then used in the spectrophotometry measurements. We determined and compared the reflectance spectrum of twelve different body regions: forehead, crown, cheeks, nape, back, chest, belly, blue wing coverts, green wing coverts, primary and secondary remiges, and tail (on all upper surfaces). The animals were sacrificed as part of a neuroendocrine study.

    Bird coloration cannot be accurately analyzed with tools designed for human vision, as birds perceive colors in a radically different way. Thus, we carried out all reflectance measurements within the avian spectral sensitivity range (300–700 nm, Bowmaker et al. 1997) using an Ocean Optics USB4000 spectrophotometer equipped with a halogen and a deuterium light source (830 Douglas Ave., Dunedin, FL, USA 34698), both connected to the sensor by a bifurcated fiber optic cable. Each plumage region was illuminated, and the light reflected at 45° was collected. The distance between the probe and the plumage was 4 mm, the spectrophotometer resolution 0.19 nm, the integration time 300 ms and each spectrum was the average of three readings. A white standard (Ocean Optics, WS-1-SS White Standard) was used to re-calibrate the equipment between measurements in order to correct for possible shifts in performance. Reflectance was measured using SpectraSuite software (Ocean Optics, Inc.).

    After obtaining reflectance spectra, we applied a receptor-noise limited model of avian vision (Vorobyev et al. 1998) to estimate how avian receivers of chromatic signals would perceive the parakeets' plumage colorations. This model takes into account the number and sensitivity of color receptors in the avian eye (cones) and how color information is processed in terms of signal-to-noise ratio, assuming that color discrimination is limited by photoreceptor noise. To apply an avian visual model to reflectance data, we used the pavo 2.4.0 package (Maia et al. 2013) for R (Team RC. R: A language and environment for statistical computing 2013). Cone quantum catch (Q) for each of the four avian cones was calculated under standardized daylight illumination (D65) as a representative spectrum for open habitat ambient light at midday, similar to the type of habitat that this species frequents. We used UV-type avian eyes for spectral cone sensitivities as a general representative of the parrot visual system (Bowmaker et al. 1997). Although cone parameters have not been measured in M. monachus, we used the generalized spectral cone sensitivities of the UV-type of Melopsittacus undulatus eyes since this is the only member of the Psittaciformes that has been characterized to date (Bowmaker et al. 1997; Goldsmith and Butler 2005; Lind et al. 2014). Contrasts between males–males, males‒females and females–females were characterized in units of "just noticeable differences" (JND), such that one JND represents the threshold of possible discrimination. Chromatic (dS) and achromatic (dL) distances were calculated in JNDs following the vision model using cues. Visual stimuli separated by one JND are discernible by birds, although only under ideal illumination (Olsson et al. 2015).

    For each Monk Parakeet we measured the height, width and length of the bill (from the tip to the base of the skull), length of the tarsus, total length and wing length. For this we used a digital caliper (range 0‒150 mm; resolution 0.01 mm; accuracy ± 0.02 mm) and a millimeter metal ruler (50 cm). The animals were also weighed with a PESOLA brand spring scale (accuracy ± 2 g).

    First, we examined the mean ± 2SE reflectance spectra of males and females to determine the presence of overlapping regions. Then, we calculated all pairwise chromatic and achromatic distances (measured in JNDs) among males, among females, and between males and females, for each body region. We tested whether between-sex differences were greater than within-sex differences using a permutational multivariate analysis of variance (PERMANOVA, Anderson 2001), as implemented in the adonis function of the vegan R package (Oksanen 2017). PERMANOVA is a multivariate analogue of the univariate analysis of variance (ANOVA) where multivariate variance is partitioned in the space of any arbitrary dissimilarity measure, JNDs in our case. In this test, significance is obtained by comparing the multivariate version of the F-statistic with the results obtained under a large number of permutations. We performed 24 PERMANOVAs (12 for achromatic distances and 12 for chromatic distances) to test for differences between sexes in each body part. Each test involved 999 permutations. We considered the observed distance between sexes to be significantly different from that expected by random chance if it was greater than 95% of the randomized values.

    Finally, we examined the multivariate morphological variation between sexes in the six traits detailed above by principal component analysis (PCA). In addition, we applied a multivariate analysis of variance (MANOVA) to test for multivariate differences between sexes. In contrast with color cues, differences in morphology can be expressed in terms of euclidean distances among individuals, fulfilling the assumptions of common (parametric) MANOVA. When a significant effect was detected, we performed additional univariate nested ANOVAs to determine which traits accounted for the significant effect in the MANOVA.

    No noticeable differences between sexes were observed in spectral shape for any of the studied body regions. There was considerable overlapping of mean reflectance values in some regions. The comparison between the mean ± 2SE reflectance spectra of males and females for the twelve different body regions examined throughout the avian visual range (300‒700 nm) is shown in Fig. 1b.

    Figure  1.  Spectrophotometric analysis of Monk Parakeet plumage. a Photograph of the Monk Parakeet in its colonial nest. b Reflectance spectra (300‒700 nm) of the twelve Monk Parakeet body regions. Each spectrum represents the mean reflectance ± 2SE of 32 males (blue) and 36 females (red)

    The achromatic and chromatic distances in JNDs among males, among females and between males and females are shown in Fig. 2. In all body regions examined, mean achromatic distances were higher than 1 JND (discrimination limit under ideal illumination). These indicate that on average, individuals could be distinguished based on achromatic cues, even when they are of the same sex (Fig. 2). Nevertheless, mean achromatic distances were not significantly different between sexes (PERMANOVA, in all cases F1, 67 < 4.117 and p > 0.06), with the exception of the blue wing coverts (F1, 67 = 5.512, p = 0.021) and nape (F1, 67 = 4.524, p = 0.042).

    Figure  2.  Achromatic and chromatic distances after applying an avian visual model. Mean values ± standard deviations of achromatic and chromatic distances measured as just noticeable differences (JNDs) following a receptor-noise limited model of avian color vision. Vertical dotted lines indicate the discrimination threshold of one JND. At values below 1, individual chromatic and achromatic cues cannot be distinguished. Permutational multivariate analyses of variance (PERMANOVAs) were used to test whether or not differences between sexes were greater than within sexes. Significant differences (p < 0.05) are indicated with an asterisk (*)

    In the mean chromatic distances, we found that three (forehead, cheeks and belly) of the twelve body regions studied were below the discrimination limit 1, indicating that individuals cannot be distinguished based on these cues. In the other nine corporal regions (crown, nape, back, chest, blue wing coverts, green wing coverts, primary remiges, secondary remiges and tail) mean chromatic distances were equal or higher than the discrimination limit. In seven corporal regions (nape, back, chest, green wing coverts, primary remiges, secondary remiges and tail) no significant differences between males and females were observed (PERMANOVA, in all cases F1, 67 < 3.076, p > 0.090) (Fig. 2). The only two exceptions were the blue wing coverts (PERMANOVA, F1, 67 = 4.28, p = 0.025) and the crown (F1, 67 = 5.659, p = 0.013). However, notice that the average color distance between sexes for the crown was only 1.109 JNDs (Fig. 2), suggesting that this difference between sexes is hardly noticeable for birds.

    The first three principal components of the PCA explained 72.31% of the total variation recorded among individuals (Fig. 3). All traits had positive, similar loadings on PC1, suggesting that this component is associated with general differences in size among individuals (Table 1). Bill traits and total weight attained positive loadings on PC2, while wing length, tarsus length and total length attained negative loadings (Table 1). Thus, positive PC2 scores correspond to heavier birds with more prominent bills, but with shorter wings, tarsus and total length. The opposite is true for individuals with negative PC2 scores. Finally, total length had a strong positive loading on PC3, while bill width attained a strong and negative score (Table 1), indicating that this PC expresses differences among individuals in these traits. MANOVA indicated that there were significant differences between sexes (Wilks' λ = 0.554, p < 0.0001) and univariate ANOVAs showed these differences to be accounted for by body weight (F1, 66 = 18.524, p = 0.0001), bill height (F1, 66 = 27.118, p < 0.0001), bill length (F1, 66 = 13.471, p = 0.0005) and bill width (F1, 66 = 25.183, p < 0.0001), but not by tarsus, wing and total length (in all cases F1, 66 < 2.038, p > 0.158; Fig. 3).

    Figure  3.  Morphometric analysis of the Monk Parakeet. Morphological differences between sexes in Monk Parakeet. a PCA for the five variables analyzed (height, width and length of the bill; length of the tarsus; total length, total weight and wing length). Polygons indicate convex hulls, and females are in orange and males in blue. b Box and whisker plots of those morphometric variables for which significant differences were detected using one-way ANOVAs. Boxes indicate the 25th and 75th percentiles (lower and upper quartiles, respectively) of the distribution, while the central band indicates the median. "Whiskers" indicate 1.5 times the interquartile range. Points indicate outliers
    Table  1.  Variable loadings and proportion of the variance explained by the first three principal components
    Variable PC1 PC2 PC3
    Bill length 0.428 0.108 0.398
    Bill width 0.385 0.402 < 0.001
    Bill height 0.429 0.265 ‒ 0.508
    Total weight 0.336 0.432 0.163
    Wing length 0.380 ‒ 0.444 ‒ 0.165
    Total length 0.338 ‒ 0.435 0.578
    Tarsus length 0.338 ‒ 0.428 ‒ 0.442
    Proportion of the variance explained 0.395 0.218 0.110
     | Show Table
    DownLoad: CSV

    In this work we examined the sexual plumage color dimorphism and size dimorphism in the colonial breeder Monk Parakeet (M. monachus) using an objective methodology (spectrophotometry) and morphometric analysis. The superposition of the reflectance spectra shows that overall, mean reflectance values are the same for both sexes in all body regions studied (see Fig. 1b). On the other hand, the achromatic and chromatic distances obtained with the avian visual model indicate a subtle sexual plumage color dimorphism in this parrot. Analysis of the mean pairwise achromatic distances showed all body regions to be above the discrimination limit for both sexes, with only slight differences between males and females for only blue wing coverts and nape. These findings may indicate that males and females are able to differentiate among individuals but not discern their sex based on plumage brightness. An interesting result regarding the parakeets' blue wing coverts is that they have mean achromatic distances above the discrimination level along with significant differences between the sexes. These feathers are only visible when the birds are flying, and are hidden when they are resting. This leads us to the question of the role these feathers play in sex differentiation: could they be involved in sex recognition during flight? Are they involved in mate choice? More detailed studies are necessary to answer these questions. Furthermore, the differences between male and female Monk Parakeets' nape achromatic distances are subtle (see Fig. 2), with values close to the discrimination limit, making it difficult to gauge the biological significance of the findings. Regarding the chromatic distances, three body regions showed chromatic distance values below the discrimination threshold, indicating that it is unlikely that individuals could be distinguished based on the coloration of these regions. The nine remaining body regions had distance values equal to or higher than the discrimination limit and seven of them did not show significant differences between males and females. The blue wing coverts and the crown were the only body regions observed to differ between males and females. The blue wing coverts were the only body region showing significant differences in both distances, but again, these feathers are hidden at rest and are only visible during flight.

    The crown on the other hand is of particular interest since it is involved in both social behavior and mate choice in different bird species (Bennett et al. 1997; Andersson and Andersson 1998; Siitari et al. 2002; Delhey et al. 2003). The differences between the male and female Monk Parakeet's crown are subtle (see Fig. 2), with values close to the discrimination limit (as in the case of the chest), again making it difficult to gauge the biological significance of the findings. Further experiments under controlled conditions should be carried out in order to evaluate whether the crown is linked to mate choice or other social behavior. Similar results were obtained with the morphometric analysis, where statistically significant differences between the sexes were only observed in terms of bill size and body weight. Again, the differences in bill size and body weight were subtle (just 1 mm in bill size and 5 g between sexes), so it is open to question whether parrots are able to discriminate these differences. Martínez and coworkers (Martinez et al. 2018) found that heavier Monk Parakeet males with larger beaks are paired with heavier females and with larger beaks, but this may be because (similarly to Burrowing Parrot C. patagonus) the Monk Parakeets could form longlasting pair-bonds from an early age and no for assortative mating (Martín and Bucher 1993). The only way to corroborate this idea is by performing experiments under controlled conditions.

    Within the context of the sexual plumage color dimorphism and size dimorphism in birds raised by Owens and Hartley (1998), our results are relevant since they are the first to describe these albeit subtle traits in the Monk Parakeet. In their work, Owens and Hartley studied the relationship between the degree of sexual differences in size, plumage color dimorphism, the kind of mating system, degree of parental care (greater sexual differences in size at higher levels of polygamy and less parental care of the male) and degree of extra-bond paternity displayed by birds (greater sexual differences in plumage coloration at high levels of frequency of extra bond paternity). As already mentioned, Owens and Hartley observed a relation between the sexual plumage color dimorphism for structural coloration (as observed in parrots) and the level of a species' extra-bond paternity. An example of the pattern described by Owens and Hartley is observed in another South American parrot, the Burrowing Parrot (C. patagonus), which is genetically monogamous and practices biparental care (Masello et al. 2002). Only with the use of spectrophotometry could a slight sexual plumage color dimorphism in the green and blue regions (differed in brightness) while the red region differed in spectral shape (reddish ventral patch) and a slight size dimorphism be observed in these birds, males being around 5% larger than females (Masello and Quillfeldt 2003, 2009). This finding corroborates the idea raised by Owens and Hartley (smaller differences in size and more parental care in monogamous species and subtle differences in coloration in species with low frequency of extra-bond paternity). In addition, despite the fact that the fundamental unit of social structure in Monk Parakeet populations is the pair (Hobson et al. 2014, 2015), which would indicate them to be monogamous, the populations studied by Martinez et al. and Bucher et al. show extra-bond paternity (EBP) behaviors, intra-specific parasitism (ISP) and reproductive trios (RT) (Martínez et al. 2013; Bucher et al. 2016). But the fact is that these behaviors in Monk Parakeets occur in a context of high inbreeding (Bucher et al. 2016); that is, the exhibited EBP, ISP and RT behaviors always occur between highly related individuals, resulting in high levels of inbreeding. This inbreeding phenomenon could be partly explained by the low dispersal distance (~ 2 km) of this species in its native South American distribution (Martín and Bucher 1993). The opposite phenomenon is observed when the Monk Parakeet behaves as an invasive exotic species in other regions of the world, where its dispersion distance can reach up to 100 km, favoring a reduction in inbreeding parameters (Da Silva et al. 2010). The high inbreeding values found by Bucher and coworkers could act as an attenuating factor of the sexual dimorphism observed in the Monk Parakeet and could explain the subtle sexual plumage color dimorphism and size dimorphism findings in our work. More detailed studies at the level of coloration and size are required to shed more light on this issue.

    Since the Monk Parakeet appears to exhibit subtle sexual plumage color dimorphism and size dimorphism, the question then arises as to how males and females are able to tell themselves apart in order to form couples. Unlike in the case of the Budgerigar (Melopsittacus undulatus), where sex differences in cere color and vocalization have been documented (Baltz and Clark 1996; Nespor et al. 1996; Hile et al. 2000), it is not possible to study cere color in the Monk Parakeet because the cere is covered by feathers of the same color as the forehead; and only group vocalizations have been studied within Monk Parakeet colonies during social interactions (threat call, alarm call, flight call, contact call, etc.) (Martella and Bucher 1990). Whether or not there are sex differences in this species' vocalization therefore remains undetermined. Behavioral sex differences have been reported in different species of parrots (repetitive strutting, raised crests, strutting back and forth along a perch, head bowing, flaring the wings, etc.) (del Hoyo et al. 1992), but only repetitive strutting behaviors have been observed in the Monk Parakeet (Eberhard 1998), indicating that it is perhaps a combination of calls and behaviors that make the sexes distinguishable from one another.

    In conclusion, this study reports a subtle sex plumage color dimorphism and size dimorphism in a species of a colonially breeding parrot native to South America, the Monk Parakeet. Comprehensive studies aimed at discriminating sex differences in calls and behaviors should be carried out in order to arrive at a better understanding of sexual recognition in this species.

    We are grateful to the Córdoba Zoo (Argentina) for their generous assistance and for allowing us to conduct part of this research in their facilities (Avian feed, etc.). This work is dedicated to the memory of Mr. Luis Ricardo Valdez for his invaluable assistance with the trap construction. D. J. Valdez and S. M. Benitez-Vieyra are researchers of CONICET. The study met Argentine legal requirements.

    MM and DJG contributed equally to this paper. DJV conceived and designed the experiments. MM, DJG and DJV performed the experiments. MM, DJG, DJV and SMB-V analyzed the data. DJV contributed reagents/materials/analysis tools. DJV and SMB-V wrote the paper. All authors read and approved the final manuscript.

    The corresponding author (Diego Javier Valdez) had no funding to cover this research; the entire work was made possible by the generous support of Córdoba Zoo, whose staff provided assistance in catching parakeets. PhDs Alicia Sércic and Andrea Cocucci from the Multidisciplinary Institute of Plant Biology (IMBIV-CONICET-UNC) generously lent the spectrophotometer. None of the persons and institutions mentioned above had grants for this research.

    The datasets (data tables and scripts) supporting the conclusions of this article are available upon request.

    This study was carried out in strict accordance with the Guidelines for Ethical Research on Laboratory and Farm Animals and Wildlife Species and with the prior approval of the ethics committee of the Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) (Resolution No. 1047 ANNEXO II, 2005). The required permits were acquired from the Ministerio de Agua, Ambiente y Servicios Públicos of Córdoba, Argentina, through the Secretaría de Ambiente y Cambio Climático to capture specimens of Monk Parakeet for scientific purposes.

    Not applicable.

    The authors declare that they have no competing interests.

  • Alm EW, Daniels-Witt QR, Learman DR, Ryu H, Jordan DW, Gehring TM, et al. Potential for gulls to transport bacteria from human waste sites to beaches. Sci Total Environ. 2018;615:123-30.
    Bortoluzzi C, Lumpkins B, Mathis GF, Franca M, King WD, Graugnard DE, et al. Zinc source modulates intestinal inflammation and intestinal integrity of broiler chickens challenged with coccidia and Clostridium perfringens. Poult Sci. 2019;98:2211-9.
    Bottone EJ. Bacillus cereus, a volatile human pathogen. Clin Microbiol Rev. 2010;23:382-98.
    Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QⅡME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7:335-6.
    Caron A, De Garine-Wichatitsky M, Gaidet N, Chiweshe N, Cumming GS. Estimating dynamic risk factors for pathogen transmission using community-level bird census data at the wildlife/domestic interface. Ecol Soc. 2010;15:299-305.
    Chen SX, Wang Y, Chen FY, Yang HC, Gan MH, Zheng SJ. A highly pathogenic strain of Staphylococcus sciuri caused fatal exudative epidermitis in piglets. PLoS ONE. 2007;2:1-6.
    Chen JY, Zhou LZ, Zhou B, Xu RX, Zhu WZ, Xu WB. Seasonal dynamics of wintering waterbirds in two shallow lakes along Yangtze River in Anhui Province. Zool Res. 2011;32:540-8.
    Chevalier C, Stojanovic O, Colin DJ, Suarez-Zamorano N, Tarallo V, Veyrat-Durebex C, et al. Gut microbiota orchestrates energy homeostasis during cold. Cell. 2015;163:1360-74.
    Craven SE, Stern NJ, Line E, Bailey JS, Cox NA, Fedorka-Cray P. Determination of the incidence of Salmonella spp., Campylobacter jejuni, and Clostridium perfringens in wild birds near broiler chicken houses by sampling intestinal droppings. Avian Dis. 2000;44:715-20.
    Curtis SK, Kothary MH, Blodgett RJ, Raybourne RB, Ziobro GC, Tall BD. Rugosity in Grimontia hollisae. Appl Environ Microbiol. 2007;73:1215-24.
    Delaunay E, Abat C, Rolain JM. Enterococcus cecorum human infection. France. New Microbes New Infect. 2015;7:50-1.
    Deng P, Swanson KS. Gut microbiota of humans, dogs and cats: current knowledge and future opportunities and challenges. Br J Nutr. 2015;113:S6-17.
    Desai SS, Harrison RA, Murphy MD. Capnocytophaga ochracea causing severe sepsis and purpura fulminans in an immunocompetent patient. J Infect. 2007;54:e107-109.
    Dewar ML, Arnould JPY, Dann P, Trathan P, Groscolas R, Smith S. Interspecific variations in the gastrointestinal microbiota in penguins. MicrobiologyOpen. 2013;2:195-204.
    Dufrêne M, Legendre P. Species assemblages and indicator species: the need for a flexible asymmetrical approach. Ecol Monogr. 1997;67:345-66.
    Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010;26:2460-1.
    Ekong PS, Fountain-Jones NM, Alkhamis MA. Spatiotemporal evolutionary epidemiology of H5N1 highly pathogenic avian influenza in West Africa and Nigeria, 2006-2015. Transbound Emerg Dis. 2018;65:e70-82.
    Erbasan F. Brain abscess caused by Micrococcus luteus in a patient with systemic lupus erythematosus: case-based review. Rheumatol Int. 2018;38:2323-8.
    Fan PX, Bian BL, Teng L, Nelson CD, Driver J, Elzo MA, et al. Host genetic effects upon the early gut microbiota in a bovine model with graduated spectrum of genetic variation. ISME J. 2020;14:302-17.
    Fang J, Wang ZH, Zhao SQ, Li YK, Tang ZY, Yu D, et al. Biodiversity changes in the lakes of the Central Yangtze. Front Ecol Environ. 2006;4:369-77.
    Ferraz V, McCarthy K, Smith D, Koornhof HJ. Rothia dentocariosa endocarditis and aortic root abscess. J Infect. 1998;37:292-5.
    Flint HJ, Bayer EA, Rincon MT, Lamed R, White BA. Polysaccharide utilization by gut bacteria: potential for new insights from genomic analysis. Nat Rev Microbiol. 2008;6:121-31.
    Fox AD, Cao L, Zhang Y, Barter M, Zhao MJ, Meng FJ, et al. Declines in the tuber feeding waterbird guild at Shengjin Lake national nature reserve, China-a barometer of submerged macrophyte collapse. Aquat Conserv-Mar Freshw Ecosyst. 2011;21:82-91.
    Galen SC, Witt CC. Diverse avian malaria and other haemosporidian parasites in Andean house wrens: evidence for regional co-diversification by host switching. J Avian Biol. 2014;45:374-86.
    Grond K, Ryu H, Baker AJ, Domingo JWS, Buehler DM. Gastro-intestinal microbiota of two migratory shorebird species during spring migration staging in Delaware Bay, USA. J Ornithol. 2014;155:969-77.
    Grond K, Lanctot RB, Jumpponen A, Sandercock BK. Recruitment and establishment of the gut microbiome in arctic shorebirds. FEMS Microbiol Ecol. 2017;93:142.
    Grond K, Sandercock BK, Jumpponen A, Zeglin LH. The avian gut microbiota: community, physiology and function in wild birds. J Avian Biol. 2018;49:e01788.
    He SD, Zhang ZY, Sun HJ, Zhu YC, Cao XD, Ye YK, et al. Potential effects of rapeseed peptide Maillard reaction products on aging-related disorder attenuation and gut microbiota modulation in d-galactose induced aging mice. Food Funct. 2019;10:4291-303.
    Hird SM, Carstens BC, Cardiff S, Dittmann DL, Brumfield RT. Sampling locality is more detectable than taxonomy or ecology in the gut microbiota of the brood parasitic Brown-headed Cowbird (Molothrus ater). PeerJ. 2014;2:e321.
    Hsueh PR, Teng LJ, Yang PC, Wang SK, Chang SC, Ho SW, et al. Bacteremia caused by Arcobacter cryaerophilus 1B. J Clin Microbiol. 1997;35:489-91.
    Jiao SW, Guo YM, Huettmann F, Lei GC. Nest-site selection analysis of hooded crane (Grus monacha) in northeastern china based on a multivariate ensemble model. Zool Sci. 2014;31:430-7.
    Jourdain E, Gauthier-Clerc M, Bicout DJ, Sabatier P. Bird migration routes and risk for pathogen dispersion into western mediterranean wetlands. Emerg Infect Dis. 2007;13:365-72.
    Jung A, Chen LR, Suyemoto MM, Barnes HJ, Borst LB. A review of Enterococcus cecorum infection in poultry. Avian Dis. 2018;62:261-71.
    Kira J, Isobe N. Helicobacter pylori infection and demyelinating disease of the central Nervous System. J Neuroimmunol. 2019;329:14-9.
    Koziel N, Kukier E, Kwiatek K, Goldsztejn M. Clostridium perfringens-epidemiological importance and diagnostics. Med Weter. 2019;75:265-70.
    LaFrentz BR, Garcia JC, Waldbieser GC, Evenhuis JP, Loch TP, Liles MR, et al. Identification of four distinct phylogenetic groups in Flavobacterium columnare with fish host associations. Front Microbiol. 2018;9:452-65.
    Lalitha P, Srinivasan M, Prajna V. Rhodococcus ruber as a cause of keratitis. Cornea. 2006;25:238-9.
    Lan PTN, Hayashi H, Sakamoto M, Benno Y. Phylogenetic analysis of cecal microbiota in chicken by the use of 16S rDNA clone libraries. Microbiol Immunol. 2002;46:371-82.
    Lee SH, Kim KK, Rhyu IC, Koh S, Lee DS, Choi BK. Phenol/water extract of Treponema socranskii subsp. socranskii as an antagonist of Toll-like receptor 4 signalling. Microbiology. 2006;152:535-46.
    Li G, Du XS, Zhou DF, Li CG, Huang LB, Zheng QK, et al. Emergence of pathogenic and multiple-antibiotic-resistant Macrococcus caseolyticus in commercial broiler chickens. Transbound Emerg Dis. 2018;65:1605-14.
    Loy A, Pfann C, Steinberger M, Hanson B, Herp S, Brugiroux S, et al. Lifestyle and horizontal gene transfer-mediated evolution of Mucispirillum schaedleri, a core member of the murine gut microbiota. Msystems. 2017;2:e00171.
    IUCN. The IUCN Red List of Threatened Species. 2020. Version 2019-3. .
    Morgavi DP, Rathahao-Paris E, Popova M, Boccard J, Nielsen KF, Boudra H. Rumen microbial communities influence metabolic phenotypes in lambs. Front Microbiol. 2015;6:1060.
    Muegge BD, Kuczynski J, Knights D, Clemente JC, Gonzalez A, Fontana L, et al. Diet drives convergence in gut microbiome functions across mammalian phylogeny and within humans. Science. 2011;332:970-4.
    Murakami Y, Hanazawa S, Tanaka S, Iwahashi H, Yamamoto Y, Fujisawa S. A possible mechanism of maxillofacial abscess formation: involvement of Porphyromonas endodontalis lipopolysaccharide via the expression of inflammatory cytokines. Oral Microbiol Immunol. 2001;16:321-5.
    Nejrup RG, Licht TR, Hellgren LI. Fatty acid composition and phospholipid types used in infant formulas modifies the establishment of human gut bacteria in germ-free mice. Sci Rep. 2017;7:3975.
    Nielsen HL. First report of Actinomyces europaeus bacteraemia result from a breast abscess in a 53-year-old man. New Microbes New Infect. 2015;7:21-2.
    Nocera FP, Papulino C, Del Prete C, Palumbo V, Pasolini MP, De Martino L. Endometritis associated with Enterococcus casseliflavus in a mare: a case report. Asian Pac Trop Biomed. 2017;7:760-2.
    Oksanen J, Blanchet G, Friendly M, Kindt R, Legendre P, McGlinn D, et al. Vegan: community ecology package. Version 2.0-2. 2010.
    Pantin-Jackwood MJ, Costa-Hurtado M, Shepherd E, DeJesus E, Smith D, Spackman E, et al. Pathogenicity and transmission of H5 and H7 highly pathogenic avian influenza viruses in mallards. J Virol. 2016;90:9967-82.
    Pate M, Zolnir-Dovc M, Kusar D, Krt B, Spicic S, Cvetnic Z, et al. The first report of Mycobacterium celatum isolation from domestic pig (Sus scrofa domestica) and roe deer (Capreolus capreolus) and an overview of human infections in Slovenia. Vet Med Int. 2011;2011:432954.
    Peng WJ, Dong B, Zhang SS, Huang H, Ye XK, Chen LN, et al. Research on rare cranes population response to land use change of nature wetland. J Indian Soc Remote Sens. 2018;46:1795-803.
    Perofsky AC, Lewis RJ, Meyers LA. Terrestriality and bacterial transfer: a comparative study of gut microbiomes in sympatric Malagasy mammals. ISME J. 2019;13:50-63.
    Ramey AM, Pearce JM, Flint PL, Ip HS, Derksen DV, Franson JC, et al. Intercontinental reassortment and genomic variation of low pathogenic avian influenza viruses isolated from northern pintails (Anas acuta) in Alaska: examining the evidence through space and time. Virology. 2010;401:179-89.
    Reed C, Bruden D, Byrd KK, Veguilla V, Bruce M, Hurlburt D, et al. Characterizing wild bird contact and seropositivity to highly pathogenic avian influenza a (H5N1) virus in Alaskan residents. Influenza Other Resp. 2014;8:516-23.
    Ruiu L. Brevibacillus laterosporus, a pathogen of invertebrates and a broad-spectrum antimicrobial species. Insects. 2013;4:476-92.
    Sanders JG, Beichman AC, Roman J, Scott JJ, Emerson D, McCarthy JJ, et al. Baleen whales host a unique gut microbiome with similarities to both carnivores and herbivores. Nat Commun. 2015;6:8285.
    Scheid PL, Lam TT, Sinsch U, Balczun C. Vermamoeba vermiformis as etiological agent of a painful ulcer close to the eye. Parasitol Res. 2019;118:1999-2004.
    Scher JU, Sczesnak A, Longman RS, Segata N, Ubeda C, Bielski C, et al. Expansion of intestinal Prevotella copri correlates with enhanced susceptibility to arthritis. eLife. 2013;2:e01202.
    Segata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, et al. Metagenomic biomarker discovery and explanation. Genome Biol. 2011;12:60.
    Smith PA, Pizarro P, Ojeda P, Contreras J, Oyanedel S, Larenas J. Routes of entry of Piscirickettsia salmonis in rainbow trout Oncorhynchus mykiss. Dis Aquat Organ. 1999;37:165-72.
    Stanley D, Denman SE, Hughes RJ, Geier MS, Crowley TM, Chen HL, et al. Intestinal microbiota associated with differential feed conversion efficiency in chickens. Appl Microbiol Biotechnol. 2012;96:1361-9.
    Stanley D, Hughes RJ, Moore RJ. Microbiota of the chicken gastrointestinal tract: influence on health, productivity and disease. Appl Microbiol Biotechnol. 2014;98:4301-10.
    Speirs LBM, Rice DTF, Petrovski S, Seviour RJ. The phylogeny, biodiversity, and ecology of the chloroflexi in activated sludge. Front Microbiol. 2019;10:2015.
    Spence C, Wells WG, Smith CJ. Characterization of the primary starch utilization operon in the obligate anaerobe Bacteroides fragilis: regulation by carbon source and oxygen. J Bacteriol. 2006;188:4663-72.
    Vendrell D, Balcazar JL, Ruiz-Zarzuela I, de Blas I, Girones O, Muzquiz JL. Lactococcus garvieae in fish: a review. Comp Immunol Microbiol Infect Dis. 2006;29:177-98.
    Venugopal AA, Szpunar S, Johnson LB. Risk and prognostic factors among patients with bacteremia due to Eggerthella lenta. Anaerobe. 2012;18:475-8.
    Waite DW, Eason DK, Taylor MW. Influence of hand rearing and bird age on the fecal microbiota of the critically endangered kakapo. Appl Environ Microbiol. 2014;80:4650-8.
    Wilkinson TJ, Cowan AA, Vallin HE, Onime LA, Oyama LB, Cameron SJ, et al. Characterization of the microbiome along the gastrointestinal tract of growing turkeys. Front Microbiol. 2017;8:1-11.
    Wise MG, Siragusa GR. Quantitative analysis of the intestinal bacterial community in one- to three-week-old commercially reared broiler chickens fed conventional or antibiotic-free vegetable-based diets. J Appl Microbiol. 2007;102:1138-49.
    Xiang XJ, Zhang FL, Fu R, Yan SF, Zhou LZ. Significant differences in bacterial and potentially pathogenic communities between sympatric hooded crane and greater white-fronted goose. Front Microbiol. 2019;10:163.
    Xiong JB, Wang K, Wu JF, Qiuqian LL, Yang KJ, Qian YX, et al. Changes in intestinal bacterial communities are closely associated with shrimp disease severity. Appl Microbiol Biotechnol. 2015;99:6911-9.
    Yang L, Zhou LZ, Song YW. The effects of food abundance and disturbance on foraging flock patterns of the wintering hooded crane (Grus monacha). Avian Res. 2015;6:15.
    Yang MJ, Song H, Sun LN, Yu ZL, Hu Z, Wang XL, et al. Effect of temperature on the microflora community composition in the digestive tract of the veined rapa whelk (Rapana venosa) revealed by 16S rRNA gene sequencing. Comp Biochem Phys D. 2019;29:145-53.
    Zhao LL, Wang G, Siegel P, He C, Wang HZ, Zhao WJ, et al. Quantitative genetic background of the host influences gut microbiomes in chickens. Sci Rep. 2013;3:1163.
    Zhu WF, Wei HJ, Chen L, Qiu RL, Fan ZY, Hu B, et al. Characterization of host plasminogen exploitation of Pasteurella multocida. Microb Pathog. 2019;129:74-7.
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