Vertebrate species, such as reptiles, birds or mammals, harbour distinct communities of microbes in their digestive systems. These miniature ecosystems – also known as microbiomes – are unique to each owner and species, reflecting their diverse lifestyles and evolutionary history.

Urbanisation can disrupt these delicate intestinal communities. Humans and other animals living in cities have different gut microbes to their counterparts living in rural areas. And captive species in homes and zoos often acquire human gut bacteria in their digestive systems, which can lead to health problems in these animals. So far, it has been unclear whether such a humanization of gut bacteria also affects wild animals living in and around cities.

To investigate this further, Dillard et al. compared the gut microbes of wild reptiles, birds, and mammals living in close contact with humans in North America, such as coyotes, crested anole lizards and white-crowned sparrows. DNA sequencing showed that in urban environments, the composition of gut bacteria living in all three wildlife species resembled the ones in humans. The types of bacteria overrepresented in the guts of urban humans were also overrepresented in urban wildlife.

This suggests that urbanization can affect the composition of gut bacteria in wildlife species by disrupting or replacing portions of their microbiome. The reason for this pattern is unclear. It is possible that humans might be sharing their gut microbes directly with city animals, or that a human-like diet is causing the change. Given the role that gut microbes play in health and disease, it is important to find out whether these changes cause the animals any harm.

The gut microbial communities of vertebrates tend to reflect their host’s phylogenetic histories. Across a diversity of vertebrate clades, the community composition of the gut microbiota is on average more similar within host species than between host species, and microbiota dissimilarity between host species is positively associated with host evolutionary divergence time (Song et al., 2020; Moeller et al., 2017; Brooks et al., 2016; Muegge et al., 2011; Ochman et al., 2010; Ley et al., 2008). However, anthropogenic influences can erode the host-lineage specificity of gut microbiota through a process of humanization, in which hosts acquire microbiota constituents found in humans (Clayton et al., 2016; Houtz et al., 2021; Trevelline and Moeller, 2022). For example, although the specific effects of captivity on microbiota differ among mammalian species (e.g., Houtz et al., 2021, Alberdi et al., 2021; reviewed in Diaz and Reese, 2021), several studies have shown that captive mammals harbor gut microbiota constituents abundant in humans but missing from wild-living conspecific populations (Clayton et al., 2016; Houtz et al., 2021; Trevelline and Moeller, 2022), suggesting transmission from humans. If host species and microbiota have adapted to one another, disruption and replacement of native microbiota may have adverse consequences for host phenotypes and fitness. For example, recent studies in germ-free mice have found that mice seeded with non-native microbiota displayed stunted immunological development and growth rates relative to house mice seeded with native, house-mouse microbiota (Chung et al., 2012; Moeller et al., 2019) (although certain non-native, e.g., human, gut microbiota have been shown to provide growth and immune benefits to mice relative to other non-native microbiota or the germ-free state; e.g., Turnbaugh et al., 2006; Round and Mazmanian, 2010). Similarly, disruption of gut microbiota in captive mammals has been implicated in the gastrointestinal disorders often experienced by captive hosts (Clayton et al., 2016; Diaz and Reese, 2021; McKenzie et al., 2017), and efforts to restore wild microbiota (e.g., through fecal microbiota transplantation) to improve the health of captive animals has seen success in some cases (e.g., Koeppel et al., 2006; reviewed in Diaz and Reese, 2021).

With the influence of humans on ecosystems spreading globally, it is necessary to understand the effects on microbiota of vertebrate wildlife, including the possibility of microbiota convergence with humans. Of particular importance are the effects of urbanization, which are escalating at accelerating rates (Kalnay and Cai, 2003; Sun et al., 2020). Gut-microbiota composition differs between urban and rural settings in diverse species of vertebrate wildlife (Teyssier et al., 2020; Sugden et al., 2020; Berlow et al., 2021). However, the extent to which the gut microbiota of urban wildlife converge with those of humans has not yet been explored.

To test for humanization of wildlife gut microbiota in urban environments, we compared the gut microbiota of three vertebrate species—coyotes (Canis latrans), anoles (Anolis cristatellus and Anolis spp.), and white-crowned sparrows (Zonotrichia leucophrys)—and humans in urban and rural settings. Anoles, coyotes, and sparrows maintain both urban and rural populations throughout North America and represent model systems for study of impacts of urbanization on vertebrate biology (Berlow et al., 2021; Campbell-Staton et al., 2020; Tigas et al., 2002). We analyzed 492 fecal microbiota profiles from 94 crested anoles, 33 anole lizards of other species (Anolis spp.) (i.e., non-cristatellus anoles), 78 coyotes, 87 sparrows, and 487 adult humans. We sampled anoles in the city of Mayagüez on the western coast of Puerto Rico and across an eastern longitudinal transect through less densely populated areas in Quemado into the rural areas in Maricao (Figure 1—figure supplement 1). Coyote data derived from Sugden et al., 2020 were sampled in the city of Edmonton, Alberta, Canada, and rural areas around Leduc. Sparrow data derived from Berlow et al., 2021 included urban and rural sampling sites around San Francisco, California, United States. Human microbiota data were combined from three studies, including urban populations in the United States and rural populations in Malawi and Venezuela (Yatsunenko et al., 2012) as well as urban, semiurban (i.e., suburban), and rural populations in Cameroon (Lokmer et al., 2020) and Tibet (Li et al., 2018). Sample metadata are presented in Supplementary file 1. A total of 49,281 Amplicon Sequence Variants (ASVs) were observed, and their relative abundances are presented in Supplementary file 2 and taxonomic assignments against the Silva 138 database in Supplementary file 3. Taxonomic barplots for all sample groups are presented in Figure 1—figure supplement 2.

Next, we calculated all microbiota dissimilarities (binary Sorensen–Dice and Bray–Curtis) between pairs of samples (Bolyen et al., 2019). Sorensen–Dice was used to test whether the community memberships of gut microbiota of urban wildlife and humans have converged relative to those of rural wildlife and humans. PERMANOVA and PERMDISP indicated significant differences in gut-microbiota composition among wildlife populations in different sampling locations (p < 0.01 for each PERMANOVA comparison and p > 0.05 for each PERMDISP comparison), and random forest models were able to discriminate rural and urban ASV profiles for each wildlife species (Figure 1—figure supplement 3). Moreover, adonis2 PERMANOVA indicated independent effects of host species identity (R² = 0.274, p value = 0.001), host population (R² = 0.0743, p value = 0.001), and whether the population was urban or rural (R² = 0.0765, p value = 0.001). Similar effects were observed in analyses based on weighted dissimilarities (species identity R² = 0.192, p value = 0.001; host population R² = 0.0778, p value = 0.001; rural/urban R² = 0.0769, p value = 0.001).

Using pairwise beta diversity similarities, we then tested for each wildlife species whether the mean similarity between urban wildlife and human microbiota was significantly higher than that between rural wildlife and human microbiota. Results indicated that microbiota similarity to humans was significantly higher in urban settings than in rural settings across all wildlife species examined (Figure 1A-C) (nonparametric pv alue <0.05 in 51/54 comparisons between pairs of groups; Supplementary file 4). Moreover, anole microbiota profiles displayed a gradient of similarity to humans that recapitulated the longitudinal transect from Mayagüez (urban) to Maricao (rural) (Figure 1B; Figure 1—figure supplement 1). The increased similarity of urban wildlife and human microbiota was evident in principal coordinates plots of urban and rural conspecific wildlife populations and urban human populations from the United States (i.e., the urban human populations closest geographically to the wildlife populations) (Figure 1D and E) as well as principal coordinates plots of all populations (Figure 1—figure supplement 4). Note that most urban wildlife clustered more closely with rural conspecifics than they did with humans, reflecting predominant effects of host-species identity in both urban and rural environments. We also observed that microbiota of urban humans and urban wildlife were more similar on average than were microbiota of rural humans and urban wildlife (Figure 1A-C, Supplementary file 4) and that microbiota of individual hosts of the same species were significantly more similar on average within urban populations than within rural populations (Figure 1—figure supplement 5). Moreover, the microbiota of urban populations of different wildlife species were more similar on average than urban and rural populations in 2/3 comparisons between pairs of wildlife species (Figure 1—figure supplement 6). Similar results were observed for analyses based on Bray–Curtis similarities (Supplementary file 5). Cumulatively, these analyses indicated that the gut microbiota of distantly related vertebrates (anoles, coyotes, and sparrows) have converged compositionally with human gut microbiota in urban environments relative to the gut microbiota of rural conspecifics.

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The convergence of gut-microbiota community memberships between wildlife and humans in urban settings could result from parallel loss or gain of ASVs. Alpha diversity (Chao1 and Shannon entropy) tended to be lower on average in urban human populations than in rural human populations (nonparametric p value <0.05 in 2/3 sets of comparisons between urban and rural human populations; Figure 1—figure supplement 7), consistent with loss of ASVs. However, the opposite trend was observed in wildlife (nonparametric p value <0.05 in 2/6 sets of comparisons between urban and rural wildlife populations), consistent with gain of ASVs. To enable identification of the specific ASVs underlying the convergence among urban human and urban wildlife gut microbiota, we identified all ASVs shared by urban wildlife populations and humans to the exclusion of rural wildlife populations. These analyses revealed that more ASVs were shared by urban wildlife and humans to the exclusion of rural wildlife than by rural wildlife and humans to the exclusion of urban wildlife (Supplementary file 6), indicative of convergence of urban wildlife microbiota with human microbiota relative to rural wildlife microbiota.

In addition, to identify the ASVs significantly overrepresented in urban populations, we used ANCOM (Mandal et al., 2015) to test for differentially abundant ASVs between urban and rural populations for each wildlife species and human dataset (Figure 2). Lists of ASVs differentially abundant between rural and urban samples for each wildlife species and human dataset are presented in Supplementary file 7. We identified the ASVs that were differentially abundant between urban and rural populations in at least one wildlife species and at least one human dataset (Figure 2). These analyses revealed ASVs that were differentially abundant between urban and rural populations in both coyotes and humans and in both anoles and humans, but none in both sparrows and humans. Clustering of ASVs near zero in Figure 2 suggests limited power, yet all ASVs that were overrepresented in urban wildlife and detected in humans showed the parallel shifts in relative abundance between urban and rural populations of humans (Figure 2C–F) (Supplementary file 7). These results indicate parallel effects of urbanization on the relative abundances of certain ASVs in both humans and individual wildlife species, similar to the parallels that have been reported between the effects of urbanization and domestication on the microbiota of humans and wildlife, respectively (Reese et al., 2021). These ASVs included a predominant human Bacteroides commensal that constituted up to 15% of human gut microbiota in urban environments (Figure 2C) and was the single most significantly overrepresented ASV in urban humans (Figure 2—figure supplement 3). This ASV was also identified as overrepresented in urban populations in analyses that considered all wildlife populations simultaneously with host species as a covariate (Supplementary file 7; Figure 2—figure supplement 4).

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Overall, this study demonstrates that gut microbiota of urban populations of wildlife species were more similar to human gut microbiota than were the gut microbiota of rural, conspecific wildlife populations. Interestingly, this convergence was evident even though the urban human and wildlife populations examined resided in different geographic locations. One possible explanation for these results is that urban environments imposed parallel selective pressures on wildlife and human microbiota relative to rural environments. For example, parallel dietary shifts in urban environments may select for common sets of ASVs in humans and wildlife. Previous work in humans has shown that Bacteroides is positively associated with diets high in animal fat and protein (Wu et al., 2011), but the extent to which this or other dietary shifts experienced by humans in urban environments are shared by the urban wildlife sampled here is not known. Our results motivate future profiling (e.g., metabarcoding) of diets and gut-microbiota composition in urban and rural populations.

A nonmutually exclusive explanation for our results is bacterial spillover from humans into wildlife in cities. Testing the rates at which bacterial lineages transmit between humans and wildlife in cities will require further strain-level profiling of the gut microbiota of these hosts sampled in shared urban environments. Increased microbiota similarity in urban environments could not be readily explained by microbiota transmission from wildlife into humans in cities, because none of the wildlife species examined here occur in two of the regions where both urban and rural humans were sampled (i.e., Cameroon and Tibet). Close contact among hosts of the same species can generate social microbiomes—microbial metacommunities formed by microbial transmission along host social networks (Sarkar et al., 2020). Previous studies have also indicated that transmission of the gut microbiota can occur between distantly related wild-living mammalian species when they come into close contact, such as predator–prey interactions (Moeller et al., 2017). Here, we observed that urban wildlife shared more microbiota constituents with human populations, including rural human populations residing on different continents, than did rural wildlife. These observations imply the existence of gut microbial transmission routes among humans and distantly related vertebrate species in urban environments, motivating the need to investigate consequences of urban-associated microbiota changes for wildlife health and fitness.

Sequencing data have been deposited in Data Dryad at https://doi.org/10.5061/dryad.dfn2z353d.

1. 1. Moeller AH

   (2022) Dryad Digital Repository

   Humanization of wildlife gut microbiota in urban environments.

   https://doi.org/10.5061/dryad.dfn2z353d

1. 1. Yatsunenko T
   2. Rey FE
   3. Manary MJ
   4. Trehan I
   5. Dominguez-Bello MG

   (2012) qiime

   ID 850. Human gut microbiome differentiation viewed across cultures, ages and families.

   https://qiita.ucsd.edu/study/description/850
2. 1. Sugden S
   2. Sanderson D
   3. Ford K
   4. Stein LY

   (2020) NCBI BioProject

   ID PRJNA528764. An altered microbiome in urban coyotes mediates relationships between anthropogenic diet and poor health.

   https://www.ncbi.nlm.nih.gov/bioproject/PRJNA528764/

1. 1. Alberdi A
   2. Martin Bideguren G
   3. Aizpurua O

   (2021) Diversity and compositional changes in the gut microbiota of wild and captive vertebrates: a meta-analysis

   Scientific Reports 11:1–10.

   https://doi.org/10.1038/s41598-021-02015-6

   • PubMed
   • Google Scholar
2. 1. Berlow M
   2. Phillips JN
   3. Derryberry EP

   (2021) Effects of Urbanization and Landscape on Gut Microbiomes in White-Crowned Sparrows

   Microbial Ecology 81:253–266.

   https://doi.org/10.1007/s00248-020-01569-8

   • PubMed
   • Google Scholar
3. 1. Bolyen E
   2. Rideout JR
   3. Dillon MR
   4. Bokulich NA
   5. Abnet CC
   6. Al-Ghalith GA
   7. Alexander H
   8. Alm EJ
   9. Arumugam M
   10. Asnicar F
   11. Bai Y
   12. Bisanz JE
   13. Bittinger K
   14. Brejnrod A
   15. Brislawn CJ
   16. Brown CT
   17. Callahan BJ
   18. Caraballo-Rodríguez AM
   19. Chase J
   20. Cope EK
   21. Da Silva R
   22. Diener C
   23. Dorrestein PC
   24. Douglas GM
   25. Durall DM
   26. Duvallet C
   27. Edwardson CF
   28. Ernst M
   29. Estaki M
   30. Fouquier J
   31. Gauglitz JM
   32. Gibbons SM
   33. Gibson DL
   34. Gonzalez A
   35. Gorlick K
   36. Guo J
   37. Hillmann B
   38. Holmes S
   39. Holste H
   40. Huttenhower C
   41. Huttley GA
   42. Janssen S
   43. Jarmusch AK
   44. Jiang L
   45. Kaehler BD
   46. Kang KB
   47. Keefe CR
   48. Keim P
   49. Kelley ST
   50. Knights D
   51. Koester I
   52. Kosciolek T
   53. Kreps J
   54. Langille MGI
   55. Lee J
   56. Ley R
   57. Liu YX
   58. Loftfield E
   59. Lozupone C
   60. Maher M
   61. Marotz C
   62. Martin BD
   63. McDonald D
   64. McIver LJ
   65. Melnik AV
   66. Metcalf JL
   67. Morgan SC
   68. Morton JT
   69. Naimey AT
   70. Navas-Molina JA
   71. Nothias LF
   72. Orchanian SB
   73. Pearson T
   74. Peoples SL
   75. Petras D
   76. Preuss ML
   77. Pruesse E
   78. Rasmussen LB
   79. Rivers A
   80. Robeson MS
   81. Rosenthal P
   82. Segata N
   83. Shaffer M
   84. Shiffer A
   85. Sinha R
   86. Song SJ
   87. Spear JR
   88. Swafford AD
   89. Thompson LR
   90. Torres PJ
   91. Trinh P
   92. Tripathi A
   93. Turnbaugh PJ
   94. Ul-Hasan S
   95. van der Hooft JJJ
   96. Vargas F
   97. Vázquez-Baeza Y
   98. Vogtmann E
   99. von Hippel M
   100. Walters W
   101. Wan Y
   102. Wang M
   103. Warren J
   104. Weber KC
   105. Williamson CHD
   106. Willis AD
   107. Xu ZZ
   108. Zaneveld JR
   109. Zhang Y
   110. Zhu Q
   111. Knight R
   112. Caporaso JG

   (2019) Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2

   Nature Biotechnology 37:852–857.

   https://doi.org/10.1038/s41587-019-0209-9

   • PubMed
   • Google Scholar
4. 1. Brooks AW
   2. Kohl KD
   3. Brucker RM
   4. van Opstal EJ
   5. Bordenstein SR

   (2016) Phylosymbiosis: Relationships and Functional Effects of Microbial Communities across Host Evolutionary History

   PLOS Biology 14:e2000225.

   https://doi.org/10.1371/journal.pbio.2000225

   • PubMed
   • Google Scholar
5. 1. Campbell-Staton SC
   2. Winchell KM
   3. Rochette NC
   4. Fredette J
   5. Maayan I
   6. Schweizer RM
   7. Catchen J

   (2020) Parallel selection on thermal physiology facilitates repeated adaptation of city lizards to urban heat islands

   Nature Ecology & Evolution 4:652–658.

   https://doi.org/10.1038/s41559-020-1131-8

   • PubMed
   • Google Scholar
6. 1. Caporaso JG
   2. Kuczynski J
   3. Stombaugh J
   4. Bittinger K
   5. Bushman FD
   6. Costello EK
   7. Fierer N
   8. Peña AG
   9. Goodrich JK
   10. Gordon JI
   11. Huttley GA
   12. Kelley ST
   13. Knights D
   14. Koenig JE
   15. Ley RE
   16. Lozupone CA
   17. McDonald D
   18. Muegge BD
   19. Pirrung M
   20. Reeder J
   21. Sevinsky JR
   22. Turnbaugh PJ
   23. Walters WA
   24. Widmann J
   25. Yatsunenko T
   26. Zaneveld J
   27. Knight R

   (2010) QIIME allows analysis of high-throughput community sequencing data

   Nature Methods 7:335–336.

   https://doi.org/10.1038/nmeth.f.303

   • PubMed
   • Google Scholar
7. 1. Chung H
   2. Pamp SJ
   3. Hill JA
   4. Surana NK
   5. Edelman SM
   6. Troy EB
   7. Reading NC
   8. Villablanca EJ
   9. Wang S
   10. Mora JR
   11. Umesaki Y
   12. Mathis D
   13. Benoist C
   14. Relman DA
   15. Kasper DL

   (2012) Gut immune maturation depends on colonization with a host-specific microbiota

   Cell 149:1578–1593.

   https://doi.org/10.1016/j.cell.2012.04.037

   • PubMed
   • Google Scholar
8. 1. Clayton JB
   2. Vangay P
   3. Huang H
   4. Ward T
   5. Hillmann BM
   6. Al-Ghalith GA
   7. Travis DA
   8. Long HT
   9. Tuan BV
   10. Minh VV
   11. Cabana F
   12. Nadler T
   13. Toddes B
   14. Murphy T
   15. Glander KE
   16. Johnson TJ
   17. Knights D

   (2016) Captivity humanizes the primate microbiome

   PNAS 113:10376–10381.

   https://doi.org/10.1073/pnas.1521835113

   • PubMed
   • Google Scholar
9. 1. Comeau AM
   2. Douglas GM
   3. Langille MGI

   (2017) Microbiome Helper: a Custom and Streamlined Workflow for Microbiome Research

   MSystems 2:e00127-16.

   https://doi.org/10.1128/mSystems.00127-16

   • PubMed
   • Google Scholar
10. 1. Diaz J
    2. Reese AT

    (2021) Possibilities and limits for using the gut microbiome to improve captive animal health

    Animal Microbiome 3:89.

    https://doi.org/10.1186/s42523-021-00155-8

    • PubMed
    • Google Scholar
11. Software

    1. Dillard BA

    (2022a) User Manual for ANCOM, version swh:1:rev:c40aafdc22d65a04645d1b31dfea7c23a0d4d4dc

    Software Heritage.

    https://archive.softwareheritage.org/swh:1:dir:2373d6a258995af1021fabb61822def3663fd554;origin=https://github.com/FrederickHuangLin/ANCOM;visit=swh:1:snp:1898c2efe98badb4b1da5b9a08ef8fb38082c1ae;anchor=swh:1:rev:c40aafdc22d65a04645d1b31dfea7c23a0d4d4dc
12. Software

    1. Dillard BA

    (2022b) MicrobiotaUrbanization, version swh:1:rev:85220569457459d2c1c9dd4d467236eab75e2898

    Software Heritage.

    https://archive.softwareheritage.org/swh:1:dir:e20dd87ac52df1810f97df6949e00b2e7f97fd44;origin=https://github.com/briandill2/MicrobiotaUrbanization;visit=swh:1:snp:d9349ac37776e1a79b8da040e86b8ce999b19268;anchor=swh:1:rev:85220569457459d2c1c9dd4d467236eab75e2898
13. 1. Houtz JL
    2. Sanders JG
    3. Denice A
    4. Moeller AH

    (2021) Predictable and host-species specific humanization of the gut microbiota in captive primates

    Molecular Ecology 30:3677–3687.

    https://doi.org/10.1111/mec.15994

    • PubMed
    • Google Scholar
14. 1. Kalnay E
    2. Cai M

    (2003) Impact of urbanization and land-use change on climate

    Nature 423:528–531.

    https://doi.org/10.1038/nature01675

    • PubMed
    • Google Scholar
15. 1. Koeppel KN
    2. Bertschinger H
    3. van Vuuren M
    4. Picard J
    5. Steiner J
    6. Williams D
    7. Cardwell J

    (2006) The use of a probiotic in captive cheetahs (Acinonyx jubatus

    Journal of the South African Veterinary Association 77:127–130.

    https://doi.org/10.4102/jsava.v77i3.359

    • PubMed
    • Google Scholar
16. 1. Ley RE
    2. Hamady M
    3. Lozupone C
    4. Turnbaugh PJ
    5. Ramey RR
    6. Bircher JS
    7. Schlegel ML
    8. Tucker TA
    9. Schrenzel MD
    10. Knight R
    11. Gordon JI

    (2008) Evolution of mammals and their gut microbes

    Science (New York, N.Y.) 320:1647–1651.

    https://doi.org/10.1126/science.1155725

    • PubMed
    • Google Scholar
17. 1. Li H
    2. Li T
    3. Li X
    4. Wang G
    5. Lin Q
    6. Qu J

    (2018) Gut Microbiota in Tibetan Herdsmen Reflects the Degree of Urbanization

    Frontiers in Microbiology 9:1745.

    https://doi.org/10.3389/fmicb.2018.01745

    • PubMed
    • Google Scholar
18. 1. Lokmer A
    2. Aflalo S
    3. Amougou N
    4. Lafosse S
    5. Froment A
    6. Tabe FE
    7. Poyet M
    8. Groussin M
    9. Said-Mohamed R
    10. Ségurel L

    (2020) Response of the human gut and saliva microbiome to urbanization in Cameroon

    Scientific Reports 10:1–15.

    https://doi.org/10.1038/s41598-020-59849-9

    • PubMed
    • Google Scholar
19. 1. Mandal S
    2. Van Treuren W
    3. White RA
    4. Eggesbø M
    5. Knight R
    6. Peddada SD

    (2015) Analysis of composition of microbiomes: a novel method for studying microbial composition

    Microbial Ecology in Health and Disease 26:27663.

    https://doi.org/10.3402/mehd.v26.27663

    • PubMed
    • Google Scholar
20. 1. McKenzie VJ
    2. Song SJ
    3. Delsuc F
    4. Prest TL
    5. Oliverio AM
    6. Korpita TM
    7. Alexiev A
    8. Amato KR
    9. Metcalf JL
    10. Kowalewski M
    11. Avenant NL
    12. Link A
    13. Di Fiore A
    14. Seguin-Orlando A
    15. Feh C
    16. Orlando L
    17. Mendelson JR
    18. Sanders J
    19. Knight R

    (2017) The Effects of Captivity on the Mammalian Gut Microbiome

    Integrative and Comparative Biology 57:690–704.

    https://doi.org/10.1093/icb/icx090

    • PubMed
    • Google Scholar
21. 1. McMurdie PJ
    2. Holmes S

    (2013) phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data

    PLOS ONE 8:e61217.

    https://doi.org/10.1371/journal.pone.0061217

    • PubMed
    • Google Scholar
22. 1. Moeller AH
    2. Suzuki TA
    3. Lin D
    4. Lacey EA
    5. Wasser SK
    6. Nachman MW

    (2017) Dispersal limitation promotes the diversification of the mammalian gut microbiota

    PNAS 114:13768–13773.

    https://doi.org/10.1073/pnas.1700122114

    • PubMed
    • Google Scholar
23. 1. Moeller AH
    2. Gomes-Neto JC
    3. Mantz S
    4. Kittana H
    5. Segura Munoz RR
    6. Schmaltz RJ
    7. Ramer-Tait AE
    8. Nachman MW

    (2019) Experimental Evidence for Adaptation to Species-Specific Gut Microbiota in House Mice

    MSphere 4:e00387-19.

    https://doi.org/10.1128/mSphere.00387-19

    • PubMed
    • Google Scholar
24. 1. Muegge BD
    2. Kuczynski J
    3. Knights D
    4. Clemente JC
    5. González A
    6. Fontana L
    7. Henrissat B
    8. Knight R
    9. Gordon JI

    (2011) Diet drives convergence in gut microbiome functions across mammalian phylogeny and within humans

    Science (New York, N.Y.) 332:970–974.

    https://doi.org/10.1126/science.1198719

    • PubMed
    • Google Scholar
25. 1. Ochman H
    2. Worobey M
    3. Kuo C-H
    4. Ndjango J-BN
    5. Peeters M
    6. Hahn BH
    7. Hugenholtz P

    (2010) Evolutionary relationships of wild hominids recapitulated by gut microbial communities

    PLOS Biology 8:e1000546.

    https://doi.org/10.1371/journal.pbio.1000546

    • PubMed
    • Google Scholar
26. 1. Reese AT
    2. Chadaideh KS
    3. Diggins CE
    4. Schell LD
    5. Beckel M
    6. Callahan P
    7. Ryan R
    8. Emery Thompson M
    9. Carmody RN

    (2021) Effects of domestication on the gut microbiota parallel those of human industrialization

    eLife 10:e60197.

    https://doi.org/10.7554/eLife.60197

    • PubMed
    • Google Scholar
27. 1. Round JL
    2. Mazmanian SK

    (2010) Inducible Foxp3+ regulatory T-cell development by a commensal bacterium of the intestinal microbiota

    PNAS 107:12204–12209.

    https://doi.org/10.1073/pnas.0909122107

    • PubMed
    • Google Scholar
28. 1. Sarkar A
    2. Harty S
    3. Johnson KV-A
    4. Moeller AH
    5. Archie EA
    6. Schell LD
    7. Carmody RN
    8. Clutton-Brock TH
    9. Dunbar RIM
    10. Burnet PWJ

    (2020) Microbial transmission in animal social networks and the social microbiome

    Nature Ecology & Evolution 4:1020–1035.

    https://doi.org/10.1038/s41559-020-1220-8

    • PubMed
    • Google Scholar
29. 1. Song SJ
    2. Sanders JG
    3. Delsuc F
    4. Metcalf J
    5. Amato K
    6. Taylor MW
    7. Mazel F
    8. Lutz HL
    9. Winker K
    10. Graves GR
    11. Humphrey G
    12. Gilbert JA
    13. Hackett SJ
    14. White KP
    15. Skeen HR
    16. Kurtis SM
    17. Withrow J
    18. Braile T
    19. Miller M
    20. McCracken KG
    21. Maley JM
    22. Ezenwa VO
    23. Williams A
    24. Blanton JM
    25. McKenzie VJ
    26. Knight R

    (2020) Comparative Analyses of Vertebrate Gut Microbiomes Reveal Convergence between Birds and Bats

    MBio 11:e02901-19.

    https://doi.org/10.1128/mBio.02901-19

    • PubMed
    • Google Scholar
30. 1. Sugden S
    2. Sanderson D
    3. Ford K
    4. Stein LY
    5. St Clair CC

    (2020) An altered microbiome in urban coyotes mediates relationships between anthropogenic diet and poor health

    Scientific Reports 10:1–4.

    https://doi.org/10.1038/s41598-020-78891-1

    • PubMed
    • Google Scholar
31. 1. Sun L
    2. Chen J
    3. Li Q
    4. Huang D

    (2020) Dramatic uneven urbanization of large cities throughout the world in recent decades

    Nature Communications 11:1–9.

    https://doi.org/10.1038/s41467-020-19158-1

    • PubMed
    • Google Scholar
32. 1. Teyssier A
    2. Matthysen E
    3. Hudin NS
    4. de Neve L
    5. White J
    6. Lens L

    (2020) Diet contributes to urban-induced alterations in gut microbiota: experimental evidence from a wild passerine

    Proceedings. Biological Sciences 287:20192182.

    https://doi.org/10.1098/rspb.2019.2182

    • PubMed
    • Google Scholar
33. 1. Tigas LA
    2. Van Vuren DH
    3. Sauvajot RM

    (2002) Behavioral responses of bobcats and coyotes to habitat fragmentation and corridors in an urban environment

    Biological Conservation 108:299–306.

    https://doi.org/10.1016/S0006-3207(02)00120-9

    • Google Scholar
34. 1. Trevelline BK
    2. Moeller AH

    (2022) Robustness of Mammalian Gut Microbiota to Humanization in Captivity

    Frontiers in Ecology and Evolution 9:785089.

    https://doi.org/10.3389/fevo.2021.785089

    • PubMed
    • Google Scholar
35. 1. Turnbaugh PJ
    2. Ley RE
    3. Mahowald MA
    4. Magrini V
    5. Mardis ER
    6. Gordon JI

    (2006) An obesity-associated gut microbiome with increased capacity for energy harvest

    Nature 444:1027–1031.

    https://doi.org/10.1038/nature05414

    • PubMed
    • Google Scholar
36. 1. Wu GD
    2. Chen J
    3. Hoffmann C
    4. Bittinger K
    5. Chen Y-Y
    6. Keilbaugh SA
    7. Bewtra M
    8. Knights D
    9. Walters WA
    10. Knight R
    11. Sinha R
    12. Gilroy E
    13. Gupta K
    14. Baldassano R
    15. Nessel L
    16. Li H
    17. Bushman FD
    18. Lewis JD

    (2011) Linking long-term dietary patterns with gut microbial enterotypes

    Science (New York, N.Y.) 334:105–108.

    https://doi.org/10.1126/science.1208344

    • PubMed
    • Google Scholar
37. 1. Yatsunenko T
    2. Rey FE
    3. Manary MJ
    4. Trehan I
    5. Dominguez-Bello MG
    6. Contreras M
    7. Magris M
    8. Hidalgo G
    9. Baldassano RN
    10. Anokhin AP
    11. Heath AC
    12. Warner B
    13. Reeder J
    14. Kuczynski J
    15. Caporaso JG
    16. Lozupone CA
    17. Lauber C
    18. Clemente JC
    19. Knights D
    20. Knight R
    21. Gordon JI

    (2012) Human gut microbiome viewed across age and geography

    Nature 486:222–227.

    https://doi.org/10.1038/nature11053

    • PubMed
    • Google Scholar

Author details

1. Brian A Dillard

   Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, United States

   Contribution

   Formal analysis, Investigation, Methodology, Validation, Visualization, Writing - original draft, Writing - review and editing

   For correspondence

   bd429@cornell.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-1845-2980

2. Albert K Chung

   Princeton University, Princeton, NJ, United States

   Contribution

   Data curation, Investigation, Methodology, Resources, Writing - review and editing

   Competing interests

   No competing interests declared

3. Alex R Gunderson

   Tulane University, Tulane, United States

   Contribution

   Conceptualization, Data curation, Investigation, Methodology, Project administration, Resources, Writing - review and editing

   Competing interests

   No competing interests declared

4. Shane C Campbell-Staton

   Princeton University, Princeton, NJ, United States

   Contribution

   Conceptualization, Data curation, Investigation, Methodology, Project administration, Resources, Writing - review and editing

   Competing interests

   No competing interests declared

5. Andrew H Moeller

   Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing - original draft, Writing - review and editing

   For correspondence

   ahm226@cornell.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8377-4647

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