Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Synchrony and idiosyncrasy in the gut microbiome of wild baboons

Nature Ecology & Evolution volume 6, pages 955–964 (2022)Cite this article

Subjects

Abstract

Human gut microbial dynamics are highly individualized, making it challenging to link microbiota to health and to design universal microbiome therapies. This individuality is typically attributed to variation in host genetics, diets, environments and medications but it could also emerge from fundamental ecological forces that shape microbiota more generally. Here, we leverage extensive gut microbial time series from wild baboons—hosts who experience little interindividual dietary and environmental heterogeneity—to test whether gut microbial dynamics are synchronized across hosts or largely idiosyncratic. Despite their shared lifestyles, baboon microbiota were only weakly synchronized. The strongest synchrony occurred among baboons living in the same social group, probably because group members range over the same habitat and simultaneously encounter the same sources of food and water. However, this synchrony was modest compared to each host’s personalized dynamics. In support, host-specific factors, especially host identity, explained, on average, more than three times the deviance in longitudinal dynamics compared to factors shared with social group members and ten times the deviance of factors shared across the host population. These results contribute to mounting evidence that highly idiosyncratic gut microbiomes are not an artefact of modern human environments and that synchronizing forces in the gut microbiome (for example, shared environments, diets and microbial dispersal) are not strong enough to overwhelm key drivers of microbiome personalization, such as host genetics, priority effects, horizontal gene transfer and functional redundancy.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Baboons in Amboseli experience shared environments at multiple scales.
Fig. 2: Baboons show population-wide, cyclical shifts in microbiome community composition across seasons and years.
Fig. 3: Baboons exhibit largely idiosyncratic gut microbial compositions and dynamics.
Fig. 4: Multilevel modelling identifies idiosyncratic microbial dynamics.
Fig. 5: Microbiome taxon heritability is associated with idiosyncratic dynamics.

Similar content being viewed by others

Data availability

The 16S rRNA gene sequences are deposited on EBI-ENA (project ERP119849) and Qiita (study 12949; ref. 64). Note that our research permission from Kenya Wildlife Service prohibits third-party sharing of the biological samples themselves.

Code availability

Analysed data and code are available on the JRB’s Open Science Framework/GitHub repository at https://doi.org/10.17605/OSF.IO/ERDXA

References

  1. Kolodny, O. et al. Coordinated change at the colony level in fruit bat fur microbiomes through time. Nat. Ecol. Evol. 3, 116–124 (2019).

    Article  PubMed  Google Scholar 

  2. Schlomann, B. H. & Parthasarathy, R. Timescales of gut microbiome dynamics. Curr. Opin. Microbiol. 50, 56–63 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  3. Koch, H. & Schmid-Hempel, P. Socially transmitted gut microbiota protect bumble bees against an intestinal parasite. Proc. Natl Acad. Sci. USA 108, 19288–19292 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Finnicum, C. T. et al. Cohabitation is associated with a greater resemblance in gut microbiota which can impact cardiometabolic and inflammatory risk. BMC Microbiol. 19, 230 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Bashan, A. et al. Universality of human microbial dynamics. Nature 534, 259–262 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Costello, E. K., Stagaman, K., Dethlefsen, L., Bohannan, B. J. & Relman, D. A. The application of ecological theory toward an understanding of the human microbiome. Science 336, 1255–1262 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Miller, E. T., Svanback, R. & Bohannan, B. J. Microbiomes as metacommunities: understanding host-associated microbes through metacommunity ecology. Trends Ecol. Evol. 33, 926–935 (2018).

    Article  PubMed  Google Scholar 

  8. Bjork, J., Díez-Vives, C., Astudillo-García, C., Archie, E. A. & Montoya, J. M. Vertical transmission of sponge microbiota is inconsistent and unfaithful. Nat. Ecol. Evol. 3, 1172–1183 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Sieber, M. et al. Neutrality in the metaorganism. PLoS Biol. 17, e3000298 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Tredennick, A. T., de Mazancourt, C., Loreau, M. & Adler, P. B. Environmental responses, not species interactions, determine synchrony of dominant species in semiarid grasslands. Ecology 98, 971–981 (2017).

    Article  PubMed  Google Scholar 

  11. Loreau, M. & de Mazancourt, C. Species synchrony and its drivers: neutral and nonneutral community dynamics in fluctuating environments. Am. Nat. 172, E48–E66 (2008).

    Article  PubMed  Google Scholar 

  12. Isbell, F. I., Polley, H. W. & Wilsey, B. J. Biodiversity, productivity and the temporal stability of productivity: patterns and processes. Ecol. Lett. 12, 443–451 (2009).

    Article  PubMed  Google Scholar 

  13. Hector, A. et al. General stabilizing effects of plant diversity on grassland productivity through population asynchrony and overyielding. Ecology 91, 2213–2220 (2010).

    Article  CAS  PubMed  Google Scholar 

  14. de Mazancourt, C. et al. Predicting ecosystem stability from community composition and biodiversity. Ecol. Lett. 16, 617–625 (2013).

    Article  PubMed  Google Scholar 

  15. Gross, K. et al. Species richness and the temporal stability of biomass production: a new analysis of recent biodiversity experiments. Am. Nat. 183, 1–12 (2014).

    Article  PubMed  Google Scholar 

  16. Louca, S. et al. Function and functional redundancy in microbial systems. Nat. Ecol. Evol. 2, 936–943 (2018).

    Article  PubMed  Google Scholar 

  17. Rainey, P. B. & Quistad, S. D. Toward a dynamical understanding of microbial communities. Philos. Trans. R. Soc. B 375, 20190248 (2020).

    Article  CAS  Google Scholar 

  18. Martiny, J. B., Jones, S. E., Lennon, J. T. & Martiny, A. C. Microbiomes in light of traits: a phylogenetic perspective. Science 350, aac9323 (2015).

    Article  PubMed  Google Scholar 

  19. Debray, R. et al. Priority effects in microbiome assembly. Nat. Rev. Microbiol. 20, 109–121 (2022).

    Article  CAS  PubMed  Google Scholar 

  20. Risely, A., Wilhelm, K., Clutton-Brock, T., Manser, M. B. & Sommer, S. Diurnal oscillations in gut bacterial load and composition eclipse seasonal and lifetime dynamics in wild meerkats. Nat. Commun. 12, 6017 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Franzosa, E. A. et al. Identifying personal microbiomes using metagenomic codes. Proc. Natl Acad. Sci. USA 112, E2930–E2938 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Faith, J. J. et al. The long-term stability of the human gut microbiota. Science 341, 1237439 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  23. Bik, E. M. et al. Marine mammals harbor unique microbiotas shaped by and yet distinct from the sea. Nat. Commun. 7, 10516 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Caporaso, J. G. et al. Moving pictures of the human microbiome. Genome Biol. 12, R50 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Costello, E. K. et al. Bacterial community variation in human body habitats across space and time. Science 326, 1694–1697 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Flores, G. E. et al. Temporal variability is a personalized feature of the human microbiome. Genome Biol. 15, 531 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  27. Johnson, A. J. et al. Daily sampling reveals personalized diet–microbiome associations in humans. Cell Host Microbe 25, 789–802 (2019).

    Article  CAS  PubMed  Google Scholar 

  28. Smits, S. A., Marcobal, A., Higginbottom, S., Sonnenburg, J. L. & Kashyap, P. C. Individualized responses of gut microbiota to dietary intervention modeled in humanized mice. mSystems 1, e00098 (2016).

  29. Rothschild, D. et al. Environment dominates over host genetics in shaping human gut microbiota. Nature 555, 210–215 (2018).

    Article  CAS  PubMed  Google Scholar 

  30. Falony, G. et al. Population-level analysis of gut microbiome variation. Science 352, 560–564 (2016).

    Article  CAS  PubMed  Google Scholar 

  31. Zhernakova, A. et al. Population-based metagenomics analysis reveals markers for gut microbiome composition and diversity. Science 352, 565–569 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Grieneisen, L. et al. Gut microbiome heritability is nearly universal but environmentally contingent. Science 373, 181–186 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Alberts S. C. & Altmann, J. in Long-Term Field Studies of Primates (eds Kappeler, P. & Watts, D. P.) 261–287 (Springer, 2012).

  34. Ren, T., Grieneisen, L., Alberts, S. C., Archie, E. A. & Wu, M. Development, diet, and dynamism: longitudinal and cross-sectional predictors of gut microbial communities in wild baboons. Environ. Microbiol. 18, 1312–1325 (2016).

    Article  PubMed  Google Scholar 

  35. Grieneisen, L. et al. Genes, geology, and germs: gut microbiota across a primate hybrid zone are explained by site soil properties, not host species. Proc. R. Soc. B 286, 20190431 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Hicks, A. L. et al. Gut microbiomes of wild great apes fluctuate seasonally in response to diet. Nat. Commun. 9, 1786 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Orkin, J. D. et al. Seasonality of the gut microbiota of free-ranging white-faced capuchins in a tropical dry forest. ISME J. 13, 183–196 (2019).

    Article  CAS  PubMed  Google Scholar 

  38. Baniel, A. et al. Seasonal shifts in the gut microbiome indicate plastic responses to diet in wild geladas. Microbiome 9, 26 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Mellard, J. P., Audoye, P. & Loreau, M. Seasonal patterns in species diversity across biomes. Ecology 100, e02627 (2019).

    Article  PubMed  Google Scholar 

  40. Sloan, W. T. et al. Quantifying the roles of immigration and chance in shaping prokaryote community structure. Environ. Microbiol. 8, 732–740 (2006).

    Article  PubMed  Google Scholar 

  41. Sloan, W. T., Woodcock, S., Lunn, M., Head, I. M. & Curtis, T. P. Modeling taxa-abundance distributions in microbial communities using environmental sequence data. Microb. Ecol. 53, 443–455 (2007).

    Article  PubMed  Google Scholar 

  42. Tung, J. et al. Social networks predict gut microbiome composition in wild baboons. eLife 4, e05224 (2015).

    Article  PubMed Central  Google Scholar 

  43. Moeller, A. H. et al. Social behavior shapes the chimpanzee pan-microbiome. Sci. Adv. 2, e1500997 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  44. Lax, S. et al. Longitudinal analysis of microbial interaction between humans and the indoor environment. Science 345, 1048–1052 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Amato, K. R. et al. Patterns in gut microbiota similarity associated with degree of sociality among sex classes of a neotropical primate. Microb. Ecol. 74, 250–258 (2017).

    Article  PubMed  Google Scholar 

  46. Amato, K. R. et al. The role of gut microbes in satisfying the nutritional demands of adult and juvenile wild, black howler monkeys (Alouatta pigra). Am. J. Phys. Anthropol. 155, 652–664 (2014).

    Article  PubMed  Google Scholar 

  47. Perofsky, A. C., Leriw, R. J., Abondano, L. A., Di Fiore, A. & Meyers, L. A. Hierarchical social networks shape gut microbial composition in wild Verreaux’s sifaka. Proc. R. Soc. B 284, 20172274 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  48. Silk, J. B. Activities and feeding behavior of free-ranging pregnant baboons. Int. J. Primatol. 8, 593–613 (1987).

    Article  Google Scholar 

  49. Altmann, S. A. Foraging for Survival: Yearling Baboons in Africa (Univ. Chicago Press, 1998).

  50. Bronikowski, A. M. & Altmann, J. Foraging in a variable environment: weather patterns and the behavioral ecology of baboons. Behav. Ecol. Sociobiol. 39, 11–25 (1996).

    Article  Google Scholar 

  51. Muruthi, P., Altmann, J. & Altmann, S. Resource base, parity and reproductive condition affect females’ feeding time and nutrient intake within and between groups of a baboon population. Oecologia 87, 467–472 (1991).

    Article  PubMed  Google Scholar 

  52. Shopland, J. M. Food quality, spatial deployment, and the intensity of feeding interference in yellow baboons (Papio cynocephalus). Behav. Ecol. Sociobiol. 21, 149–156 (1987).

    Article  Google Scholar 

  53. Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Morton, J. T. et al. Establishing microbial composition measurement standards with reference frames. Nat. Commun. 10, 2719 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Gloor, G. B., Macklaim, J. M., Pawlowsky-Glahn, V. & Egozcue, J. J. Microbiome datasets are compositional: and this is not optional. Front. Microbiol. 8, 2224 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  56. R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).

  57. Burns, A. R. et al. Contribution of neutral processes to the assembly of gut microbial communities in the zebrafish over host development. ISME J. 10, 655–664 (2016).

    Article  CAS  PubMed  Google Scholar 

  58. Sprockett D. tyRa: Build Models for Microbiome Data. R package version 0.1.0 https://danielsprockett.github.io/tyRa/articles/tyRa.html (2020).

  59. Oksanen J. et al. vegan: Community Ecology Package. R package version 2.5-7 (2020).

  60. Vieira-Silva, S. et al. Species–function relationships shape ecological properties of the human gut microbiome. Nat. Microbiol. 1, 16088 (2016).

    Article  CAS  PubMed  Google Scholar 

  61. Wood, S. N. Stable and efficient multiple smoothing parameter estimation for generalized additive models. J. Am. Stat. Assoc. 99, 673–686 (2004).

    Article  Google Scholar 

  62. Wood, S. N. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J. R. Stat. Soc. B 73, 3–36 (2011).

    Article  Google Scholar 

  63. Wood, S. N. Generalized Additive Models: An Introduction with R 2nd edn (CRC Press, 2017).

  64. Gonzalez, A. et al. Qiita: rapid, web-enabled microbiome meta-analysis. Nat. Methods 15, 796–798 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank J. Altmann for her essential role in stewarding the Amboseli Baboon Project and in collecting and maintaining the faecal samples used in this manuscript. This work was supported by the National Science Foundation (NSF) and the National Institutes of Health (NIH), especially NSF Rules of Life Award DEB1840223 (E.A.A. and J.A.G.) and the National Institute on Aging R21AG055777 (E.A.A. and R.B.) and NIH R01AG053330 (E.A.A.), NIH R01AG071684 (E.A.A.) and NIHR35 GM128716 (R.B.), the Duke University Population Research Institute P2C-HD065563 (pilot to J.T.), the University of Notre Dame’s Eck Institute for Global Health (E.A.A.) and the Notre Dame Environmental Change Initiative (E.A.A.). Since 2000, long-term data collection in Amboseli has been supported by NSF and NIH, including IOS1456832 (S.C.A.), IOS1053461 (E.A.A.), DEB1405308 (J.T.), IOS0919200 (S.C.A.), DEB0846286 (S.C.A.), DEB0846532 (S.C.A.), IBN0322781 (S.C.A.), IBN0322613 (S.C.A.), BCS0323553 (S.C.A.), BCS0323596 (S.C.A.), P01AG031719 (S.C.A.), R21AG049936 (J.T. and S.C.A.), R03AG045459 (J.T. and S.C.A.), R01AG034513 (S.C.A.), R01HD088558 (J.T.) and P30AG024361 (S.C.A.). We also especially thank the members of the Maasai pastoralist communities in the Amboseli-Longido areas. We thank the Kenya Wildlife Service, Kenya’s Wildlife Research & Training Institute, the National Council for Science, Technology and Innovation and the National Environment Management Authority for permission to conduct research and collect biological samples in Kenya. We also thank the University of Nairobi, Institute of Primate Research, National Museums of Kenya, the Enduimet Wildlife Management Area, Ker & Downey Safaris, Air Kenya and Safarilink for their cooperation and assistance in the field. We thank K. Pinc for managing and designing the database. We also thank T. Voyles, A. Dumaine, Y. Zhang, M. Rao, T. Vilgalys, A. Lea, N. Snyder-Mackler, P. Durst, J. Zussman, G. Chavez and R. Debray for contributing to faecal sample processing. Complete acknowledgements for the ABRP can be found online at https://amboselibaboons.nd.edu/acknowledgements/.

Author information

Authors and Affiliations

  1. Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, USA

    Johannes R. Björk, Mauna R. Dasari, David Jansen & Elizabeth A. Archie

  2. Program in Computational Biology and Bioinformatics, Duke University, Durham, NC, USA

    Kim Roche & Sayan Mukherjee

  3. Department of Genetics, Cell Biology, and Development, University of Minnesota, Minneapolis, MN, USA

    Laura Grieneisen, Trevor J. Gould & Ran Blekhman

  4. Department of Genetics, CHU Sainte Justine Research Center, Montréal, Quebec, Canada

    Jean-Christophe Grenier & Vania Yotova

  5. Research Center, Montreal Heart Institute, Montréal, Quebec, Canada

    Jean-Christophe Grenier

  6. Department of Pediatrics and the Scripps Institution of Oceanography, University of California, San Diego, San Diego, CA, USA

    Neil Gottel & Jack A. Gilbert

  7. Department of Biology, Duke University, Durham, NC, USA

    Laurence R. Gesquiere, Jacob B. Gordon, Susan C. Alberts & Jenny Tung

  8. Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA

    Niki H. Learn

  9. Amboseli Baboon Research Project, Amboseli National Park, Amboseli, Kenya

    Tim L. Wango, Raphael S. Mututua, J. Kinyua Warutere & Long’ida Siodi

  10. The Department of Veterinary Anatomy and Animal Physiology, University of Nairobi, Nairobi, Kenya

    Tim L. Wango

  11. Department of Medicine, Section of Genetic Medicine, University of Chicago, Chicago, IL, USA

    Luis B. Barreiro

  12. Department of Evolutionary Anthropology, Duke University, Durham, NC, USA

    Susan C. Alberts & Jenny Tung

  13. Duke University Population Research Institute, Duke University, Durham, NC, USA

    Susan C. Alberts & Jenny Tung

  14. Canadian Institute for Advanced Research, Toronto, Ontario, Canada

    Jenny Tung

  15. Department of Ecology, Evolution, and Behavior, University of Minnesota, Minneapolis, MN, USA

    Ran Blekhman

Authors
  1. Johannes R. Björk
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mauna R. Dasari
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kim Roche
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Laura Grieneisen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Trevor J. Gould
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jean-Christophe Grenier
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Vania Yotova
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Neil Gottel
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. David Jansen
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Laurence R. Gesquiere
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Jacob B. Gordon
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Niki H. Learn
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Tim L. Wango
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Raphael S. Mututua
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. J. Kinyua Warutere
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Long’ida Siodi
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Sayan Mukherjee
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Luis B. Barreiro
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Susan C. Alberts
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Jack A. Gilbert
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Jenny Tung
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Ran Blekhman
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Elizabeth A. Archie
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

E.A.A., J.R.B., L.B.B., R.B., J.A.G., S.M. and J.T. designed the research. E.A.A., S.C.A., R.B., M.R.D., L.G., J.B.G., L.R.G., N.G., S.M., V.Y., N.H.L., T.L.W., R.S.M., J.K.W., L.S., L.B.B. and J.T. produced the data. J.R.B., T.J.G., D.J., L.G. and J.-C.G. performed the bioinformatics. J.R.B., K.R. and S.M. performed the statistical analyses. E.A.A. and J.R.B. wrote the manuscript with important contributions from all authors.

Corresponding authors

Correspondence to Johannes R. Björk or Elizabeth A. Archie.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Ecology & Evolution thanks Elin Videvall, Lifeng Zhu and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Methods and Figs. 1–26.

Reporting Summary

Peer Review File

Supplementary Video 1

Animation of home range use by the five original social groups, as defined in Fig. 1. This animation shows the geographical location of each social group over time, with the x axis showing longitude and the y axis latitude. Each dot represents the average monthly longitude and latitude per social group and month. While solid dots represent the groups’ current position in the focal month and hydrological year, the hollow circles show previous locations, thus outlining each social group’s total home range area over time.

Supplementary Video 2

Animation of the microbiome PC1 and PC2 for baboons living in the five original social groups, as defined in Fig. 1. To make this animation, we first averaged the PC1 and PC2 sample scores by host and collection date, such that each host only has one value per collection date. We then ‘filled in’ missing collection dates and performed a 30-d sliding window analysis (step size = 1). Each frame (image) in the animation corresponds to one sliding window. The date in the top left corner corresponds to the first date of each window and each dot represents one individual host coloured by its social group.

Supplementary Tables

Supplementary Tables 1–9.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Björk, J.R., Dasari, M.R., Roche, K. et al. Synchrony and idiosyncrasy in the gut microbiome of wild baboons. Nat Ecol Evol 6, 955–964 (2022). https://doi.org/10.1038/s41559-022-01773-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41559-022-01773-4

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing