Skip to main content
Log in

Individual- and Species-Specific Skin Microbiomes in Three Different Estrildid Finch Species Revealed by 16S Amplicon Sequencing

  • Host Microbe Interactions
  • Published:
Microbial Ecology Aims and scope Submit manuscript

Abstract

An animals’ body is densely populated with bacteria. Although a large number of investigations on physiological microbial colonisation have emerged in recent years, our understanding of the composition, ecology and function of the microbiota remains incomplete. Here, we investigated whether songbirds have an individual-specific skin microbiome that is similar across different body regions. We collected skin microbe samples from three different bird species (Taeniopygia gutatta, Lonchura striata domestica and Stagonopleura gutatta) at two body locations (neck region, preen gland area). To characterise the skin microbes and compare the bacterial composition, we used high-throughput 16S rRNA amplicon sequencing. This method proved suitable for identifying the skin microbiome of birds, even though the bacterial load on the skin appeared to be relatively low. We found that across all species, the two evaluated skin areas of each individual harboured very similar microbial communities, indicative of an individual-specific skin microbiome. Despite experiencing the same environmental conditions and consuming the same diet, significant differences in the skin microbe composition were identified among the three species. The bird species differed both quantitatively and qualitatively regarding the observed bacterial taxa. Although each species harboured its own unique set of skin microbes, we identified a core skin microbiome among the studied species. As microbes are known to influence the host’s body odour, our findings of an individual-specific skin microbiome might suggest that the skin microbiome in birds is involved in the odour production and could encode information on the host’s genotype.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Archie EA, Theis KR (2011) Animal behaviour meets microbial ecology. Anim. Behav. 82:425–436

    Article  Google Scholar 

  2. McFall-Ngai M, Hadfield MG, Bosch TC et al (2013) Animals in a bacterial world, a new imperative for the life sciences. PNAS. 110:3229–3236

    Article  PubMed  Google Scholar 

  3. Flint HJ, Scott KP, Louis P, Duncan SH (2012) The role of the gut microbiota in nutrition and health. Nat. Rev. Gastroenterol. Hepatol. 9:577–589

    Article  PubMed  CAS  Google Scholar 

  4. Cogen AL, Yamasaki K, Sanchez KM et al (2010) Selective antimicrobial action is provided by phenol-soluble modulins derived from Staphylococcus epidermidis, a normal resident of the skin. J. Invest. Dermatol. 130:192–200

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  5. Naik S, Bouladoux N, Linehan JL et al (2015) Commensal–dendritic-cell interaction specifies a unique protective skin immune signature. Nature. 520:104–108

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  6. Ezenwa VO, Gerardo NM, Inouye DW, Medina M, Xavier JB (2012) Animal behavior and the microbiome. Science. 338:198–199

    Article  PubMed  CAS  Google Scholar 

  7. Ezenwa VO, Williams AE (2014) Microbes and animal olfactory communication: where do we go from here? BioEssays. 36:847–854

    Article  PubMed  Google Scholar 

  8. Bravo JA, Forsythe P, Chew MV et al (2011) Ingestion of Lactobacillus strain regulates emotional behavior and central GABA receptor expression in a mouse via the vagus nerve. PNAS. 108:16050–16055

    Article  PubMed  Google Scholar 

  9. Sharon G, Segal D, Ringo JM et al (2010) Commensal bacteria play a role in mating preference of Drosophila melanogaster. PNAS. 107:20051–20056

    Article  PubMed  Google Scholar 

  10. Bordenstein SR, O'hara FP, Werren JH (2001) Wolbachia-induced incompatibility precedes other hybrid incompatibilities in Nasonia. Nature. 409:707–710

    Article  PubMed  CAS  Google Scholar 

  11. Singh PB, Herbert J, Roser B, Arnott L, Tucker DK, Brown RE (1990) Rearing rats in a germ-free environment eliminates their odors of individuality. J. Chem. Ecol. 16:1667–1682

    Article  PubMed  CAS  Google Scholar 

  12. Albone ES, Gosden PE, Ware GC (1977) Bacteria as a source of chemical signals in mammals. Chemical signals in vertebrates. Springer, New York

    Google Scholar 

  13. Albone ES, Perry GC (1976) Anal sac secretion of the red fox, Vulpes vulpes; volatile fatty acids and diamines: implications for a fermentation hypothesis of chemical recognition. J. Chem. Ecol. 2:101–111

    Article  CAS  Google Scholar 

  14. Theis KR, Schmidt TM, Holekamp KE (2012) Evidence for a bacterial mechanism for group-specific social odors among hyenas. Sci. Rep. 2:615

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  15. Theis KR, Venkataraman A, Dycus JA et al (2013) Symbiotic bacteria appear to mediate hyena social odors. PNAS. 110:19832–19837

    Article  PubMed  CAS  Google Scholar 

  16. Gorman ML (1976) A mechanism for individual recognition by odour in Herpestes auropunctatus (Carnivora: Viverridae). Anim. Behav. 24:141–145

    Article  Google Scholar 

  17. Leclaire S, Nielsen JF, Drea CM (2014) Bacterial communities in meerkat anal scent secretions vary with host sex, age, and group membership. Behav. Ecol. 25:996–1004

    Article  Google Scholar 

  18. Leclaire S, Jacob S, Greene LK et al (2017) Social odours covary with bacterial community in the anal secretions of wild meerkats. Sci. Rep. 7:3240

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. Rennie PJ, Gower DB, Holland KT, Mallet AI, Watkins WJ (1990) The skin microflora and the formation of human axillary odour. Int. J. Cosmet. Sci. 12:197–207

    Article  PubMed  CAS  Google Scholar 

  20. Fredrich E, Barzantny H, Brune I, Tauch A (2013) Daily battle against body odor: towards the activity of the axillary microbiota. Trends Microbiol. 21:305–312

    Article  PubMed  CAS  Google Scholar 

  21. Kong HH (2011) Skin microbiome: genomics-based insights into the diversity and role of skin microbes. Trends Mol. Med. 17:320–328

    Article  PubMed  PubMed Central  Google Scholar 

  22. Bouslimani A, Porto C, Rath CM et al (2015) Molecular cartography of the human skin surface in 3D. PNAS. 112:E2120–E2129

    Article  PubMed  CAS  Google Scholar 

  23. Grice EA, Kong HH, Conlan S et al (2009) Topographical and temporal diversity of the human skin microbiome. Science. 324:1190–1192

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. Grice EA, Segre JA (2011) The skin microbiome. Nat. Rev. Microbiol. 9:244–253

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. Lax S, Smith DP, Hampton-Marcell J et al (2014) Longitudinal analysis of microbial interaction between humans and the indoor environment. Science. 345:1048–1052

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. Fitzpatrick BM, Allison AL (2014) Similarity and differentiation between bacteria associated with skin of salamanders (Plethodon jordani) and free-living assemblages. FEMS Microbiol. Ecol. 88:482–494

    Article  PubMed  CAS  Google Scholar 

  27. Roggenbuck M, Schnell IB, Blom N et al (2014) The microbiome of new world vultures. Nat. Commun. 5:5498

    Article  PubMed  CAS  Google Scholar 

  28. Funkhouser LJ, Bordenstein SR (2013) Mom knows best: the universality of maternal microbial transmission. PLoS Biol. 11:e1001631

  29. Kaltenpoth M, Göttler W, Herzner G, Strohm E (2005) Symbiotic bacteria protect wasp larvae from fungal infestation. Curr. Biol. 15:475–479

    Article  PubMed  CAS  Google Scholar 

  30. Banning JL, Weddle AL, Wahl III GW et al (2008) Antifungal skin bacteria, embryonic survival, and communal nesting in four-toed salamanders, Hemidactylium scutatum. Oecologia. 156:423–429

    Article  PubMed  Google Scholar 

  31. Tung J, Barreiro LB, Burns MB et al (2015) Social networks predict gut microbiome composition in wild baboons. elife. 4:e05224

    Article  PubMed Central  Google Scholar 

  32. Ruiz-de-Castañeda R, Vela AI, Lobato E, Briones V, Moreno J (2011) Bacterial loads on eggshells of the pied flycatcher: environmental and maternal factors. Condor. 113:200–208

    Article  Google Scholar 

  33. Kulkarni S, Heeb P (2007) Social and sexual behaviours aid transmission of bacteria in birds. Behav. Process. 74:88–92

    Article  Google Scholar 

  34. Howard JC (1977) H-2 and mating preferences. Nature. 266:406–408

    Article  Google Scholar 

  35. Goodrich JK, Waters JL, Poole AC et al (2014) Human genetics shape the gut microbiome. Cell. 159:789–799

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. Costello EK, Lauber CL, Hamady M et al (2009) Bacterial community variation in human body habitats across space and time. Science. 326:1694–1697

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. Larsen A, Tao Z, Bullard SA, Arias CR (2013) Diversity of the skin microbiota of fishes: evidence for host species specificity. FEMS Microbiol. Ecol. 85:483–494

    Article  PubMed  CAS  Google Scholar 

  38. Chiarello M, Villéger S, Bouvier C, Bettarel Y, Bouvier T (2015) High diversity of skin-associated bacterial communities of marine fishes is promoted by their high variability among body parts, individuals and species. FEMS Microbiol. Ecol. 91:fiv061

    Article  PubMed  CAS  Google Scholar 

  39. McKenzie VJ, Bowers RM, Fierer N, Knight R, Lauber CL (2012) Co-habiting amphibian species harbor unique skin bacterial communities in wild populations. ISME J. 6:588–596

    Article  PubMed  CAS  Google Scholar 

  40. Kueneman JG, Parfrey LW, Woodhams DC et al (2014) The amphibian skin-associated microbiome across species, space and life history stages. Mol. Ecol. 23:1238–1250

    Article  PubMed  Google Scholar 

  41. Apprill A, Mooney TA, Lyman E, Stimpert AK, Rappé MS (2011) Humpback whales harbour a combination of specific and variable skin bacteria. Environ. Microbiol. Rep. 3:223–232

    Article  PubMed  CAS  Google Scholar 

  42. Whittaker DJ, Gerlach NM, Slowinski SP et al (2016) Social environment has a primary influence on the microbial and odor profiles of a chemically signaling songbird. Front. Ecol. Evol. 4:90

    Article  Google Scholar 

  43. Gunderson AR, Forsyth MH, Swaddle JP (2009) Evidence that plumage bacteria influence feather coloration and body condition of eastern bluebirds Sialia sialis. J. Avian Biol. 40:440–447

    Article  Google Scholar 

  44. Shawkey MD, Pillai SR, Hill GE (2003) Chemical warfare? Effects of uropygial oil on feather degrading bacteria. J. Avian Biol. 34:345–349

    Article  Google Scholar 

  45. Bisson IA, Marra PP, Burtt EH, Sikaroodi M, Gillevet PM (2007) A molecular comparison of plumage and soil bacteria across biogeographic, ecological, and taxonomic scales. Microb. Ecol. 54:65–81

    Article  PubMed  Google Scholar 

  46. Krause ET, Brummel C, Kohlwey S, Baier MC, Müller C, Bonadonna F, Caspers BA (2014) Differences in olfactory species recognition in the females of two Australian songbird species. Behav. Ecol. Sociobiol. 68:1819–1827

    Article  Google Scholar 

  47. Mihailova M, Berg ML, Buchanan KL, Bennett AT (2014) Odour-based discrimination of subspecies, species and sexes in an avian species complex, the crimson rosella. Anim. Behav. 95:155–164

    Article  Google Scholar 

  48. Bonadonna F, Nevitt GA (2004) Partner-specific odor recognition in an Antarctic seabird. Science. 306:835–835

    Article  PubMed  CAS  Google Scholar 

  49. Bonadonna F, Mardon J (2010) One house two families: petrel squatters get a sniff of low-cost breeding opportunities. Ethology. 116:176–182

    Article  Google Scholar 

  50. Leclaire S, Strandh M, Mardon J, Westerdahl H, Bonadonna F (2017) Odour-based discrimination of similarity at the major histocompatibility complex in birds. Proc. R. Soc. Lond. B. 284:20162466

    Article  Google Scholar 

  51. Krause ET, Krüger O, Kohlmeier P, Caspers BA (2012) Olfactory kin recognition in a songbird. Biol. Lett. 8:327–329

    Article  PubMed  PubMed Central  Google Scholar 

  52. Krause ET, Caspers BA (2012) Are olfactory cues involved in nest recognition in two social species of estrildid finches? PLoS One. 7:e36615

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  53. Caspers BA, Hagelin JC, Paul M et al (2017) Zebra finch chicks recognise parental scent, and retain chemosensory knowledge of their genetic mother, even after egg cross-fostering. Sci. Rep. 7:12859

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  54. Nickel R, Schummer A, Seiferle E (2004) Anatomie der Vögel. Verlag Paul Parey, Stuttgart

    Google Scholar 

  55. Forstmeier W, Segelbacher G, Mueller JC, Kempenaers B (2007) Genetic variation and differentiation in captive and wild zebra finches (Taeniopygia guttata). Mol. Ecol. 16:4039–4050

    Article  PubMed  CAS  Google Scholar 

  56. Golüke S, Caspers BA (2017) Sex-specific differences in preen gland size of zebra finches during the course of breeding. Auk. 134:821–831

    Article  Google Scholar 

  57. Tuttle EM, Sebastian PJ, Posto AL et al (2014) Variation in preen oil composition pertaining to season, sex, and genotype in the polymorphic white-throated sparrow. J. Chem. Ecol. 40:1025–1038

    Article  PubMed  CAS  Google Scholar 

  58. Quigley L, O’Sullivan O, Beresford TP et al (2012) A comparison of methods used to extract bacterial DNA from raw milk and raw milk cheese. J. Appl. Microbiol. 113:96–105

    Article  PubMed  CAS  Google Scholar 

  59. Yuan S, Cohen DB, Ravel J, Abdo Z, Forney LJ (2012) Evaluation of methods for the extraction and purification of DNA from the human microbiome. PLoS One. 7:e33865

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  60. Ariefdjohan MW, Savaiano DA, Nakatsu CH (2010) Comparison of DNA extraction kits for PCR-DGGE analysis of human intestinal microbial communities from fecal specimens. Nutr. J. 9:1

    Article  CAS  Google Scholar 

  61. Jervis-Bardy J, Leong LE, Marri S et al (2015) Deriving accurate microbiota profiles from human samples with low bacterial content through post-sequencing processing of Illumina MiSeq data. Microbiome. 3:1

    Article  Google Scholar 

  62. Klindworth A, Pruesse E, Schweer T et al (2013) Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 41:e1

    Article  PubMed  CAS  Google Scholar 

  63. Magoč T, Salzberg SL (2011) FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics. 27:2957–2963

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  64. Martin M (2011) Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J 17:10–12

    Article  Google Scholar 

  65. Joshi NA, Fass JN (2011) Sickle: a sliding-window, adaptive, quality-based trimming tool for FastQ files. https://github.com/najoshi/sickle Accessed Sept 2016

  66. Schloss PD, Westcott SL, Ryabin T et al (2009) Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75:7537–7541

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  67. Quast C, Pruesse E, Yilmaz P et al (2013) The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41:D590–D596

    Article  PubMed  CAS  Google Scholar 

  68. Huse SM, Welch DM, Morrison HG, Sogin ML (2010) Ironing out the wrinkles in the rare biosphere through improved OTU clustering. Environ. Microbiol. 12:1889–1898

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  69. Edgar RC (2010) Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 26:2460–2461

    Article  PubMed  CAS  Google Scholar 

  70. Edgar RC (2004) MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32:1792–1797

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  71. Kumar S, Stecher G, Tamura K (2016) MEGA7: molecular evolutionary genetics analysis version 7.0 for bigger datasets. Mol. Biol. Evol. 33:1870–1874

    Article  PubMed  CAS  Google Scholar 

  72. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ (1990) Basic local alignment search tool. J. Mol. Biol. 215:403–410

    Article  PubMed  CAS  Google Scholar 

  73. NCBI RC (2016) Database resources of the National Center for biotechnology information. Nucleic Acids Res. 44:D7

    Article  CAS  Google Scholar 

  74. Silvestro D, Michalak I (2012) raxmlGUI: a graphical front-end for RAxML. Org. Divers. Evol. 12:335–337

    Article  Google Scholar 

  75. Stamatakis A (2014) RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics. 30:1312–1313

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  76. Core Team R (2017) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna https://www.R-project.org/

    Google Scholar 

  77. Chen J, Bittinger K, Charlson ES, Hoffmann C, Lewis J, Wu GD, Collman RG, Bushman FD, Li H (2012) Associating microbiome composition with environmental covariates using generalized UniFrac distances. Bioinformatics. 28:2106–2113

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  78. Warnes GR, Bolker B, Bonebakker L et al. (2016) gplots: various R programming tools for plotting data. R package version 3.0.1. https://CRAN.R-project.org/package=gplots

  79. Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. Roy. Stat. Soc. B. 57:289–300

    Google Scholar 

  80. Oksanen J, Blanchet FG, Friendly M et al. (2017) Vegan: community ecology package. R package version 2.4.2. https://CRAN.R-project.org/package=vegan

  81. Zuur AF, Ieno EN, Walker NJ, Saveliev AA, Smith GM (2009) Mixed effects models and extensions in ecology with R. Springer, New York

    Book  Google Scholar 

  82. Mills DK, Entry JA, Voss JD, Gillevet PM, Mathee K (2006) An assessment of the hypervariable domains of the 16S rRNA genes for their value in determining microbial community diversity: the paradox of traditional ecological indices. FEMS Microbiol. Ecol. 57:496–503

    Article  PubMed  CAS  Google Scholar 

  83. Clarke KR, Warwick RM (1994) Similarity-based testing for community pattern: the two-way layout with no replication. Mar. Biol. 118:167–176

    Article  Google Scholar 

  84. Faith JJ, Ahern PP, Ridaura VK, Cheng J, Gordon JI (2014) Identifying gut microbe–host phenotype relationships using combinatorial communities in gnotobiotic mice. Sci. Transl. Med. 6:220ra11

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  85. Cryan JF, Dinan TG (2012) Mind-altering microorganisms: the impact of the gut microbiota on brain and behaviour. Nat. Rev. Neurosci. 13:701–712

    Article  PubMed  CAS  Google Scholar 

  86. Hsiao EY, McBride SW, Hsien S et al (2013) Microbiota modulate behavioral and physiological abnormalities associated with neurodevelopmental disorders. Cell. 155:1451–1463

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  87. Spor A, Koren O, Ley R (2011) Unravelling the effects of the environment and host genotype on the gut microbiome. Nat. Rev. Microbiol. 9:279

    Article  PubMed  CAS  Google Scholar 

  88. Ley RE, Bäckhed F, Turnbaugh P (2005) Obesity alters gut microbial ecology. PNAS. 102:11070–11075

    Article  PubMed  CAS  Google Scholar 

  89. Walter J, Ley R (2011) The human gut microbiome: ecology and recent evolutionary changes. Annu. Rev. Microbiol. 65:411–429

    Article  PubMed  CAS  Google Scholar 

  90. Lee WJ, Hase K (2014) Gut microbiota-generated metabolites in animal health and disease. Nat. Chem. Biol. 10:416–424

    Article  PubMed  CAS  Google Scholar 

  91. Gao Z, Tseng CH, Pei Z, Blaser MJ (2007) Molecular analysis of human forearm superficial skin bacterial biota. PNAS. 104:2927–2932

    Article  PubMed  CAS  Google Scholar 

  92. Zann RA (1996) The zebra finch: a synthesis of field and laboratory studies (volume 5 Oxford ornithology series). Oxford University Press, Oxford

    Google Scholar 

  93. Bataille A, Lee-Cruz L, Tripathi B, Kim H, Waldman B (2016) Microbiome variation across amphibian skin regions: implications for chytridiomycosis mitigation efforts. Microb. Ecol. 71:221–232

    Article  PubMed  Google Scholar 

  94. Sanchez E, Bletz MC, Duntsch L, Bhuju S, Geffers R et al (2016) Cutaneous bacterial communities of a poisonous salamander: a perspective from life stages, body parts and environmental conditions. Microb. Ecol. 73:455

    Article  PubMed  CAS  Google Scholar 

  95. Findley K, Oh J, Yang J et al (2013) Topographic diversity of fungal and bacterial communities in human skin. Nature. 498:367–370

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  96. Roth RR, James WD (1988) Microbial ecology of the skin. Annu. Rev. Microbiol. 42:441–464

    Article  PubMed  CAS  Google Scholar 

  97. Sabino-Pinto J, Bletz MC, Islam MM et al (2016) Composition of the cutaneous bacterial community in Japanese amphibians: effects of captivity, host species, and body region. Microb. Ecol. 72:460–469

    Article  PubMed  Google Scholar 

  98. Salter SJ, Cox MJ, Turek EM, Calus ST et al (2014) Reagent and laboratory contamination can critically impact sequence-based microbiome analyses. BMC Biol. 12:87

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  99. Glassing A, Dowd SE, Galandiuk S, Davis B, Chiodini RJ (2016) Inherent bacterial DNA contamination of extraction and sequencing reagents may affect interpretation of microbiota in low bacterial biomass samples. Gut. Pathog. 8:24

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  100. Dille JW, Rogers CM, Schneegurt MA (2016) Isolation and characterization of bacteria from the feathers of wild dark-eyed juncos (Junco hyemalis). Auk. 133:155–167

    Article  Google Scholar 

  101. Caspers BA, Gagliardo A, Krause ET (2015) Impact of kin odour on reproduction in zebra finches. Behav. Ecology. Sociobiol. 69:1827–1833

    Article  Google Scholar 

Download references

Acknowledgements

We are particularly grateful to Elke Hippauf for support in the laboratory and to Ursula Kodytek, Kristina Ruhe and Brigitta Otte-Eustergerling for taking care of the birds. Furthermore, we thank Sebastian Dörrenberg and Sarah Golüke for helping in skin microbe sampling, Helga Pankoke for statistical advice and Oliver Krüger for logistical support. This study was financially supported by a Freigeist Fellowship from the Volkswagen Foundation to B.A.C. This work was supported in part by grants from the German Federal Ministry of Education and Research (BMBF) for the ‘Bielefeld-Gießen Center for Microbial Bioinformatics - BiGi’ project (grant number 031A533A) within the German Network for Bioinformatics Infrastructure (de.NBI). We also thank the de.NBI for the opportunity to take part in a bioinformatics workshop. We thank Sonja Engel for creating the beautiful zebra finch artwork. Additionally, we thank three anonymous reviewers for their helpful comments and suggestions.

Author information

Authors and Affiliations

Authors

Contributions

B.A.C. and A.T. conceived the experiment; K.E., J.S. and B.A.C. designed the experiment; K.E., J.S. and A.W. carried out the experiment; J.S., D.W., S.J. and B.A.C. analysed the data; S.J. contributed analytical tools; K.E., J.S. and B.A.C. wrote the manuscript with input from S.J., D.W., J.K. and A.T.

Corresponding authors

Correspondence to Kathrin Engel or Jan Sauer.

Ethics declarations

Conflict of Interest

The authors declare that they have no conflict of interest.

Ethical Approval

All applicable national guidelines for the care and use of animals were followed.

Additional information

K.E. and J.S. are joint first authors of this work.

Data accessibility

The 16S rRNA sequence reads obtained in this study have been deposited in the EMBL-EBI database under the Bioproject ID PRJEB23205.

Electronic supplementary material

ESM 1

Contains the detailed description of the ‘Library preparation and DNA sequencing’ part. (DOCX 19 kb)

ESM 2

Contains all raw and metadata used for statistical analysis, the consensus sequences used for phylogenetic tree reconstruction and the full OTU table with taxonomy. (XLSX 1158 kb)

ESM 3

Contains Figs. S3.1–S3.6, showing the skin microbiome data separately visualised for each species and the skin microbiomes of all three species based on a weighted UniFrac distance matrix (S3.1–S3.4). S3.5 and S3.6 show two Venn diagrams including only those OTUs present in at least 50 and 75% of the samples per bird species (DOCX 176 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Engel, K., Sauer, J., Jünemann, S. et al. Individual- and Species-Specific Skin Microbiomes in Three Different Estrildid Finch Species Revealed by 16S Amplicon Sequencing. Microb Ecol 76, 518–529 (2018). https://doi.org/10.1007/s00248-017-1130-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00248-017-1130-8

Keywords

Navigation