Individual and Site-Specific Variation in a Biogeographical Profile of the Coyote Gastrointestinal Microbiota

Abstract

Most knowledge of the vertebrate gut microbiota comes from fecal samples; due to difficulties involved in sample collection, the upper intestinal microbiota is poorly understood in wild animals despite its potential to inform broad interpretations about host-gut microbe relationships under natural conditions. Here, we used 16S rRNA gene sequencing to characterize the microbiota of wild coyotes (Canis latrans) along the gastrointestinal tract, including samples from the duodenum, jejunum, ileum, caecum, ascending and descending colon, and feces. We used this intestinal profile to (1) quantify how intestinal site and individual identity interact to shape the microbiota in an uncontrolled setting, and (2) evaluate whether the fecal microbiota adequately represent other intestinal sites. Microbial communities in the large intestine were distinct from those in the small intestine, with higher diversity and a greater abundance of anaerobic taxa. Within each of the small and large intestine, individual identity explained significantly more among-sample variation than specific intestinal sites, revealing the importance of individual variation in the microbiota of free-living animals. Fecal samples were not an adequate proxy for studying upper intestinal environments, as they contained only half the amplicon sequence variants (ASVs) present in the small intestine at three- to four-fold higher abundances. Our study is a unique biogeographical investigation of the microbiota using free-living mammals rather than livestock or laboratory organisms and provides a foundational understanding of the gastrointestinal microbiota in a wild canid.

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Data Availability

The unfiltered sequence data used in this study has been deposited in the NCBI Short Read Archive under accession number PRJNA528765.

References

  1. 1.

    Lozupone C, Stomabaugh J, Gordon J et al (2012) Diversity, stability and resilience of the human gut microbiota. Nature 489:220–230. https://doi.org/10.1038/nature11550.Diversity

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  2. 2.

    McFall-Ngai MJ, Hadfield MG, Bosch TCG, Carey HV, Domazet-Lošo T, Douglas AE, Dubilier N, Eberl G, Fukami T, Gilbert SF, Hentschel U, King N, Kjelleberg S, Knoll AH, Kremer N, Mazmanian SK, Metcalf JL, Nealson K, Pierce NE, Rawls JF, Reid A, Ruby EG, Rumpho M, Sanders JG, Tautz D, Wernegreen JJ (2013) Animals in a bacterial world, a new imperative for the life sciences. Proc. Natl. Acad. Sci. 110:3229–3236. https://doi.org/10.1073/pnas.1218525110

    Article  PubMed  PubMed Central  Google Scholar 

  3. 3.

    Amato KR (2013) Co-evolution in context: the importance of studying gut microbiomes in wild animals. Microbiome Sci Med 1:10–29. https://doi.org/10.2478/micsm-2013-0002

    Article  Google Scholar 

  4. 4.

    Muegge BD, Kuczynski J, Knights D et al (2011) Diet drives convergence in gut microbiome functions across mammalian phylogeny and within humans. Science (80- ) 332:970–974. https://doi.org/10.1126/science.1198719

    CAS  Article  Google Scholar 

  5. 5.

    Hicks AL, Lee KJ, Couto-Rodriguez M, Patel J, Sinha R, Guo C, Olson SH, Seimon A, Seimon TA, Ondzie AU, Karesh WB, Reed P, Cameron KN, Lipkin WI, Williams BL (2018) Gut microbiomes of wild great apes fluctuate seasonally in response to diet. Nat. Commun. 9:1786. https://doi.org/10.1038/s41467-018-04204-w

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Sherrill-Mix S, McCormick K, Lauder A, Bailey A, Zimmerman L, Li Y, Django JBN, Bertolani P, Colin C, Hart JA, Hart TB, Georgiev AV, Sanz CM, Morgan DB, Atencia R, Cox D, Muller MN, Sommer V, Piel AK, Stewart FA, Speede S, Roman J, Wu G, Taylor J, Bohm R, Rose HM, Carlson J, Mjungu D, Schmidt P, Gaughan C, Bushman JI, Schmidt E, Bittinger K, Collman RG, Hahn BH, Bushman FD (2018) Allometry and ecology of the bilaterian gut microbiome. MBio 9:e00319–e00318. https://doi.org/10.1128/mBio.00319-18

    Article  PubMed  PubMed Central  Google Scholar 

  7. 7.

    Hanning I, Diaz-Sanchez S (2015) The functionality of the gastrointestinal microbiome in non-human animals. Microbiome 3:51. https://doi.org/10.1186/s40168-015-0113-6

    Article  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Karasov WH, Douglas AE (2013) Comparative digestive physiology. Comp Physiol 3:741–783. https://doi.org/10.1002/cphy.c110054.Comparative

    Article  Google Scholar 

  9. 9.

    Hillman ET, Lu H, Yao T, Nakatsu CH (2017) Microbial ecology along the gastrointestinal tract. Microbes Environ. 32:300–313. https://doi.org/10.1264/jsme2.me17017

    Article  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Sender R, Fuchs S, Milo R (2016) Revised estimates for the number of human and bacteria cells in the body. PLoS Biol. 14:e1002533. https://doi.org/10.1371/journal.pbio.1002533

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Pilla R, Suchodolski JS (2020) The role of the canine gut microbiome and metabolome in health and gastrointestinal disease. Front Vet Sci 6:498. https://doi.org/10.3389/fvets.2019.00498

    Article  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Angelakis E, Armougom F, Carri F et al (2015) A metagenomic investigation of the duodenal microbiota reveals links with obesity. PLoS One 10:e0137784. https://doi.org/10.1371/journal.pone.0137784

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Onishi JC, Campbell S, Moreau M, Patel F, Brooks AI, Zhou YX, Häggblom MM, Storch J (2017) Bacterial communities in the small intestine respond differently to those in the caecum and colon in mice fed low- and high-fat diets. Microbiology 163:1189–1197. https://doi.org/10.1099/mic.0.000496

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Nagata N, Tohya M, Fukuda S, Suda W, Nishijima S, Takeuchi F, Ohsugi M, Tsujimoto T, Nakamura T, Shimomura A, Yanagisawa N, Hisada Y, Watanabe K, Imbe K, Akiyama J, Mizokami M, Miyoshi-Akiyama T, Uemura N, Hattori M (2019) Effects of bowel preparation on the human gut microbiome and metabolome. Sci. Rep. 9:4042. https://doi.org/10.1038/s41598-019-40182-9

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Gu S, Chen D, Zhang J-N, Lv X, Wang K, Duan LP, Nie Y, Wu XL (2013) Bacterial community mapping of the mouse gastrointestinal tract. PLoS One 8:e74957. https://doi.org/10.1371/journal.pone.0074957

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Han X, Shao H, Wang Y, Hu A, Chen R, Chen Q (2020) Composition of the bacterial community in the gastrointestinal tract of Kunming mice. Electron. J. Biotechnol. 43:16–22. https://doi.org/10.1016/j.ejbt.2019.11.003

    CAS  Article  Google Scholar 

  17. 17.

    Zhang L, Jiang X, Li A, Waqas M, Gao X, Li K, Xie G, Zhang J, Mehmood K, Zhao S, Wangdui B, Li J (2020) Characterization of the microbial community structure in intestinal segments of yak (Bos grunniens). Anaerobe 61:102115. https://doi.org/10.1016/j.anaerobe.2019.102115

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Kim JH, Hong SW, Park BY, Yoo JG, Oh MH (2019) Characterisation of the bacterial community in the gastrointestinal tracts of elk (Cervus canadensis). Antonie van Leeuwenhoek, Int J Gen Mol Microbiol 112:225–235. https://doi.org/10.1007/s10482-018-1150-5

    Article  Google Scholar 

  19. 19.

    Thomas NA, Olvera-Ramírez AM, Abecia L, Adam CL, Edwards JE, Cox GF, Findlay PA, Destables E, Wood TA, McEwan NR (2019) Characterisation of the effect of day length, and associated differences in dietary intake, on the gut microbiota of Soay sheep. Arch. Microbiol. 201:889–896. https://doi.org/10.1007/s00203-019-01652-w

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Willson NL, Van TTH, Lever J et al (2019) Characterisation of the intestinal microbiota of commercially farmed saltwater crocodiles, Crocodylus porosus. Appl. Microbiol. Biotechnol. 103:8977–8985. https://doi.org/10.1007/s00253-019-10143-3

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Yasuda K, Oh K, Ren B, Tickle TL, Franzosa EA, Wachtman LM, Miller AD, Westmoreland SV, Mansfield KG, Vallender EJ, Miller GM, Rowlett JK, Gevers D, Huttenhower C, Morgan XC (2015) Biogeography of the intestinal mucosal and lumenal microbiome in the rhesus macaque. Cell Host Microbe 17:385–391. https://doi.org/10.1016/j.chom.2015.01.015

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Liu Y, Zheng Z, Yu L, Wu S, Sun L, Wu S, Xu Q, Cai S, Qin N, Bao W (2019) Examination of the temporal and spatial dynamics of the gut microbiome in newborn piglets reveals distinct microbial communities in six intestinal segments. Sci. Rep. 9:3453. https://doi.org/10.1038/s41598-019-40235-z

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Honneffer JB, Steiner JM, Lidbury JA, Suchodolski JS (2017) Variation of the microbiota and metabolome along the canine gastrointestinal tract. Metabolomics 13:26. https://doi.org/10.1007/s11306-017-1165-3

    CAS  Article  Google Scholar 

  24. 24.

    Yan W, Sun C, Zheng J, Wen C, Ji C, Zhang D, Chen Y, Hou Z, Yang N (2019) Efficacy of fecal sampling as a gut proxy in the study of chicken gut microbiota. Front. Microbiol. 10:2126. https://doi.org/10.3389/fmicb.2019.02126

    Article  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Videvall E, Strandh M, Engelbrecht A, Cloete S, Cornwallis CK (2018) Measuring the gut microbiome in birds: comparison of faecal and cloacal sampling. Mol. Ecol. Resour. 18:424–434. https://doi.org/10.1111/1755-0998.12744

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Ingala MR, Simmons NB, Wultsch C, Krampis K, Speer KA, Perkins SL (2018) Comparing microbiome sampling methods in a wild mammal: fecal and intestinal samples record different signals of host ecology, evolution. Front. Microbiol. 9:803. https://doi.org/10.3389/fmicb.2018.00803

    Article  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Pascoe EL, Hauffe HC, Marchesi JR, Perkins SE (2017) Network analysis of gut microbiota literature: an overview of the research landscape in non-human animal studies. ISME J 11:2644–2651. https://doi.org/10.1038/ismej.2017.133

    Article  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Wasimuddin MS, Melzheimer J et al (2017) Gut microbiomes of free-ranging and captive Namibian cheetahs: diversity, putative functions and occurrence of potential pathogens. Mol. Ecol. 26:5515–5527. https://doi.org/10.1111/mec.14278

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Nelson TM, Rogers TL, Carlini AR, Brown MV (2013) Diet and phylogeny shape the gut microbiota of Antarctic seals: a comparison of wild and captive animals. Environ. Microbiol. 15:1132–1145. https://doi.org/10.1111/1462-2920.12022

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Alessandri G, Milani C, Mancabelli L, Mangifesta M, Lugli GA, Viappiani A, Duranti S, Turroni F, Ossiprandi MC, van Sinderen D, Ventura M (2019) The impact of human-facilitated selection on the gut microbiota of domesticated mammals. FEMS Microbiol. Ecol. 95:1–13. https://doi.org/10.1093/femsec/fiz121

    CAS  Article  Google Scholar 

  31. 31.

    Laviad-Shitrit S, Izhaki I, Lalzar M, Halpern M (2019) Comparative analysis of intestine microbiota of four wild waterbird species. Front. Microbiol. 10:1911. https://doi.org/10.3389/fmicb.2019.01911

    Article  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Suchodolski JS, Camacho J, Steiner JM (2008) Analysis of bacterial diversity in the canine duodenum, jejunum, ileum, and colon by comparative 16S rRNA gene analysis. FEMS Microbiol. Ecol. 66:567–578. https://doi.org/10.1111/j.1574-6941.2008.00521.x

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Gehrt SD, Riley SPD (2010) Coyotes (Canis latrans). In: Gehrt SD, Riley SPD, Cypher BL (eds) Urban Carnivores: Ecology, Conflict, and Conservation. pp 79–95

  34. 34.

    Murray M, Edwards MA, Abercrombie B, St. Clair CC (2015) Poor health is associated with use of anthropogenic resources in an urban carnivore. Proc. R. Soc. B Biol. Sci. 282:20150009. https://doi.org/10.1098/rspb.2015.0009

    Article  Google Scholar 

  35. 35.

    Callahan BJ, Mcmurdie PJ, Rosen MJ et al (2016) DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13:581–583. https://doi.org/10.1038/nmeth.3869

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  36. 36.

    R Core Team (2019) R: a language and environment for statistical computing

  37. 37.

    Callahan BJ, McMurdie PJ, Holmes SP (2017) Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. ISME J 11:2639–2643. https://doi.org/10.1038/ismej.2017.119

    Article  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Cole JR, Wang Q, Fish JA, Chai B, McGarrell DM, Sun Y, Brown CT, Porras-Alfaro A, Kuske CR, Tiedje JM (2014) Ribosomal Database Project: data and tools for high throughput rRNA analysis. Nucleic Acids Res. 42:633–642. https://doi.org/10.1093/nar/gkt1244

    CAS  Article  Google Scholar 

  39. 39.

    Wang Q, Garrity GM, Tiedje JM, Cole JR (2007) Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73:5261–5267. https://doi.org/10.1128/AEM.00062-07

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Davis NM, Proctor DM, Holmes SP, Relman DA, Callahan BJ (2018) Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome 6:226. https://doi.org/10.1186/s40168-018-0605-2

    Article  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Schliep KP (2011) phangorn: phylogenetic analysis in R. Bioinformatics 27:592–593. https://doi.org/10.1093/bioinformatics/btq706

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Callahan BJ, Sankaran K, Fukuyama JA, et al (2016) Bioconductor workflow for microbiome data analysis: from raw reads to community analyses. F1000Research 5:1492. https://doi.org/10.12688/f1000research.8986.2

  43. 43.

    McMurdie PJ, 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

  44. 44.

    Gloor GB, Macklaim JM, Pawlowsky-Glahn V, Egozcue JJ (2017) Microbiome datasets are compositional: and this is not optional. Front. Microbiol. 8:2224–2882. https://doi.org/10.1080/01904168209363016

    Article  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Fernandes A, Macklaim JM, Linn T et al (2013) ANOVA-like differential expression (ALDEx) analysis for mixed population RNA-Seq. PLoS One 8:e67019. https://doi.org/10.1371/journal.pone.0067019

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  46. 46.

    Bates D, Machler M, Bolker BM, Walker SC (2015) Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67. https://doi.org/10.18637/jss.v067.i01

  47. 47.

    Morgan XC, Tickle TL, Sokol H, Gevers D, Devaney KL, Ward DV, Reyes JA, Shah SA, LeLeiko N, Snapper SB, Bousvaros A, Korzenik J, Sands BE, Xavier RJ, Huttenhower C (2012) Dysfunction of the intestinal microbiome in inflammatory bowel disease and treatment. Genome Biol. 13:R79. https://doi.org/10.1186/gb-2012-13-9-r79

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  48. 48.

    Oksanen J, Blanchet FG, Friendly M, et al (2018) Vegan: community ecology package

  49. 49.

    Lozupone C, Knight R (2005) UniFrac: a new phylogenetic method for comparing microbial communities. Appl. Environ. Microbiol. 71:8228–8235. https://doi.org/10.1128/AEM.71.12.8228

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  50. 50.

    Liaw A, Wiener M (2002) Classification and regression by randomForest. R News 2:18–22

    Google Scholar 

  51. 51.

    Barton K (2018) MuMIn: multi-model inference

  52. 52.

    Colston TJ, Jackson CR (2016) Microbiome evolution along divergent branches of the vertebrate tree of life: what is known and unknown. Mol. Ecol. 25:3776–3800. https://doi.org/10.1111/mec.13730

    Article  PubMed  PubMed Central  Google Scholar 

  53. 53.

    Zhang H, Chen L (2010) Phylogenetic analysis of 16S rRNA gene sequences reveals distal gut bacterial diversity in wild wolves (Canis lupus). Mol. Biol. Rep. 37:4013–4022. https://doi.org/10.1007/s11033-010-0060-z

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  54. 54.

    Schmidt M, Unterer S, Suchodolski JS, Honneffer JB, Guard BC, Lidbury JA, Steiner JM, Fritz J, Kölle P (2018) The fecal microbiome and metabolome differs between dogs fed bones and raw food (BARF) diets and dogs fed commercial diets. PLoS One 13:e0201279. https://doi.org/10.1371/journal.pone.0201279

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  55. 55.

    Deng P, Swanson KS (2015) Gut microbiota of humans, dogs and cats: current knowledge and future opportunities and challenges. Br. J. Nutr. 113:S6–S17. https://doi.org/10.1017/S0007114514002943

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  56. 56.

    Donohoe DR, Garge N, Zhang X, Sun W, O'Connell TM, Bunger MK, Bultman SJ (2011) The microbiome and butyrate regulate energy metabolism and autophagy in the mammalian colon. Cell Metab. 13:517–526. https://doi.org/10.1016/j.cmet.2011.02.018

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  57. 57.

    Mu C, Yang Y, Su Y, Zoetendal EG, Zhu W (2017) Differences in microbiota membership along the gastrointestinal tract of piglets and their differential alterations following an early-life antibiotic intervention. Front. Microbiol. 8:797. https://doi.org/10.3389/fmicb.2017.00797

    Article  PubMed  PubMed Central  Google Scholar 

  58. 58.

    Crespo-Piazuelo D, Estellé J, Revilla M, Criado-Mesas L, Ramayo-Caldas Y, Óvilo C, Fernández AI, Ballester M, Folch JM (2018) Characterization of bacterial microbiota compositions along the intestinal tract in pigs and their interactions and functions. Sci. Rep. 8:12727. https://doi.org/10.1038/s41598-018-30932-6

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  59. 59.

    Wilkinson TJ, Cowan AA, Vallin HE, Onime LA, Oyama LB, Cameron SJ, Gonot C, Moorby JM, Waddams K, Theobald VJ, Leemans D, Bowra S, Nixey C, Huws SA (2017) Characterization of the microbiome along the gastrointestinal tract of growing turkeys. Front. Microbiol. 8:1089. https://doi.org/10.3389/fmicb.2017.01089

    Article  PubMed  PubMed Central  Google Scholar 

  60. 60.

    Amato KR, Yeoman CJ, Kent A, Righini N, Carbonero F, Estrada A, Rex Gaskins H, Stumpf RM, Yildirim S, Torralba M, Gillis M, Wilson BA, Nelson KE, White BA, Leigh SR (2013) Habitat degradation impacts black howler monkey (Alouatta pigra) gastrointestinal microbiomes. ISME J 7:1344–1353. https://doi.org/10.1038/ismej.2013.16

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  61. 61.

    Park W (2018) Gut microbiomes and their metabolites shape human and animal health. J. Microbiol. 56:151–153. https://doi.org/10.1007/s12275-018-0577-8

    Article  PubMed  PubMed Central  Google Scholar 

  62. 62.

    Rosenfeld CS (2017) Gut dysbiosis in animals due to environmental chemical exposures. Front. Cell. Infect. Microbiol. 7:396. https://doi.org/10.3389/fcimb.2017.00396

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  63. 63.

    Wong K, Shaw TI, Oladeinde A, Glenn TC, Oakley B, Molina M (2016) Rapid microbiome changes in freshly deposited cow feces under field conditions. Front. Microbiol. 7:500. https://doi.org/10.3389/fmicb.2016.00500

    Article  PubMed  PubMed Central  Google Scholar 

  64. 64.

    Dressman JB (1986) Comparison of canine and human gastrointestinal physiology. Pharm. Res. 3:123–131. https://doi.org/10.1023/A:1016353705970

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  65. 65.

    Gaulke CA, Arnold HK, Humphreys IR, Kembel SW, O’Dwyer JP, Sharpton TJ (2018) Ecophylogenetics clarifies the evolutionary association between mammals and their gut microbiota. MBio 9:e01348–e01318. https://doi.org/10.1128/mBio.01348-18

    Article  PubMed  PubMed Central  Google Scholar 

  66. 66.

    Robinson JWL, Menge H, Sepúlveda FV, Mirkovitch V (1977) Functional and structural characteristics of the jejunum and ileum in the dog and the rat. Digestion 15:188–199. https://doi.org/10.1159/000198003

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  67. 67.

    Laganiere S, Berteloot A, Maestracci D (1984) Digestive and absorptive functions along the dog small intestine: comparative distributions in relation to biochemical and morphological parameters. Comp. Biochem. Physiol. 79A:463–472. https://doi.org/10.1016/0300-9629(84)90547-4

    CAS  Article  Google Scholar 

  68. 68.

    Murray M, Cembrowski A, Latham ADM, Lukasik VM, Pruss S, St Clair CC (2015) Greater consumption of protein-poor anthropogenic food by urban relative to rural coyotes increases diet breadth and potential for human-wildlife conflict. Ecography (Cop) 38:001–008. https://doi.org/10.1111/ecog.01128

    Article  Google Scholar 

  69. 69.

    Metzler-Zebeli BU, Schmitz-Esser S, Mann E, Grüll D, Molnar T, Zebeli Q (2015) Adaptation of the cecal bacterial microbiome of growing pigs in response to resistant starch type 4. Appl. Environ. Microbiol. 81:8489–8499. https://doi.org/10.1128/aem.02756-15

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  70. 70.

    Zhu Y, Wang C, Li F (2015) Impact of dietary fiber/starch ratio in shaping caecal microbiota in rabbits. Can. J. Microbiol. 61:771–784. https://doi.org/10.1139/cjm-2015-0201

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  71. 71.

    Tao S, Tian P, Luo Y, Tian J, Hua C, Geng Y, Cong R, Ni Y, Zhao R (2017) Microbiome-metabolome responses to a high-grain diet associated with the hind-gut health of goats. Front. Microbiol. 8:1764. https://doi.org/10.3389/fmicb.2017.01764

    Article  PubMed  PubMed Central  Google Scholar 

  72. 72.

    Galley JD, Yu Z, Kumar P, Dowd SE, Lyte M, Bailey MT (2015) The structures of the colonic mucosa-associated and luminal microbial communities are distinct and differentially affected by a prolonged murine stressor. Gut Microbes 5:748–760. https://doi.org/10.4161/19490976.2014.972241

    Article  Google Scholar 

  73. 73.

    Ringel Y, Maharshak N, Ringel-Kulka T, Wolber EA, Sartor RB, Carroll IM (2015) High throughput sequencing reveals distinct microbial populations within the mucosal and luminal niches in healthy individuals. Gut Microbes 6:173–181. https://doi.org/10.1080/19490976.2015.1044711

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  74. 74.

    Yeoman CJ, Ishaq SL, Bichi E, Olivo SK, Lowe J, Aldridge BM (2018) Biogeographical differences in the influence of maternal microbial sources on the early successional development of the bovine neonatal gastrointestinal tract. Sci. Rep. 8:3197. https://doi.org/10.1038/s41598-018-21440-8

    CAS  Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgments

Coyote carcasses were provided by Bill, Duncan, and Malcolm Abercrombie of Animal Damage Control, Inc., and SS would like to thank Dana Sanderson and several undergraduate volunteers at the University of Alberta for assistance with coyote necropsies.

Code Availability

The R scripts and all material(code and workspace) is available (present tense) in the linked GitHub repository https://github.com/sasugden/Coyote_intestinal_biogeography.

Funding

This study was supported by Discovery Grants from the Natural Sciences and Engineering Research Council of Canada (NSERC) to CCSC (RGPIN-2017-05915) and LYS (RGPIN-2019-04399).

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Correspondence to Scott Sugden.

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Sugden, S., St. Clair, C.C. & Stein, L.Y. Individual and Site-Specific Variation in a Biogeographical Profile of the Coyote Gastrointestinal Microbiota. Microb Ecol (2020). https://doi.org/10.1007/s00248-020-01547-0

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Keywords

  • Wildlife microbiota
  • Gut microbiome
  • Gastrointestinal tract
  • Coyote
  • High-throughput sequencing
  • Individual variation