in

Distinct gut microbiomes in two polar bear subpopulations inhabiting different sea ice ecoregions

  • 1.

    Ley, R. E. et al. Evolution of mammals and their gut microbes. Science 320, 1647–1651. https://doi.org/10.1126/science.1155725 (2008).

    ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 2.

    Hale, V. L. et al. Diet versus phylogeny: a comparison of gut microbiota in captive colobine monkey species. Microb. Ecol. 75, 515–527. https://doi.org/10.1007/s00248-017-1041-8 (2018).

    Article 
    PubMed 

    Google Scholar 

  • 3.

    Pickard, J. M., Zeng, M. Y., Caruso, R. & Núñez, G. Gut microbiota: role in pathogen colonization, immune responses, and inflammatory disease. Immunol. Rev. 279, 70–89 (2017).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 4.

    Ley, R. E., Peterson, D. A. & Gordon, J. I. Ecological and evolutionary forces shaping microbial diversity in the human intestine. Cell 124, 837–848 (2006).

    CAS 
    PubMed 

    Google Scholar 

  • 5.

    Pascoe, E. L., Hauffe, H. C., Marchesi, J. R. & Perkins, S. E. 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 (2017).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 6.

    Hauffe, H. C. & Barelli, C. Conserve the germs: the gut microbiota and adaptive potential. Conserv. Genet. 20, 19–27. https://doi.org/10.1007/s10592-019-01150-y (2019).

    Article 

    Google Scholar 

  • 7.

    Ellegaard, K. M. & Engel, P. Beyond 16S rRNA Community profiling: intra-species diversity in the gut microbiota. Front. Microbiol. 7, doi:https://doi.org/10.3389/fmicb.2016.01475 (2016).

  • 8.

    Sugden, S., Sanderson, D., Ford, K., Stein, L. Y. & St. Clair, C. C. An altered microbiome in urban coyotes mediates relationships between anthropogenic diet and poor health. Sci. Rep. 10, 22207, doi:https://doi.org/10.1038/s41598-020-78891-1 (2020).

  • 9.

    Góngora, E., Elliott, K. H. & Whyte, L. Gut microbiome is affected by inter-sexual and inter-seasonal variation in diet for thick-billed murres (Uria lomvia). Sci. Rep. 11, 1200. https://doi.org/10.1038/s41598-020-80557-x (2021).

    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 10.

    Muegge, B. D. et al. Diet drives convergence in gut microbiome functions across mammalian phylogeny and within humans. Science 332, 970. https://doi.org/10.1126/science.1198719 (2011).

    ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 11.

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

    ADS 
    CAS 

    Google Scholar 

  • 12.

    McKenzie, V. J. et al. The effects of captivity on the mammalian gut microbiome. Integr. Comp. Biol. 57, 690–704 (2017).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 13.

    Des Roches, S. et al. The ecological importance of intraspecific variation. Nat. Ecol. Evol. 2, 57–64, doi:https://doi.org/10.1038/s41559-017-0402-5 (2018).

  • 14.

    Des Roches, S., Pendleton, L. H., Shapiro, B. & Palkovacs, E. P. Conserving intraspecific variation for nature’s contributions to people. Nat. Ecol. Evol. 5, 574–582, doi:https://doi.org/10.1038/s41559-021-01403-5 (2021).

  • 15.

    Wasimuddin, et al. 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 (2017).

    CAS 
    Article 
    PubMed 

    Google Scholar 

  • 16.

    Alfano, N. et al. Variation in koala microbiomes within and between individuals: effect of body region and captivity status. Sci. Rep. 5, 10189. https://doi.org/10.1038/srep10189 (2015).

    ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 17.

    Schwab, C., Cristescu, B., Northrup, J. M., Stenhouse, G. B. & Gänzle, M. Diet and environment shape fecal bacterial microbiota composition and enteric pathogen load of grizzly bears. Plos One 6, e27905 (2011).

  • 18.

    Sommer, F. et al. The gut microbiota modulates energy metabolism in the hibernating brown bear Ursus arctos. Cell Rep 14, 1655–1661 (2016).

    CAS 
    PubMed 

    Google Scholar 

  • 19.

    Durner, G., Laidre, K. & York, G. Polar Bears: Proceedings of the 18th Working Meeting of the IUCN/SSC Polar Bear Specialist Group, 7–11 June 2016, Anchorage, Alaska. Gland, Switzerland and Cambridge, UK: IUCN. xxx+ 207pp (2018).

  • 20.

    Amstrup, S. C., Marcot, B. G. & Douglas, D. C. in Arctic sea ice decline: Observations, projections, mechanisms, and implications Geophysics monograph series (eds E.T. DeWeaver, C.M. Bitz, & L.-B. Tremblay) 213–268 (AGU, 2008).

  • 21.

    Thiemann, G. W., Iverson, S. J. & Stirling, I. Polar bear diets and arctic marine food webs: Insights from fatty acid analysis. Ecol. Monogr 78, 591–613 (2008).

    Google Scholar 

  • 22.

    McKinney, M. A. et al. Regional contamination versus regional dietary differences: Understanding geographic variation in brominated and chlorinated contaminant levels in polar bears. Environ. Sci. Technol. 45, 896–902 (2011).

    ADS 
    CAS 
    PubMed 

    Google Scholar 

  • 23.

    Laidre, K. L. et al. Arctic marine mammal population status, sea ice habitat loss, and conservation recommendations for the 21st century. Conserv. Biol. 29, 724–737 (2015).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 24.

    Stern, H. L. & Laidre, K. L. Sea-ice indicators of polar bear habitat. Cryosphere 10, 2027–2041. https://doi.org/10.5194/tc-10-2027-2016 (2016).

    ADS 
    Article 

    Google Scholar 

  • 25.

    Atwood, T. C. et al. Rapid environmental change drives increased land use by an Arctic marine predator. PLoS ONE 11, e0155932 (2016).

  • 26.

    Rode, K. D., Robbins, C. T., Nelson, L. & Amstrup, S. C. Can polar bears use terrestrial foods to offset lost ice-based hunting opportunities?. Front. Ecol. Environ. 13, 138–145 (2015).

    Google Scholar 

  • 27.

    Herreman, J. K. & Peacock, E. Polar bear use of a persistent food subsidy: insights from non-invasive genetic sampling in Alaska. Ursus 24, 148–163 (2013).

    Google Scholar 

  • 28.

    Glad, T. et al. Bacterial diversity in faeces from polar bear (Ursus maritimus) in Arctic Svalbard. BMC Microbiol. 10, doi:https://doi.org/10.1186/1471-2180-10-10 (2010).

  • 29.

    Watson, S. E. et al. Global change-driven use of onshore habitat impacts polar bear faecal microbiota. ISME J. https://doi.org/10.1038/s41396-019-0480-2 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 30.

    McKinney, M. A. et al. Global change effects on the long-term feeding ecology and contaminant exposures of East Greenland polar bears. Glob. Change Biol. 19, 2360–2372. https://doi.org/10.1111/gcb.12241 (2013).

    ADS 
    Article 

    Google Scholar 

  • 31.

    Ilinskaya, O. N., Ulyanova, V. V., Yarullina, D. R. & Gataullin, I. G. Secretome of Intestinal Bacilli: A Natural Guard against Pathologies. Front. Microbiol. 8, doi:https://doi.org/10.3389/fmicb.2017.01666 (2017).

  • 32.

    Cho, G.-S. et al. Quantification of Slackia and Eggerthella spp. in Human Feces and Adhesion of Representatives Strains to Caco-2 Cells. Front. Microbiol. 7, doi:https://doi.org/10.3389/fmicb.2016.00658 (2016).

  • 33.

    Astbury, S. et al. Lower gut microbiome diversity and higher abundance of proinflammatory genus Collinsella are associated with biopsy-proven nonalcoholic steatohepatitis. Gut Microbes 11, 569–580. https://doi.org/10.1080/19490976.2019.1681861 (2020).

    CAS 
    Article 
    PubMed 

    Google Scholar 

  • 34.

    Gomez-Arango, L. F. et al. Low dietary fiber intake increases Collinsella abundance in the gut microbiota of overweight and obese pregnant women. Gut Microbes 9, 189–201 (2018).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 35.

    Jeong, Y. et al. Gut microbial composition and function are altered in patients with early rheumatoid arthritis. J. Clin. Med. 8, 693 (2019).

    CAS 
    PubMed Central 

    Google Scholar 

  • 36.

    Liu, X. et al. Blautia-a new functional genus with potential probiotic properties?. Gut microbes 13, 1–21. https://doi.org/10.1080/19490976.2021.1875796 (2021).

    CAS 
    Article 
    PubMed 

    Google Scholar 

  • 37.

    Claus, S. P. et al. Colonization-induced host-gut microbial metabolic interaction. MBio 2, e00271-e210 (2011).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 38.

    Martínez, I. et al. Diet-induced metabolic improvements in a hamster model of hypercholesterolemia are strongly linked to alterations of the gut microbiota. Appl. Environ. Microbiol. 75, 4175–4184 (2009).

    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 39.

    Sergeant, M. J. et al. Extensive microbial and functional diversity within the chicken cecal microbiome. PLoS One 9, e91941 (2014).

  • 40.

    Zhang, X. et al. Human gut microbiota changes reveal the progression of glucose intolerance. PLoS One 8, e71108 (2013).

  • 41.

    Shetty, S. A., Marathe, N. P., Lanjekar, V., Ranade, D. & Shouche, Y. S. Comparative genome analysis of Megasphaera sp. reveals niche specialization and its potential role in the human gut. PLoS One 8, e79353 (2013).

  • 42.

    Jiang, X.-L., Su, Y. & Zhu, W.-Y. Fermentation characteristics of Megasphaera elsdenii J6 derived from pig feces on different lactate isomers. J. Integr. Agric. 15, 1575–1583. https://doi.org/10.1016/S2095-3119(15)61236-9 (2016).

    CAS 
    Article 

    Google Scholar 

  • 43.

    Hobson, K. A. & Stirling, I. Low variation in blood delta C-13 among Hudson Bay polar bears: implications for metabolism and tracing terrestrial foraging. Mar. Mammal Sci 13, 359–367 (1997).

    Google Scholar 

  • 44.

    Hobson, K. A., Stirling, I. & Andriashek, D. S. Isotopic homogeneity of breath CO2 from fasting and berry-eating polar bears: implications for tracing reliance on terrestrial foods in a changing Arctic. Can. J. Zool 87, 50–55 (2009).

    CAS 

    Google Scholar 

  • 45.

    Sakamoto, M. & Ohkuma, M. Reclassification of Xylanibacter oryzae Ueki et al. 2006 as Prevotella oryzae comb. nov., with an emended description of the genus Prevotella. Int. J. Syst. Evol. Microbiol. 62, 2637–2642 (2012).

  • 46.

    Ley, R. E. Obesity and the human microbiome. Curr. Opin. Gastroenterol. 26, 5–11 (2010).

    PubMed 

    Google Scholar 

  • 47.

    Rajilić-Stojanović, M. et al. Global and deep molecular analysis of microbiota signatures in fecal samples from patients with irritable bowel syndrome. Gastroenterology 141, 1792–1801 (2011).

    PubMed 

    Google Scholar 

  • 48.

    Larsen, N. et al. Gut microbiota in human adults with type 2 diabetes differs from non-diabetic adults. PLoS One 5, e9085 (2010).

  • 49.

    Le Chatelier, E. et al. Richness of human gut microbiome correlates with metabolic markers. Nature 500, 541–546 (2013).

    Google Scholar 

  • 50.

    Rajilić-Stojanović, M. & de Vos, W. M. The first 1000 cultured species of the human gastrointestinal microbiota. FEMS Microbiol. Rev. 38, 996–1047. https://doi.org/10.1111/1574-6976.12075 (2014).

    CAS 
    Article 
    PubMed 

    Google Scholar 

  • 51.

    do Nascimento Silva, A., de Avila, E. D., Nakano, V. & Avila-Campos, M. J. Pathogenicity and genetic profile of oral Porphyromonas species from canine periodontitis. Arch. Oral Biol. 83, 20–24 (2017).

  • 52.

    Acuña-Amador, L. & Barloy-Hubler, F. Porphyromonas spp. have an extensive host range in ill and healthy individuals and an unexpected environmental distribution: a systematic review and meta-analysis. Anaerobe 66, 102280, doi:https://doi.org/10.1016/j.anaerobe.2020.102280 (2020).

  • 53.

    Solé, C. et al. Alterations in gut microbiome in cirrhosis as assessed by quantitative metagenomics: relationship with acute-on-chronic liver failure and prognosis. Gastroenterology 160, 206–218. e213 (2021).

  • 54.

    Osman, M. A. et al. Parvimonas micra, Peptostreptococcus stomatis, Fusobacterium nucleatum and Akkermansia muciniphila as a four-bacteria biomarker panel of colorectal cancer. Sci. Rep. 11, 2925. https://doi.org/10.1038/s41598-021-82465-0 (2021).

    ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 55.

    Murphy, E. C. & Frick, I.-M. Gram-positive anaerobic cocci – commensals and opportunistic pathogens. FEMS Microbiol. Rev. 37, 520–553. https://doi.org/10.1111/1574-6976.12005 (2013).

    CAS 
    Article 
    PubMed 

    Google Scholar 

  • 56.

    Vitali, B., Abruzzo, A. & Mastromarino, P. in The Microbiota in Gastrointestinal Pathophysiology (eds Martin H. Floch, Yehuda Ringel, & W. Allan Walker) 399–407 (Academic Press, 2017).

  • 57.

    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).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 58.

    Kapourchali, F. R. & Cresci, G. A. M. Early-life gut microbiome—the importance of maternal and infant factors in its establishment. Nutr. Clin. Pract. 35, 386–405. https://doi.org/10.1002/ncp.10490 (2020).

    Article 
    PubMed 

    Google Scholar 

  • 59.

    Guo, G. et al. The Gut Microbial Community Structure of the North American River Otter (Lontra canadensis) in the Alberta Oil Sands Region in Canada: relationship with local environmental variables and metal body burden. Environ. Toxicol. Chem. https://doi.org/10.1002/etc.4876 (2020).

    Article 
    PubMed 

    Google Scholar 

  • 60.

    Haworth, S. E., White, K. S., Côté, S. D. & Shafer, A. B. A. Space, time and captivity: quantifying the factors influencing the fecal microbiome of an alpine ungulate. FEMS microbiology ecology 95, doi:https://doi.org/10.1093/femsec/fiz095 (2019).

  • 61.

    McKinney, M. A., Atwood, T. C., Iverson, S. J. & Peacock, E. Temporal complexity of southern Beaufort Sea polar bear diets during a period of increasing land use. Ecosphere 8, e01633. https://doi.org/10.1002/ecs2.1633 (2017).

    Article 

    Google Scholar 

  • 62.

    Atwood, T. C. et al. Rapid environmental change drives increased land use by an arctic marine predator. PLoS ONE 11, e0155932–e0155932. https://doi.org/10.1371/journal.pone.0155932 (2016).

    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 63.

    Laidre, K. L., Stirling, I., Estes, J. A., Kochnev, A. & Roberts, J. Historical and potential future importance of large whales as food for polar bears. Front. Ecol. Environ. 16, 515–524. https://doi.org/10.1002/fee.1963 (2018).

    Article 

    Google Scholar 

  • 64.

    Bromaghin, J. F. et al. Polar bear population dynamics in the southern Beaufort Sea during a period of sea ice decline. Ecol. Appl. 25, 634–651. https://doi.org/10.1890/14-1129.1 (2015).

    Article 
    PubMed 

    Google Scholar 

  • 65.

    Atwood, T. C. et al. Environmental and behavioral changes may influence the exposure of an Arctic apex predator to pathogens and contaminants. Sci. Rep. 7, doi:https://doi.org/10.1038/s41598-017-13496-9 (2017).

  • 66.

    Bowen, W. D. & Iverson, S. J. Methods of estimating marine mammal diets: a review of validation experiments and sources of bias and uncertainty. Mar. Mamm. Sci. 29, 719–754. https://doi.org/10.1111/j.1748-7692.2012.00604.x (2013).

    Article 

    Google Scholar 

  • 67.

    Sonsthagen, S. A. et al. DNA metabarcoding of feces to infer summer diet of Pacific walruses. Mar. Mamm. Sci. https://doi.org/10.1111/mms.12717 (2020).

    Article 

    Google Scholar 

  • 68.

    Michaux, J., Dyck, M., Boag, P., Lougheed, S. & Van Coeverden de Groot, P. New insights on polar bear (Ursus maritimus) diet from faeces based on next-generation sequencing technologies. ARCTIC 74, 87–99, doi:https://doi.org/10.14430/arctic72239 (2021).

  • 69.

    Bourque, J., Atwood, T. C., Divoky, G. J., Stewart, C. & McKinney, M. A. Fatty acid-based diet estimates suggest ringed seal remain the main prey of southern Beaufort Sea polar bears despite recent use of onshore food resources. Ecol. Evol. 10, 2093–2103. https://doi.org/10.1002/ece3.6043 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 70.

    Dominianni, C. et al. Sex, Body Mass Index, and Dietary Fiber Intake Influence the Human Gut Microbiome. PLoS One 10, doi: https://doi.org/10.1371/journal.pone.0124599 (2015).

  • 71.

    Bennett, G. et al. Host age, social group, and habitat type influence the gut microbiota of wild ring-tailed lemurs (Lemur catta). Am. J. Primatol. 78, 883–892. https://doi.org/10.1002/ajp.22555 (2016).

    CAS 
    Article 
    PubMed 

    Google Scholar 

  • 72.

    Peng, C. et al. Sex-specific association between the gut microbiome and high-fat diet-induced metabolic disorders in mice. Biol. Sex Differ. 11, 5. https://doi.org/10.1186/s13293-020-0281-3 (2020).

    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 73.

    Markle, J. G. et al. Sex differences in the gut microbiome drive hormone-dependent regulation of autoimmunity. Science 339, 1084–1088 (2013).

    ADS 
    CAS 
    PubMed 

    Google Scholar 

  • 74.

    Kaliannan, K. et al. Estrogen-mediated gut microbiome alterations influence sexual dimorphism in metabolic syndrome in mice. Microbiome 6, 205. https://doi.org/10.1186/s40168-018-0587-0 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 75.

    Park, M. J. et al. Reproductive senescence and ischemic stroke remodel the gut microbiome and modulate the effects of estrogen treatment in female rats. Transl. Stroke Res., 1–19 (2019).

  • 76.

    Thiemann, G. W., Budge, S. M., Iverson, S. J. & Stirling, I. Unusual fatty acid biomarkers reveal age- and sex-specific foraging in polar bears (Ursus maritimus). Can. J. Zool. 85, 505–517. https://doi.org/10.1139/Z07-028 (2007).

    CAS 
    Article 

    Google Scholar 

  • 77.

    Stirling, I. & Derocher, A. E. Effects of climate warming on polar bears: a review of the evidence. Glob. Change Biol. 18, 2694–2706. https://doi.org/10.1111/j.1365-2486.2012.02753.x (2012).

    ADS 
    Article 

    Google Scholar 

  • 78.

    Miller, S., Wilder, J. & Wilson, R. R. Polar bear–grizzly bear interactions during the autumn open-water period in Alaska. J. Mammal. 96, 1317–1325 (2015).

    Google Scholar 

  • 79.

    Mshelia, E. S. et al. The association between gut microbiome, sex, age and body condition scores of horses in Maiduguri and its environs. Microb. Pathog. 118, 81–86. https://doi.org/10.1016/j.micpath.2018.03.018 (2018).

    Article 
    PubMed 

    Google Scholar 

  • 80.

    Falony, G. et al. Population-level analysis of gut microbiome variation. Science 352, 560. https://doi.org/10.1126/science.aad3503 (2016).

    ADS 
    CAS 
    Article 

    Google Scholar 

  • 81.

    Walters, W. A., Xu, Z. & Knight, R. Meta-analyses of human gut microbes associated with obesity and IBD. FEBS Lett. 588, 4223–4233 (2014).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 82.

    Feng, P. et al. A review on gut remediation of selected environmental contaminants: possible roles of probiotics and gut microbiota. Nutrients 11, 22 (2019).

    CAS 

    Google Scholar 

  • 83.

    Vasemägi, A., Visse, M. & Kisand, V. Effect of environmental factors and an emerging parasitic disease on gut microbiome of wild salmonid fish. MSphere 2 (2017).

  • 84.

    Kreisinger, J., Bastien, G. r., Hauffe, H. C., Marchesi, J. & Perkins, S. E. Interactions between multiple helminths and the gut microbiota in wild rodents. Philos. Trans. R. Soc. B: Biol. Sci. 370, doi:https://doi.org/10.1098/rstb.2014.0295 (2015).

  • 85.

    Faust, K. & Raes, J. Microbial interactions: from networks to models. Nat. Rev. Microbiol. 10, 538–550 (2012).

    CAS 
    PubMed 

    Google Scholar 

  • 86.

    Baldo, L. et al. Convergence of gut microbiotas in the adaptive radiations of African cichlid fishes. ISME J. 11, 1975–1987 (2017).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 87.

    Yan, D. et al. Effects of Chronic Stress on the Fecal Microbiome of Malayan Pangolins (Manis javanica) Rescued from the Illegal Wildlife Trade. Curr. Microbiol. 78, 1017–1025. https://doi.org/10.1007/s00284-021-02357-4 (2021).

    CAS 
    Article 
    PubMed 

    Google Scholar 

  • 88.

    Schirmer, M. et al. Linking the human gut microbiome to inflammatory cytokine production capacity. Cell 167, 1125-1136.e1128. https://doi.org/10.1016/j.cell.2016.10.020 (2016).

    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 89.

    Mallott, E. K., Borries, C., Koenig, A., Amato, K. R. & Lu, A. Reproductive hormones mediate changes in the gut microbiome during pregnancy and lactation in Phayre’s leaf monkeys. Sci. Rep. 10, 9961. https://doi.org/10.1038/s41598-020-66865-2 (2020).

    ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 90.

    Burokas, A., Moloney, R. D., Dinan, T. G. & Cryan, J. F. Microbiota regulation of the mammalian gut–brain axis. Adv. Appl. Microbiol. 91, 1–62 (2015).

    CAS 
    PubMed 

    Google Scholar 

  • 91.

    Bercik, P. et al. Chronic gastrointestinal inflammation induces anxiety-like behavior and alters central nervous system biochemistry in mice. Gastroenterology 139, 2102–2112 (2010).

  • 92.

    Walter, J. M., Bagi, A. & Pampanin, D. M. Insights into the potential of the Atlantic cod gut microbiome as biomarker of oil contamination in the marine environment. Microorganisms 7, 209 (2019).

    CAS 
    PubMed Central 

    Google Scholar 

  • 93.

    Xia, J. et al. Effects of short term lead exposure on gut microbiota and hepatic metabolism in adult zebrafish. Comput. Biochem. Physiol. C: Toxicol. Pharmacol. 209, 1–8 (2018).

    CAS 

    Google Scholar 

  • 94.

    Breton, J. et al. Ecotoxicology inside the gut: impact of heavy metals on the mouse microbiome. BMC Pharmacol. Toxicol. 14, 1–11 (2013).

    Google Scholar 

  • 95.

    Schliebe, S. et al. Effects of sea ice extent and food availability on spatial and temporal distribution of polar bears during the fall open-water period in the Southern Beaufort Sea. Polar Biol. 31, 999–1010 (2008).

    Google Scholar 

  • 96.

    Bahrndorff, S., Alemu, T., Alemneh, T. & Lund Nielsen, J. The microbiome of animals: implications for conservation biology. Int J Genomics 2016, 5304028–5304028, doi:https://doi.org/10.1155/2016/5304028 (2016).

  • 97.

    McKenney, E., Koelle, K., Dunn, R. & Yoder, A. The ecosystem services of animal microbiomes. Mol. Ecol. 27, 2164–2172 (2018).

    CAS 
    PubMed 

    Google Scholar 

  • 98.

    Calvert, W. & Ramsay, M. A. Evaluation of age determination of polar bears by counts of cementum growth layer groups. Ursus 10, 449–453 (1998).

    Google Scholar 

  • 99.

    Iverson, S. J., Field, C., Bowen, W. D. & Blanchard, W. Quantitative fatty acid signature analysis: a new method of estimating predator diets. Ecol. Monogr 74, 211–235 (2004).

    Google Scholar 

  • 100.

    Galicia, M. P., Thiemann, G. W., Dyck, M. G. & Ferguson, S. H. Characterization of polar bear (Ursus maritimus) diets in the Canadian High Arctic. Polar Biol. 38, 1983–1992 (2015).

    Google Scholar 

  • 101.

    Bourque, J. et al. Feeding habits of a new Arctic predator: Insight from full-depth blubber fatty acid signatures of Greenland, Faroe Islands, Denmark, and managed-care killer whales Orcinus orca. Mar. Ecol. Prog. Ser. 603, 1–12 (2018).

    ADS 
    CAS 

    Google Scholar 

  • 102.

    Budge, S. M., Iverson, S. J. & Koopman, H. N. Studying trophic ecology in marine ecosystems using fatty acids: a primer on analysis and interpretation. Mar. Mamm. Sci. 22, 759–801 (2006).

    Google Scholar 

  • 103.

    Aitchison, J. The statistical analysis of compositional data. J. R. Stat. Soc.: Ser. B (Methodol.) 44, 139–160 (1982).

    MathSciNet 
    MATH 

    Google Scholar 

  • 104.

    R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, Austria, 2019).

  • 105.

    Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583. https://doi.org/10.1038/nmeth.3869 (2016).

    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 106.

    Davis, N. M., Proctor, D. M., Holmes, S. P., Relman, D. A. & Callahan, B. J. Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome 6, 1–14 (2018).

    Google Scholar 

  • 107.

    Chong, J., Liu, P., Zhou, G. & Xia, J. Using MicrobiomeAnalyst for comprehensive statistical, functional, and meta-analysis of microbiome data. Nat. Protoc. 15, 799–821. https://doi.org/10.1038/s41596-019-0264-1 (2020).

    CAS 
    Article 
    PubMed 

    Google Scholar 

  • 108.

    McMurdie, P., Holmes, S., Kindt, R., Legendre, P. & O’Hara, R. P. an R package for reproducible interactive analysis and graphics of microbiome census data. Watson M, editor. PLoS One [Internet]. Public Library of Science (2013).

  • 109.

    McMurdie, P. J. & Holmes, S. Waste Not, Want Not: Why Rarefying Microbiome Data Is Inadmissible. PLoS Comput. Biol. 10, e1003531. https://doi.org/10.1371/journal.pcbi.1003531 (2014).

    ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 110.

    Oksanen, J. et al. The vegan package. Commun. Ecol. Package 10, 719 (2007).

    Google Scholar 

  • 111.

    Anderson, M. J. Distance-based tests for homogeneity of multivariate dispersions. Biometrics 62, 245–253 (2006).

    MathSciNet 
    PubMed 
    PubMed Central 
    MATH 

    Google Scholar 

  • 112.

    Lin, H. & Peddada, S. D. Analysis of compositions of microbiomes with bias correction. Nat. Commun. 11, 3514. https://doi.org/10.1038/s41467-020-17041-7 (2020).

    ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 113.

    Rode, K. D. et al. Identifying reliable indicators of fitness in polar bears. PLoS ONE 15, e0237444. https://doi.org/10.1371/journal.pone.0237444 (2020).

    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 114.

    Burnham, K. P., Anderson, D. R. & Huyvaert, K. P. AIC model selection and multimodel inference in behavioral ecology: some background, observations, and comparisons. Behav. Ecol. Sociobiol. 65, 23–35 (2011).

    Google Scholar 


  • Source: Ecology - nature.com

    Courtney Lesoon and Elizabeth Yarina win Fulbright-Hays Scholarships

    Overcoming a bottleneck in carbon dioxide conversion