Cunningham, A. A., Daszak, P. & Wood, J. L. N. One health, emerging infectious diseases and wildlife: two decades of progress? Philos. Trans. R. Soc. B Biol. Sci. 372, 20160167 (2017).
Suzan, G., Esponda, F., Carrasco-Hernández, R. & Aguirre, A. A. in New Directions in Conservation Medicine: Applied Cases of Ecological Health (eds. Aguirre, A. A., Ostfeld, R. & Daszak, P.). 135–150 (Oxford University Press USA, 2012).
Hussain, S., Ram, M. S., Kumar, A., Shivaji, S. & Umapathy, G. Human presence increases parasitic load in endangered lion-tailed macaques (Macaca silenus) in its fragmented rainforest habitats in Southern India. PLoS ONE 8, 1–8 (2013).
Junge, R. E., Barrett, M. A. & Yoder, A. D. Effects of anthropogenic disturbance on indri (Indri indri) health in Madagascar. Am. J. Primatol. 73, 632–642 (2011).
Google Scholar
Friggens, M. M. & Beier, P. Anthropogenic disturbance and the risk of flea-borne disease transmission. Oecologia 164, 809–820 (2010).
Google Scholar
Woodroffe, R. et al. Contact with domestic dogs increases pathogen exposure in endangered African wild dogs (Lycaon pictus). PLoS ONE 7, e30099 (2012).
Crowl, T. A., Crist, T. O., Parmenter, R. R., Belovsky, G. & Lugo, A. E. The spread of invasive species and infectious disease as drivers of ecosystem change. Front. Ecol. Environ. 6, 238–246 (2008).
Google Scholar
Keesing, F., Holt, R. D. & Ostfeld, R. S. Effects of species diversity on disease risk. Ecol. Lett. 9, 485–498 (2006).
Google Scholar
Gámez-Virués, S. et al. Landscape simplification filters species traits and drives biotic homogenization. Nat. Commun. 6, 8568 (2015).
Alberdi, A., Aizpurua, O., Bohmann, K., Zepeda-Mendoza, M. L. & Gilbert, M. T. P. Do vertebrate gut metagenomes confer rapid ecological adaptation? Trends Ecol. Evol. 31, 689–699 (2016).
Google Scholar
Hooper, L. V., Littman, D. R. & Macpherson, A. J. Interactions between the microbiota and the immune system. Science 336, 1268–1273 (2012).
Google Scholar
Shapira, M. Gut microbiotas and host evolution: scaling up symbiosis. Trends Ecol. Evol. 31, 539–549 (2016).
Google Scholar
Brugman, S. et al. A comparative review on microbiota manipulation: lessons from fish, plants, livestock, and human research. Front. Nutr. 5, 1–15 (2018).
Google Scholar
Wasimuddin et al. Astrovirus infections induce age-dependent dysbiosis in gut microbiomes of bats. ISME J. 12, 2883–2893 (2018).
Google Scholar
Wasimuddin et al. Adenovirus infection is associated with altered gut microbial communities in a non-human primate. Sci. Rep. 9, 1–12 (2019).
Google Scholar
Wilkins, L. J., Monga, M. & Miller, A. W. Defining dysbiosis for a cluster of chronic diseases. Sci. Rep. 9, 1–10 (2019).
Brüssow, H. Problems with the concept of gut microbiota dysbiosis. Microb. Biotechnol. 13, 423–434 (2020).
Google Scholar
Otto, S. P. Adaptation, speciation and extinction in the Anthropocene. Proc. R. Soc. B Biol. Sci. 285, 20182047 (2018).
Amato, K. R. et al. Habitat degradation impacts black howler monkey (Alouatta pigra) gastrointestinal microbiomes. ISME J. 7, 1344–1353 (2013).
Google Scholar
Ingala, M. R., Becker, D. J., Bak Holm, J., Kristiansen, K. & Simmons, N. B. Habitat fragmentation is associated with dietary shifts and microbiota variability in common vampire bats. Ecol. Evol. https://doi.org/10.1002/ece3.5228 (2019)
Juan, P. A. S., Hendershot, J. N., Daily, G. C. & Fukami, T. Land-use change has host-specificinfluenc on avian gut microbiomes. ISME J. https://doi.org/10.1038/s41396-019-0535-4 (2019)
Barelli, C. et al. Habitat fragmentation is associated to gut microbiota diversity of an endangered primate: implications for conservation. Sci. Rep. 5, 14862 (2015).
Google Scholar
de Juan, S., Thrush, S. F. & Hewitt, J. E. Counting on β-diversity to safeguard the resilience of estuaries. PLoS ONE 8, 1–11 (2013).
Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach (Springer, 2002).
Oksanen, J. et al. vegan: Community Ecology Package. R package version 2.5-5. https://github.com/vegandevs/vegan (2019).
Nakagawa, S. & Cuthill, I. C. Effect size, confidence interval and statistical significance: a practical guide for biologists. Biol. Rev. 82, 591–605 (2007).
Google Scholar
Cohen, J. Statistical Power Analysis for the Behavioral Sciences (Lawrence Erlbaum Associates, 1988).
Gillingham, M. A. F. et al. Offspring microbiomes differ across breeding sites in a panmictic species. Front. Microbiol. 10, 35 (2019).
Anderson, M. J. Distance-based tests for homogeneity of multivariate dispersions. Biometrics 62, 245–253 (2006).
Google Scholar
Mandal, S. et al. Analysis of composition of microbiomes: a novel method for studying microbial composition. Microb. Ecol. Heal. Dis. 26, 1–7 (2015).
Douglas, G. M. et al. PICRUSt2 for prediction of metagenome functions. Nat. Biotechnol. 38, 669–673 (2020).
Google Scholar
Louca, S. & Doebeli, M. Efficient comparative phylogenetics on large trees. Bioinformatics 34, 1053–1055 (2018).
Google Scholar
Barbera, P. et al. EPA-ng: massively parallel evolutionary placement of genetic sequences. Syst. Biol. 68, 365–369 (2019).
Google Scholar
Czech, L., Barbera, P. & Stamatakis, A. Genesis and Gappa: processing, analyzing and visualizing phylogenetic (placement) data. Bioinformatics 36, 3263–3265 (2020).
Google Scholar
Nyhus, P. J. Human—wildlife conflict and coexistence. Annu. Rev. Environ. Resour. 41, 143–171 (2016).
Google Scholar
Foden, W. B. et al. Climate change vulnerability assessment of species. WIREs Clim. Chang. 10, 1–36 (2019).
Google Scholar
Beck, J. M. et al. Multicenter comparison of lung and oral microbiomes of HIV-infected and HIV-uninfected individuals. Am. J. Respir. Crit. Care Med. 192, 1335–1344 (2015).
Google Scholar
Pita, L., Rix, L., Slaby, B. M., Franke, A. & Hentschel, U. The sponge holobiont in a changing ocean: from microbes to ecosystems. Microbiome 6, 46 (2018).
Google Scholar
Rosado, P. M. et al. Marine probiotics: increasing coral resistance to bleaching through microbiome manipulation. ISME J. 13, 921–936 (2019).
Google Scholar
Wang, L. et al. Corals and their microbiomes are differentially affected by exposure to elevated nutrients and a natural thermal anomaly. Front. Mar. Sci. 5, 1–16 (2018).
Google Scholar
Zaneveld, J. R., McMinds, R. & Thurber, R. V. Stress and stability: applying the Anna Karenina principle to animal microbiomes. Nat. Microbiol. 2, 17121 (2017).
Rocca, J. D. et al. The Microbiome Stress Project: toward a global meta-analysis of environmental stressors and their effects on microbial communities. Front. Microbiol. 10, 3272 (2019).
Gillingham, M. A. F. et al. Bioaccumulation of trace elements affects chick body condition and gut microbiome in greater flamingos. Sci. Total Environ. 761, 143250 (2020).
Chase, J. M. Stochastic community assembly causes higher biodiversity in more productive environments. Science 328, 1388–1392 (2010).
Google Scholar
Jiménez, R. R., Alvarado, G., Estrella, J. & Sommer, S. Moving beyond the host: unraveling the skin microbiome of endangered Costa Rican amphibians. Front. Microbiol. 10, 1–18 (2019).
Google Scholar
Wang, J. et al. Phylogenetic beta diversity in bacterial assemblages across ecosystems: deterministic versus stochastic processes. ISME J. 7, 1310–1321 (2013).
Google Scholar
Chase, J. M. & Myers, J. A. Disentangling the importance of ecological niches from stochastic processes across scales. Philos. Trans. R. Soc. B Biol. Sci. 366, 2351–2363 (2011).
Google Scholar
Pound, K. L., Lawrence, G. B. & Passy, S. I. Beta diversity response to stress severity and heterogeneity in sensitive versus tolerant stream diatoms. Divers. Distrib. 25, 374–384 (2019).
Google Scholar
Zhou, J. & Ning, D. Stochastic Community Assembly: does it matter in microbial ecology? Microbiol. Mol. Biol. Rev. 81, 1–32 (2017).
Google Scholar
Nicholas, R. A. J. & Ayling, R. D. Mycoplasma bovis: disease, diagnosis, and control. Res. Vet. Sci. 74, 105–112 (2003).
Google Scholar
Ley, D. H. in Diseases of Poultry (eds. et al.) (Blackwell Publishing, 2008).
Groebel, K., Hoelzle, K., Wittenbrink, M. M., Ziegler, U. & Hoelzle, L. E. Mycoplasma suis invades porcine erythrocytes. Infect. Immun. 77, 576–584 (2009).
Google Scholar
do Nascimento, N. C., Santos, A. P., Guimaraes, A. M. S., Sanmiguel, P. J. & Messick, J. B. Mycoplasma haemocanis—the canine hemoplasma and its feline counterpart in the genomic era. Vet. Res. 43, 66 (2012).
Hardham, J. M. et al. Transfer of Bacteroides splanchnicus to Odoribacter gen. nov. as Odoribacter splanchnicus comb. nov., and description of Odoribacter denticanis sp. nov., isolated from the crevicular spaces of canine periodontitis patients. Int. J. Syst. Evol. Microbiol. 58, 103–109 (2008).
Google Scholar
Kaakoush, N. O. Insights into the role of Erysipelotrichaceae in the human host. Front. Cell. Infect. Microbiol. 5, 1–4 (2015).
Google Scholar
Ormerod, K. L. et al. Genomic characterization of the uncultured Bacteroidales family S24-7 inhabiting the guts of homeothermic animals. Microbiome 4, 1–17 (2016).
Google Scholar
Herrmann, E. et al. RNA-based stable isotope probing suggests Allobaculum spp. as particularly active glucose assimilators in a complex murine microbiota cultured in vitro. Biomed Res. Int. 2017, 1829685 (2017).
Greetham, H. L. et al. Allobaculum stercoricanis gen. nov., sp. nov., isolated from canine feces. Anaerobe 10, 301–307 (2004).
Google Scholar
Silva, Y. P., Bernardi, A. & Frozza, R. L. The role of short-chain fatty acids from gut microbiota in gut-brain communication. Front. Endocrinol. 11, 1–14 (2020).
Wiegel, J., Tanner, R. & Rainey, F. A. in The Prokaryotes: Volume 4: Bacteria: Firmicutes, Cyanobacteria (eds. Dworkin, M., Falkow, S., Rosenberg, E., Schleifer, K.-H. & Stackebrandt, E.) 654–678 (Springer US, 2006).
Tamanai-Shacoori, Z. et al. Roseburia spp.: a marker of health? Future Microbiol 12, 157–170 (2017).
Google Scholar
Freier, T. A., Beitz, D. C., Li, L. & Hartman, P. A. Characterization of Eubacterium coprostanoligenes sp. nov., a Cholesterol-Reducing Anaerobe. Int. J. Syst. Bacteriol. 44, 137–142 (1994).
Google Scholar
Venegas, D. P. et al. Short chain fatty acids (SCFAs)-mediated gut epithelial and immune regulation and its relevance for inflammatory bowel diseases. Front. Immunol. 10, 277 (2019).
MetaCyc. MetaCyc Pathway: pyrimidine deoxyribonucleotides biosynthesis from CTP. https://biocyc.org/META/NEW-IMAGE?type=PATHWAY&object=PWY-7210&show-citations=T (2020).
Caspi, R. et al. The MetaCyc database of metabolic pathways and enzymes. Nucleic Acids Res. 46, D633–D639 (2018).
Google Scholar
MetaCyc. MetaCyc Pathway: poly(glycerol phosphate) wall teichoic acid biosynthesis. https://biocyc.org/META/NEW-IMAGE?type=PATHWAY&object=TEICHOICACID-PWY (2020).
Brown, S., Santa Maria, J. P. & Walker, S. Wall teichoic acids of gram-positive bacteria. Annu. Rev. Microbiol. 67, 313–336 (2013).
Google Scholar
MetaCyc. MetaCyc Pathway: L-lysine biosynthesis II. https://metacyc.org/META/NEW-IMAGE?type=PATHWAY&object=PWY-2941 (2020).
Hutton, C. A., Perugini, M. A. & Gerrard, J. A. Inhibition of lysine biosynthesis: an evolving antibiotic strategy. Mol. Biosyst. 3, 458–465 (2007).
Google Scholar
Wanner, S. et al. Wall teichoic acids mediate increased virulence in Staphylococcus aureus. Nat. Microbiol. 2, 1–12 (2017).
MetaCyc. MetaCyc Pathway: formaldehyde assimilation II (assimilatory RuMP Cycle). https://biocyc.org/META/NEW-IMAGE?type=PATHWAY&object=PWY-1861 (2020).
Chen, N. H., Djoko, K. Y., Veyrier, F. J. & McEwan, A. G. Formaldehyde stress responses in bacterial pathogens. Front. Microbiol. 7, 1–17 (2016).
Tauseef, S. M., Premalatha, M., Abbasi, T. & Abbasi, S. A. Methane capture from livestock manure. J. Environ. Manag. 117, 187–207 (2013).
Google Scholar
Dale, V. H., Brown, S., Calderón, M. O., Montoya, A. S. & Martínez, R. E. Estimating baseline carbon emissions for the eastern Panama Canal watershed. Mitig. Adapt. Strateg. Glob. Chang 8, 323–348 (2003).
Google Scholar
Schmid, J. et al. Ecological drivers of Hepacivirus infection in a neotropical rodent inhabiting landscapes with various degrees of human environmental change. Oecologia https://doi.org/10.1007/s00442-018-4210-7 (2018)
Adler, G. H. & Beatty, R. P. Changing reproductive rates in a neotropical forest rodent, Proechimys semispinosus. J. Anim. Ecol. 66, 472 (1997).
Google Scholar
Adler, G. H. Fruit and seed exploitation by Central American spiny rats, Proechimys semispinosus. Stud. Neotrop. Fauna Environ. 30, 237–244 (1995).
Hoch, G. A. & Adler, G. H. Removal of black palm (Astrocaryum standleyanum) seeds by spiny rats (Proechimys semispinosus). J. Trop. Ecol. 13, 51–58 (1997).
Google Scholar
Endries, M. J. & Adler, G. H. Spacing patterns of a tropical forest rodent, the spiny rat (Proechimys semispinosus), in Panama. J. Zool. 265, 147–155 (2005).
Google Scholar
Adler, G. H. The island syndrome in isolated populations of a tropical forest rodent. Oecologia 108, 694–700 (1996).
Google Scholar
Caporaso, J. G. et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. PNAS 108, 4516–4522 (2011).
Google Scholar
Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336 (2010).
Google Scholar
Menke, S. et al. Oligotyping reveals differences between gut microbiomes of free-ranging sympatric Namibian carnivores (Acinonyx jubatus, Canis mesomelas) on a bacterial species-like level. Front. Microbiol. 5, 526 (2014).
Google Scholar
Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).
Google Scholar
Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).
Google Scholar
Callahan, B. J., Mcmurdie, P. J. & Holmes, S. P. Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. ISME J. 11, 2639–2643 (2017).
Google Scholar
Quast, C. et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 41, 590–596 (2013).
Google Scholar
Yilmaz, P. et al. The SILVA and ‘All-species Living Tree Project (LTP)’ taxonomic frameworks. Nucleic Acids Res. 42, 643–648 (2014).
Google Scholar
Glöckner, F. O. et al. 25 years of serving the community with ribosomal RNA gene reference databases and tools. J. Biotechnol. 261, 169–176 (2017).
Google Scholar
Pedregosa, F. et al. Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011).
Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2—āpproximately maximum-likelihood trees for large alignments. PLoS ONE 5, e9490 (2010).
Huson, D. H. & Scornavacca, C. Dendroscope 3: an interactive tool for rooted phylogenetic trees and networks. Syst. Biol. 61, 1061–1067 (2012).
Google Scholar
R. Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing. http://www.r-project.org/index.html (2017).
McMurdie, P. J. & Holmes, S. phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).
Davis, N. M., Proctor, Di. 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
Shannon, C. E. A mathematical theory of communication. Bell Syst. Tech. J. 27, 379–423 (1948).
Google Scholar
Faith, D. P. Conservation evaluation and phylogenetic diversity. Biol. Conserv. 61, 1–10 (1992).
Google Scholar
Bates, D., Mächler, M., Bolker, B. M. & Walker, S. C. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
Mcmurdie, P. J. & Holmes, S. Waste not, want not: why rarefying microbiome data is inadmissible. PLoS Comput. Biol. 10, e1003531 (2014).
Kim, Y. S., Unno, T., Kim, B.-Y. & Park, M. Sex differences in gut microbiota. World J. Mens. Health 38, 48–60 (2020).
Google Scholar
Kolodny, O. et al. Coordinated change at the colony level in fruit bat fur microbiomes through time. Nat. Ecol. Evol. 3, 116–124 (2019).
Google Scholar
Kartzinel, T. R., Hsing, J. C., Musili, P. M., Brown, B. R. P. & Pringle, R. M. Covariation of diet and gut microbiome in African megafauna. Proc. Natl Acad. Sci. USA 116, 23588–23593 (2019).
Google Scholar
Zuur, A., Ieno, E. N., Walker, N., Saveliev, A. A. & Smith, G. M. Mixed Effects Models and Extensions in Ecology with R (Springer Science & Business Media, 2009).
Nakagawa, S. & Schielzeth, H. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods Ecol. Evol. 4, 133–142 (2013).
Google Scholar
Brooks, M. E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R. J. 9, 378–400 (2017).
Google Scholar
Lozupone, C. & Knight, R. UniFrac: a new phylogenetic method for comparing microbial communities. Appl. Environ. Microbiol. 71, 8228–8235 (2005).
Google Scholar
Lozupone, C. A., Hamady, M., Kelley, S. T. & Knight, R. Quantitative and qualitative β diversity measures lead to different insights into factors that structure microbial communities. Appl. Environ. Microbiol. 73, 1576–1585 (2007).
Google Scholar
Anderson, M. J. Permutational Multivariate Analysis of Variance (PERMANOVA). https://doi.org/10.1002/9781118445112.stat07841. (2017)
Anderson, M. J. & Walsh, D. C. I. PERMANOVA, ANOSIM, and the Mantel test in the face of heterogeneous dispersions: what null hypothesis are you testing? Ecol. Monogr. 83, 557–574 (2013).
Google Scholar
Li, H. et al. Pika population density is associated with the composition and diversity of gut microbiota. Front. Microbiol. 7, 1–9 (2016).
Weiss, S. et al. Normalization and microbial differential abundance strategies depend upon data characteristics. Microbiome 5, 1–18 (2017).
Google Scholar
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B 57, 289–300 (1995).
Fackelmann, G. gfackelmann/human-encroachment-into-wildlife-gut-microbiomes: Release 1.0.0. https://doi.org/10.5281/zenodo.4725220. (2021)
Source: Ecology - nature.com
