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Analysis of Ecological Networks in Multicomponent Communities of Microorganisms: Possibilities, Limitations, and Potential Errors

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Abstract—

Owing to the high resolving power and efficiency of DNA sequencing, researchers have discovered the extremely high diversity of bacterial, fungal, protist, and microinvertebrate communities in the soil, wood, phyllosphere, and other natural media. Studies on the properties of these communities require powerful tools for analyzing multicomponent systems. One of them is the analysis of ecological networks, which makes it possible to solve a broad range of problems. This review briefly describes the possibilities of network analysis, its concepts, and metrics of network topology.It also indicates limitations related to specific features of DNA sequencing (compositionality and data sparsity) and potential sources of errors in the interpretation of results (relic DNA, artifactual DNA sequences and spurious connections in a network). The focus is on the communities of microorganisms, but the discussed issues are relevant for most other groups of the biota.

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REFERENCES

  1. Ives, A.R. and Carpenter, S.R., Stability and diversity of ecosystems, Science, 2007, vol. 317, no. 5834, pp. 58–62. https://doi.org/10.1126/science.1133258

    Article  CAS  PubMed  Google Scholar 

  2. Pimm, S.L., The complexity and stability of ecosystems, Nature, 1984, vol. 307, no. 5949, pp. 321–326. https://doi.org/10.1038/307321a0

    Article  Google Scholar 

  3. Tylianakis, J.M., Didham, R.K., Bascompte, J., and Wardle, D.A., Global change and species interactions in terrestrial ecosystems, Ecol. Lett., 2008, vol. 11, no. 12, pp. 1351–1363. https://doi.org/10.1111/j.1461-0248.2008.01250.x

    Article  PubMed  Google Scholar 

  4. Begon, M., Harper, J.L., and Townsend, C.R., Ecology: Individuals, Populations, and Communities, Oxford: Blackwell, 1986. Translated under the title Ekologiya: Osobi, populyatsii i soobshchestva, Moscow: Mir, 1989.

  5. Layeghifard, M., Hwang, D.M., and Guttman, D.S., Disentangling interactions in the microbiome: A network perspective, Trends Microbiol., 2017, vol. 25, no. 3, pp. 217–228. https://doi.org/10.1016/j.tim.2016.11.008

    Article  CAS  PubMed  Google Scholar 

  6. Nannipieri, P., Ascher, J., Ceccherini, M.T., et al., Microbial diversity and soil functions, Eur. J. Soil Sci., 2003, vol. 54, no. 4, pp. 655–670. https://doi.org/10.1046/j.1351-0754.2003.0556.x

    Article  Google Scholar 

  7. Metzker, M.L., Sequencing technologies – the next generation, Nat. Rev. Genet., 2010, vol. 11, no. 1, pp. 31–46. https://doi.org/10.1038/nrg2626

    Article  CAS  PubMed  Google Scholar 

  8. Bahram, M., Netherway, T., Hildebrand, F., et al., Plant nutrient-acquisition strategies drive topsoil microbiome structure and function, New Phytol., 2020, vol. 227, no. 4, pp. 1189–1199. https://doi.org/10.1111/nph.16598

    Article  CAS  PubMed  Google Scholar 

  9. Davison, J., Moora, M., Öpik, M., et al., Global assessment of arbuscular mycorrhizal fungus diversity reveals very low endemism, Science, 2015, vol. 349, no. 6251, pp. 970–973. https://doi.org/10.1126/science.aab1161

    Article  CAS  PubMed  Google Scholar 

  10. Tedersoo, L., Bahram, M., Põlme, S., et al., Global diversity and geography of soil fungi, Science, 2014, vol. 346, no. 6213, p. 1078. https://doi.org/10.1126/Science.1256688

    Article  CAS  Google Scholar 

  11. Tisthammer, K.H., Cobian, G.M., and Amend, A.S., Global biogeography of marine fungi is shaped by the environment, Fungal Ecol., 2016, vol. 19, pp. 39–46.

    Article  Google Scholar 

  12. Weiss, S., van Treuren, W., Lozupone, C., et al., Correlation detection strategies in microbial data sets vary widely in sensitivity and precision, ISME J., 2016, vol. 10, no. 7, pp. 1669–1681. https://doi.org/10.1038/ismej.2015.235

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Faust, K. and Raes, J., Microbial interactions: From networks to models, Nat. Rev. Microbiol., 2012, vol. 10, no. 8, pp. 538–550. https://doi.org/10.1038/nrmicro2832

    Article  CAS  PubMed  Google Scholar 

  14. Newman, M., Networks: An Introduction, Oxford: Oxford Univ. Press, 2010. https://doi.org/10.1093/acprof:oso/9780199206650.001.0001

  15. Jacquiod, S., Puga-Freitas, R., Spor, A., et al., A core microbiota of the plant–earthworm interaction conserved across soils, Soil Biol. Biochem., 2020, vol. 144. https://doi.org/10.1016/j.soilbio.2020.107754

  16. Tipton, L., Muller, C.L., Kurtz, Z.D., et al., Fungi stabilize connectivity in the lung and skin microbial ecosystems, Microbiome, 2018, vol. 6, no. 1, p. 12. https://doi.org/10.1186/s40168-017-0393-0

    Article  PubMed  PubMed Central  Google Scholar 

  17. Jiang, Y.J., Sun, B., Li, H.X., et al., Aggregate-related changes in network patterns of nematodes and ammonia oxidizers in an acidic soil, Soil Biol. Biochem., 2015, vol. 88, pp. 101–109. https://doi.org/10.1016/j.soilbio.2015.05.013

    Article  CAS  Google Scholar 

  18. Darcy, J.L., Swift, S.O.I., Cobian, G.M., et al., Fungal communities living within leaves of native Hawaiian dicots are structured by landscape-scale variables as well as by host plants, Mol. Ecol., 2020, vol. 29, pp. 3102–3115. https://doi.org/10.1111/mec.15544

    Article  Google Scholar 

  19. Banerjee, S., Thrall, P.H., Bissett, A., et al., Linking microbial co-occurrences to soil ecological processes across a woodland–grassland ecotone, Ecol. Evol., 2018, vol. 8, no. 16, pp. 8217–8230. https://doi.org/10.1002/ece3.4346

    Article  PubMed  PubMed Central  Google Scholar 

  20. Cram, J.A., Xia, L.C., Needham, D.M., et al., Cross-depth analysis of marine bacterial networks suggests downward propagation of temporal changes, ISME J., 2015, vol. 9, no. 12, pp. 2573–2586. https://doi.org/10.1038/ismej.2015.76

    Article  PubMed  PubMed Central  Google Scholar 

  21. Chaffron, S., Rehrauer, H., Pernthaler, J., and von Mering, C., A global network of coexisting microbes from environmental and whole-genome sequence data, Genome Res., 2010, vol. 20, no. 7, pp. 947–959. https://doi.org/10.1101/gr.104521.109

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Meyer, J.L., Gunasekera, S.P., Scott, R.M., et al., Microbiome shifts and the inhibition of quorum sensing by black band disease cyanobacteria, ISME J., 2016, vol. 10, no. 5, pp. 1204–1216. https://doi.org/10.1038/ismej.2015.184

    Article  CAS  PubMed  Google Scholar 

  23. Pollet, T., Berdjeb, L., Garnier, C., et al., Prokaryotic community successions and interactions in marine biofilms: The key role of flavobacteriia, FEMS Microbiol. Ecol., 2018, vol. 94, no. 6. https://doi.org/10.1093/femsec/fiy083

  24. Rottjers, L. and Faust, K., From hairballs to hypotheses: Biological insights from microbial networks, FEMS Microbiol. Rev., 2018, vol. 42, no. 6, pp. 761–780. https://doi.org/10.1093/femsre/fuy030

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Deng, Y., Jiang, Y.-H., Yang, Y., et al., Molecular ecological network analyses, BMC Bioinformatics, 2012, vol. 13, no. 1, p. 113. https://doi.org/10.1186/1471-2105-13-113

    Article  PubMed  PubMed Central  Google Scholar 

  26. Dattilo, W. and Rico-Gray, V., Ecological Networks in the Tropics: An Integrative Overview of Species Interactions from Some of the Most Species-rich Habitats on Earth, Cham: Springer, 2018. https://doi.org/10.1007/978-3-319-68228-0.

  27. Junker, B.H. and Schreiber, F., Analysis of Biological Networks, Hoboken, NJ: Wiley, 2008.

    Book  Google Scholar 

  28. Kepes, F., Biological Networks, Singapore: World Scientific, 2007.

    Book  Google Scholar 

  29. Woodward, G., Advances in Ecological Research: Ecological Networks, Amsterdam: Elsevier, 2010.

    Google Scholar 

  30. Semenov, M.V., Metabarcoding and metagenomics in soil-ecological research: Achievements, Problems, and Possibilities, Zh. Obshch. Biol., 2019, vol. 80, no. 6, pp. 403–417. https://doi.org/10.1134/S004445961906006X

    Article  Google Scholar 

  31. Salazar, G., Cornejo-Castillo, F.M., Benitez-Barrios, V., et al., Global diversity and biogeography of deep-sea pelagic prokaryotes, ISME J., 2016, vol. 10, no. 3, pp. 596–608. https://doi.org/10.1038/ismej.2015.137

    Article  PubMed  Google Scholar 

  32. Pawlowski, J., Audic, S., Adl, S., et al., CBOL protist working group: Barcoding eukaryotic richness beyond the animal, plant, and fungal kingdoms, PLoS Biol., 2012, vol. 10, no. 11. https://doi.org/10.1371/journal.pbio.1001419

  33. Mikryukov, V.S., Dulya, O.V., and Modorov, M.V., Phylogenetic signature of fungal response to long-term chemical pollution, Soil Biol. Biochem., 2020, vol. 140, Article no. 107644. https://doi.org/10.1016/j.soilbio.2019.107644

    Article  CAS  Google Scholar 

  34. Porter, T.M., Morris, D.M., Basiliko, N., et al., Variations in terrestrial arthropod DNA metabarcoding methods recovers robust beta diversity but variable richness and site indicators, Sci. Rep., 2019, vol. 9, Article no. 18218. https://doi.org/10.1038/s41598-019-54532-0

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Chernov, T.I., Zhelezova, O.D., Kutovaya, O.V., et al., Comparative analysis of the structure of buried and surface soils by analysis of microbial DNA, Microbiology (Moscow), 2018, vol. 87, no. 6, pp. 833–841. https://doi.org/10.1134/S0026261718060073

    Article  CAS  Google Scholar 

  36. Walters, K.E. and Martiny, J.B.H., Alpha-, beta-, and gamma-diversity of bacteria varies across habitats, PLoS One, 2020, vol. 15, no. 9, e0233872. https://doi.org/10.1371/journal.pone.0233872

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Schoch, C.L., Seifert, K.A., Huhndorf, S., et al., Nuclear ribosomal internal transcribed spacer (ITS) region as a universal DNA barcode marker for fungi, Proc. Natl. Acad. Sci. U. S. A., 2012, vol. 109, no. 16, pp. 6241–6246. https://doi.org/10.1073/pnas.1117018109

    Article  PubMed  PubMed Central  Google Scholar 

  38. Pace, N.R., A molecular view of microbial diversity and the biosphere, Science, 1997, vol. 276, no. 5313, pp. 734–740. https://doi.org/10.1126/science.276.5313.734

    Article  CAS  PubMed  Google Scholar 

  39. Liu, M., Clarke, L.J., Baker, S.C., et al., A practical guide to DNA metabarcoding for entomological ecologists, Ecol. Entomol., 2020, vol. 45, no. 3, pp. 373–385. https://doi.org/10.1111/een.12831

    Article  Google Scholar 

  40. Callahan, B.J., McMurdie, P.J., and Holmes, S.P., Exact sequence variants should replace operational taxonomic units in marker-gene data analysis, ISME J., 2017, vol. 11, no. 12, pp. 2639–2643. https://doi.org/10.1038/ismej.2017.119

    Article  PubMed  PubMed Central  Google Scholar 

  41. Nilsson, R.H., Larsson, K.H., Taylor, A.F.S., et al., The UNITE database for molecular identification of fungi: Handling dark taxa and parallel taxonomic classifications, Nucleic Acids Res., 2019, vol. 47, no. D1, pp. D259–D264. https://doi.org/10.1093/nar/gky1022

    Article  CAS  PubMed  Google Scholar 

  42. Pruesse, E., Quast, C., Knittel, K., et al., SILVA: A comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB, Nucleic Acids Res., 2007, vol. 35, no. 21, pp. 7188–7196. https://doi.org/10.1093/nar/gkm864

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Guillou, L., Bachar, D., Audic, S., et al., The Protist Ribosomal Reference database (PR2): A catalog of unicellular eukaryote small sub-unit rRNA sequences with curated taxonomy, Nucleic Acids Res., 2013, vol. 41, no. D1, pp. 597–604. https://doi.org/10.1093/nar/gks1160

    Article  CAS  Google Scholar 

  44. Ratnasingham, S. and Hebert, P.D., BOLD: The barcode of life data system (http://www.barcodinglife.org), Mol. Ecol. Notes, 2007, vol. 7, no. 3, pp. 355–364. https://doi.org/10.1111/j.1471-8286.2007.01678.x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Gloor, G.B., Macklaim, J.M., Pawlowsky-Glahn, V., and Egozcue, J.J., Microbiome datasets are compositional: and this is not optional, Front. Microbiol., 2017, vol. 8, Article no. 2224. https://doi.org/10.3389/fmicb.2017.02224

    Article  PubMed  PubMed Central  Google Scholar 

  46. Morton, J.T., Marotz, C., Washburne, A., et al., Establishing microbial composition measurement standards with reference frames, Nat. Commun., 2019, vol. 10, no. 1, p. 2719. https://doi.org/10.1038/s41467-019-10656-5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Martin-Fernández, J.-A., Hron, K., Templ, M., et al., Bayesian-multiplicative treatment of count zeros in compositional data sets, Stat. Model., 2015, vol. 15, no. 2, pp. 134–158. https://doi.org/10.1177/1471082X14535524

    Article  Google Scholar 

  48. Freeman, L.C., The Development of Social Network Analysis: A Study in the Sociology of Science, Vancouver BC: Empirical Press, 2004.

    Google Scholar 

  49. Fernandes, A.D., Reid, J.N., Macklaim, J.M., et al., Unifying the analysis of high-throughput sequencing datasets: Characterizing RNA-seq, 16s rRNA gene sequencing and selective growth experiments by compositional data analysis, Microbiome, 2014, vol. 2, p. 15. https://doi.org/10.1186/2049-2618-2-15

    Article  PubMed  PubMed Central  Google Scholar 

  50. Yoon, G., Gaynanova, I., and Muller, C.L., Microbial networks in SPRING – Semi-parametric rank-based correlation and partial correlation estimation for quantitative microbiome data, Front. Genet., 2019, vol. 10, p. 516. https://doi.org/10.3389/fgene.2019.00516

    Article  PubMed  PubMed Central  Google Scholar 

  51. Faust, K., Sathirapongsasuti, J.F., Izard, J., et al., Microbial co-occurrence relationships in the human microbiome, PLoS Comput. Biol., 2012, vol. 8, no. 7. https://doi.org/10.1371/journal.pcbi.1002606

  52. Hirano, H. and Takemoto, K., Difficulty in inferring microbial community structure based on co-occurrence network approaches, BMC Bioinformatics, 2019, vol. 20. https://doi.org/10.1186/s12859-019-2915-1

  53. Csardi, G. and Nepusz, T., The igraph software package for complex network research, InterJournal, Complex Systems, 2006, vol. 1695, no. 5, pp. 1–9.

    Google Scholar 

  54. Bastian, M., Heymann, S., and Jacomy, M., Gephi: An open source software for exploring and manipulating networks, in Proc. Third International AAAI Conf. on Weblogs and Social Media, 2009, vol. 8, pp. 361–362. https://doi.org/10.13140/2.1.1341.1520.

  55. Shannon, P., Markiel, A., Ozier, O., et al., Cytoscape: A software environment for integrated models of biomolecular interaction networks, Genome Res., 2003, vol. 13, no. 11, pp. 2498–2504. https://doi.org/10.1101/gr.1239303

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Friedman, J. and Alm, E.J., Inferring correlation networks from genomic survey data, PLoS Comput. Biol., 2012, vol. 8, no. 9. https://doi.org/10.1371/journal.pcbi.1002687

  57. Schwager E., Weingart G., Bielski C. CCREPE: Compositionality corrected by permutation and renormalization. https://www.bioconductor.org/packages/devel/ bioc/html/ccrepe.html.

  58. Reshef, D.N., Reshef, Y.A., Finucane, H.K., et al., Detecting novel associations in large datasets, Science, 2011, vol. 334, no. 6062, pp. 1518–1524. https://doi.org/10.1126/science.1205438

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Ruan, Q., Dutta, D., Schwalbach, M.S., et al., Local similarity analysis reveals unique associations among marine bacterioplankton species and environmental factors, Bioinformatics, 2006, vol. 22, no. 20, pp. 2532–2538. https://doi.org/10.1093/bioinformatics/btl417

    Article  CAS  PubMed  Google Scholar 

  60. Fang, H., Huang, C., Zhao, H., and Deng, M., CCLasso: Correlation inference for compositional data through Lasso, Bioinformatics, 2015, vol. 31, no. 19, pp. 3172–3180. https://doi.org/10.1093/bioinformatics/btv349

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Quinn, T.P., Richardson, M.F., Lovell, D., and Crowley, T.M., Propr: An R-package for identifying proportionally abundant features using compositional data analysis, Sci. Rep., 2017, vol. 7. https://doi.org/10.1038/s41598-017-16520-0

  62. Ban, Y., An l., and Jiang, H., Investigating microbial co-occurrence patterns based on metagenomic compositional data, Bioinformatics, 2015, vol. 31, no. 20, pp. 3322–3329. https://doi.org/10.1093/bioinformatics/btv364

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Faust, K. and Raes, J., CoNet app: Inference of biological association networks using Cytoscape, F1000Research, 2016, vol. 5. https://doi.org/10.12688/f1000research.9050.2

  64. Antoniazzi, R., Dattilo, W., and Rico-Gray, V., A useful guide of main indices and software used for ecological networks studies, in Ecological Networks in the Tropics, Dattilo, W. and Rico-Gray, V., Eds., Cham: Springer, 2018, pp. 185–196. https://doi.org/10.1007/978-3-319-68228-0_13.

  65. Dunne, J.A., Williams, R.J., and Martinez, N.D., Food-web structure and network theory: The role of connectance and size, Proc. Natl. Acad. Sci. U. S. A., 2002, vol. 99, no. 20, pp. 12917–12922. https://doi.org/10.1073/pnas.192407699

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Delmas, E., Besson, M., Brice, M.H., et al., Analyzing ecological networks of species interactions, Biol. Rev., 2019, vol. 94, no. 1, pp. 16–36. https://doi.org/10.1111/brv.12433

    Article  Google Scholar 

  67. Lupatini, M., Suleiman, A.K.A., Jacques, R.J.S., et al., Network topology reveals high connectance levels and few key microbial genera within soils, Front. Environ. Sci., 2014, vol. 2. https://doi.org/10.3389/fenvs.2014.00010

  68. Mikhailov, I.S., Zakharova, Y.R., Bukin, Y.S., et al., Co-occurrence networks among bacteria and microbial eukaryotes of Lake Baikal during a spring phytoplankton bloom, Octolasion cyaneum, Microb. Ecol., 2019, vol. 77, no. 1, pp. 96–109. https://doi.org/10.1007/s00248-018-1212-2

    Article  PubMed  Google Scholar 

  69. MacArthur R. Fluctuations of animal populations and a measure of community stability, Ecology, 1955, vol. 36, no. 3, pp. 533–536. https://doi.org/10.2307/1929601

    Article  Google Scholar 

  70. May, R.M., Will a large complex system be stable?, Nature, 1972, vol. 238, no. 5364, pp. 413–414. https://doi.org/10.1038/238413a0

    Article  CAS  PubMed  Google Scholar 

  71. Landi, P., Minoarivelo, H.O., Brannstrom, A., et al., Complexity and stability of ecological networks: A review of the theory, Popul. Ecol., 2018, vol. 60, no. 4, pp. 319–345. https://doi.org/10.1007/s10144-018-0628-3

    Article  Google Scholar 

  72. Jalili, M., Salehzadeh-Yazdi, A., Asgari, Y., et al., CentiServer: A comprehensive resource, web-based application and R package for centrality analysis, PLoS One, 2015, vol. 10, no. 11, e0143111. https://doi.org/10.1371/journal.pone.0143111

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Lau, M.K., Borrett, S.R., Baiser, B., et al., Ecological network metrics: Opportunities for synthesis, Ecosphere, 2017, vol. 8, no. 8, e01900. https://doi.org/10.1002/ecs2.1900

    Article  Google Scholar 

  74. Faust, K., Bauchinger, F., Laroche, B., et al., Signatures of ecological processes in microbial community time series, Microbiome, 2018, vol. 6, Article no. 120. https://doi.org/10.1186/s40168-018-0496-2

    Article  PubMed  PubMed Central  Google Scholar 

  75. Rottjers, L. and Faust, K., Can we predict keystones?, Nat. Rev. Microbiol., 2019, vol. 17, no. 3, pp. 193–193. https://doi.org/10.1038/s41579-018-0132-y

    Article  CAS  PubMed  Google Scholar 

  76. Agler, M.T., Ruhe, J., Kroll, S., et al., Microbial hub taxa link host and abiotic factors to plant microbiome variation, PLoS Biol., 2016, vol. 14, no. 1, pp. 1–31. https://doi.org/10.1371/journal.pbio.1002352

    Article  CAS  Google Scholar 

  77. Douglas, G.M., Maffei, V.J., Zaneveld, J.R., et al., PICRUSt2 for prediction of metagenome functions, Nat. Biotechnol., 2020, vol. 38, no. 6, pp. 685–688. https://doi.org/10.1038/s41587-020-0548-6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Newman, M.E.J., Assortative mixing in networks, Phys. Rev. Lett., 2002, vol. 89, no. 20, 208701. https://doi.org/10.1103/PhysRevLett.89.208701

    Article  CAS  PubMed  Google Scholar 

  79. Yang, Z., Algesheimer, R., and Tessone, C.J., A comparative analysis of community detection algorithms on artificial networks, Sci. Rep., 2016, vol. 6. https://doi.org/10.1038/srep30750

  80. Piraveenan, M., Uddin, S., and Chung, K.S.K., Measuring topological robustness of networks under sustained targeted attacks, in 2012 IEEE/ACM Int. Conf. on Advances in Social Networks Analysis and Mining (Asonam), 2012, pp. 38–45. https://doi.org/10.1109/Asonam.2012.17.

  81. Peel, L., Delvenne, J.C., and Lambiotte, R., Multiscale mixing patterns in networks, Proc. Natl. Acad. Sci. U. S. A., 2018, vol. 115, no. 16, pp. 4057–4062. https://doi.org/10.1073/pnas.1713019115

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. R Core Team, R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, 2020. https://www.R-project.org/

  83. Milo, R., Shen-Orr, S., Itzkovitz, S., et al., Network motifs: Simple building blocks of complex networks, Science, 2002, vol. 298, no. 5594, pp. 824–827. https://doi.org/10.1126/science.298.5594.824

    Article  CAS  PubMed  Google Scholar 

  84. Giling, D.P., Ebeling, A., Eisenhauer, N., et al., Plant diversity alters the representation of motifs in food webs, Nat. Commun., 2019, vol. 10, no. 1, p. 1226. https://doi.org/10.1038/s41467-019-08856-0

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Stouffer, D.B., Camacho, J., Jiang, W., and Amaral, L.A.N., Evidence for the existence of a robust pattern of prey selection in food webs, Proc. R. Soc. Lond. B, 2007, vol. 274, no. 1621, pp. 1931–1940. https://doi.org/10.1098/rspb.2007.0571

    Article  Google Scholar 

  86. Dell’Anno, A. and Danovaro, R., Extracellular DNA plays a key role in deep-sea ecosystem functioning, Science, 2005, vol. 309, no. 5744, pp. 2179–2179. https://doi.org/10.1126/science.1117475

    Article  PubMed  Google Scholar 

  87. Carini, P., Delgado-Baquerizo, M., Hinckley, E.L.S., et al., Effects of spatial variability and relic DNA removal on the detection of temporal dynamics in soil microbial communities, mBbio, 2020, vol. 11, no. 1, e02776–02719. https://doi.org/10.1128/mBio.02776-19

    Article  CAS  Google Scholar 

  88. Lennon, J.T., Muscarella, M.E., Placella, S.A., and Lehmkuhl, B.K., How, when, and where relic DNA affects microbial diversity, mBio, 2018, vol. 9, no. 3. e00637–00618. https://doi.org/10.1128/mBio.00637-18

    Article  PubMed  PubMed Central  Google Scholar 

  89. Wagner, A.O., Malin, C., Knapp, B.A., and Illmer, P., Removal of free extracellular DNA from environmental samples by ethidium monoazide and propidium monoazide, Appl. Environ. Microbiol., 2008, vol. 74, no. 8, pp. 2537–2539. https://doi.org/10.1128/Aem.02288-07

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Baldrian, P., Kolařík, M., Štursová, M., et al., Active and total microbial communities in forest soil are largely different and highly stratified during decomposition, ISME J., 2012, vol. 6, no. 2, pp. 248–258. https://doi.org/10.1038/ismej.2011.95

    Article  CAS  PubMed  Google Scholar 

  91. Edgar, R.C., UNCROSS: Filtering of high-frequency cross-talk in 16S amplicon reads, bioRxiv, 2016, 088666. https://doi.org/10.1101/088666

  92. Davis, N.M., Proctor, D.M., Holmes, S.P., et al., Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data, Microbiome, 2018, vol. 6, Article no. 226. https://doi.org/10.1186/s40168-018-0605-2

    Article  PubMed  PubMed Central  Google Scholar 

  93. Lagkouvardos, I., Fischer, S., Kumar, N., and Clavel, T., Rhea: A transparent and modular R pipeline for microbial profiling based on 16s rRNA gene amplicons, PeerJ, 2017, vol. 5, e2836. https://doi.org/10.7717/peerj.2836

    Article  PubMed  PubMed Central  Google Scholar 

  94. Verhoeven, K.J.F., Simonsen, K.L., and McIntyre, L.M., Implementing false discovery rate control: Increasing your power, Oikos, 2005, vol. 108, pp. 643–647. https://doi.org/10.1111/j.0030-1299.2005.13727.x

    Article  Google Scholar 

  95. Blanchet, F.G., Cazelles, K., and Gravel, D., Co-occurrence is not evidence of ecological interactions, Ecol. Lett., 2020, vol. 23, no. 7, pp. 1050–1063. https://doi.org/10.1111/ele.13525

    Article  PubMed  Google Scholar 

  96. Berry, D. and Widder, S., Deciphering microbial interactions and detecting keystone species with co-occurrence networks, Front. Microbiol., 2014, vol. 5, Article no. 219. https://doi.org/10.3389/fmicb.2014.00219

    Article  PubMed  PubMed Central  Google Scholar 

  97. Connor, N., Barberan, A., and Clauset, A., Using null models to infer microbial co-occurrence networks, PLoS One, 2017, vol. 12, no. 5, e0176751. https://doi.org/10.1371/journal.pone.0176751

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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ACKNOWLEDGMENTS

The authors are grateful to M.V. Modorov, O.E. Likho-deevskaya, I.A. Shadrin, and E.A. Belsky for their valuable comments on the manuscript.

Funding

The collection of the material, network construction, bioinformatics analysis, and statistical data processing were supported by the Russian Foundation for Basic Research, project nos. 18-29-05042 and 19-04-00921; the manuscript was prepared for publication under state contract with the Institute of Plant and Animal Ecology, Ural Branch, Russian Academy of Sciences. Bioinformatic analysis was performed using the Uran supercomputer at the Institute of Mathematics and Mechanics, Ural Branch, Russian Academy of Sciences.

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Correspondence to V. S. Mikryukov.

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Translated by N. Gorgolyuk

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Mikryukov, V.S., Dulya, O.V., Likhodeevskii, G.A. et al. Analysis of Ecological Networks in Multicomponent Communities of Microorganisms: Possibilities, Limitations, and Potential Errors. Russ J Ecol 52, 188–200 (2021). https://doi.org/10.1134/S1067413621030085

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