Best practices in microbial biogeography: The 6W principle

Pengfa Li , Alex J. Dumbrell , Jiandong Jiang

Soil Ecology Letters ›› 2025, Vol. 7 ›› Issue (3) : 250310

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Soil Ecology Letters ›› 2025, Vol. 7 ›› Issue (3) : 250310 DOI: 10.1007/s42832-025-0310-6
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Best practices in microbial biogeography: The 6W principle

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Abstract

A comprehensive understanding of microbial biogeography is essential to elucidate the mechanisms that regulate microbial diversity and facilitate ecosystem functioning. Here, we present a standardised approach for microbial biogeography research, using the ‘6W principles’ of ‘Who’, ‘What’, ‘Where’, ‘When’, ‘Why’, and ‘How’, to provide a paradigmatic framework for its study. The ‘6W principle’ we developed aimed to address the six fundamental questions in microbial biogeographical researches, including the taxonomic and functional identity, abundance and diversity, distribution patterns, movement or evolutionary trajectory, driving factors, and future changes of microbial communities. Some key corresponding actions were suggested to promote the microbial biogeographical research such as constructing high-resolution taxonomic and functional annotation databases, developing absolute-quantitative high-throughput sequencing, increasing sampling coverage, establishing multidimensional time-series monitoring, developing unified theoretical frameworks and advanced biogeographical modelling approaches, and establishing long-term global networking experiments. We call on the community to jointly enrich the connotation and coverage of the 6W principle, in order to promote the further development and exploitation of microbial biogeography in the context of ongoing global change.

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Keywords

microbial biogeography / 6W principle / soil ecology

Highlight

● The ‘6W principle’ provides a paradigmatic framework for microbial biogeography.

● Six key actions such as developing unified theoretical framework were suggested.

● The 6W principle should be further refined by the whole community.

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Pengfa Li, Alex J. Dumbrell, Jiandong Jiang. Best practices in microbial biogeography: The 6W principle. Soil Ecology Letters, 2025, 7(3): 250310 DOI:10.1007/s42832-025-0310-6

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References

[1]

Abarenkov, K., Nilsson, R.H., Larsson, K.H., Taylor, A.F.S., May, T.W., Frøslev, T.G., Pawlowska, J., Lindahl, B., Põldmaa, K., Truong, C., Vu, D., Hosoya, T., Niskanen, T., Piirmann, T., Ivanov, F., Zirk, A., Peterson, M., Cheeke, T.E., Ishigami, Y., Jansson, A.T., Jeppesen, T.S., Kristiansson, E., Mikryukov, V., Miller, J.T., Oono, R., Ossandon, F.J., Paupério, J., Saar, I., Schigel, D., Suija, A., Tedersoo, L., Kõljalg, U., 2024. The UNITE database for molecular identification and taxonomic communication of fungi and other eukaryotes: sequences, taxa and classifications reconsidered. Nucleic Acids Research52, D791–D797.

[2]

Aslani, F., Geisen, S., Ning, D.L., Tedersoo, L., Bahram, M., 2022. Towards revealing the global diversity and community assembly of soil eukaryotes. Ecology Letters25, 65–76.

[3]

Averill, C., Anthony, M.A., Baldrian, P., Finkbeiner, F., van den Hoogen, J., Kiers, T., Kohout, P., Hirt, E., Smith, G.R., Crowther, T.W., 2022. Defending Earth's terrestrial microbiome. Nature Microbiology7, 1717–1725.

[4]

Bahram, M., Hildebrand, F., Forslund, S.K., Anderson, J.L., Soudzilovskaia, N.A., Bodegom, P.M., Bengtsson-Palme, J., Anslan, S., Coelho, L.P., Harend, H., Huerta-Cepas, J., Medema, M.H., Maltz, M.R., Mundra, S., Olsson, P.A., Pent, M., Põlme, S., Sunagawa, S., Ryberg, M., Tedersoo, L., Bork, P., 2018. Structure and function of the global topsoil microbiome. Nature560, 233–237.

[5]

Bolyen, E., Rideout, J.R., Dillon, M.R., Bokulich, N.A., Abnet, C.C., Al-Ghalith, G.A., Alexander, H., Alm, E.J., Arumugam, M., Asnicar, F., Bai, Y., Bisanz, J.E., Bittinger, K., Brejnrod, A., Brislawn, C.J., Brown, C.T., Callahan, B.J., Caraballo-Rodríguez, A.M., Chase, J., Cope, E.K., Da Silva, R., Diener, C., Dorrestein, P.C., Douglas, G.M., Durall, D.M., Duvallet, C., Edwardson, C.F., Ernst, M., Estaki, M., Fouquier, J., Gauglitz, J.M., Gibbons, S.M., Gibson, D.L., Gonzalez, A., Gorlick, K., Guo, J.R., Hillmann, B., Holmes, S., Holste, H., Huttenhower, C., Huttley, G.A., Janssen, S., Jarmusch, A.K., Jiang, L.J., Kaehler, B.D., Bin Kang, K., Keefe, C.R., Keim, P., Kelley, S.T., Knights, D., Koester, I., Kosciolek, T., Kreps, J., Langille, M.G.I., Lee, J., Ley, R., Liu, Y.X., Loftfield, E., Lozupone, C., Maher, M., Marotz, C., Martin, B.D., McDonald, D., McIver, L.J., Melnik, A.V., Metcalf, J.L., Morgan, S.C., Morton, J.T., Naimey, A.T., Navas-Molina, J.A., Nothias, L.F., Orchanian, S.B., Pearson, T., Peoples, S.L., Petras, D., Preuss, M.L., Pruesse, E., Rasmussen, L.B., Rivers, A., Robeson II, M.S., Rosenthal, P., Segata, N., Shaffer, M., Shiffer, A., Sinha, R., Song, S.J., Spear, J.R., Swafford, A.D., Thompson, L.R., Torres, P.J., Trinh, P., Tripathi, A., Turnbaugh, P.J., Ul-Hasan, S., vander Hooft, J.J.J., Vargas, F., Vázquez-Baeza, Y., Vogtmann, E., von Hippel, M., Walters, W., Wan, Y.H., Wang, M.X., Warren, J., Weber, K.C., Williamson, C.H.D., Willis, A.D., Xu, Z.Z., Zaneveld, J.R., Zhang, Y.L., Zhu, Q.Y., Knight, R., Caporaso, J.G., 2019. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nature Biotechnology37, 852–857.

[6]

Chu, H.Y., Gao, G.F., Ma, Y.Y., Fan, K.K., Delgado-Baquerizo, M., 2020. Soil microbial biogeography in a changing world: Recent advances and future perspectives. mSystems5, e00803–19.

[7]

Dini-Andreote, F., Stegen, J.C., van Elsas, J.D., Salles, J.F., 2015. Disentangling mechanisms that mediate the balance between stochastic and deterministic processes in microbial succession. Proceedings of the National Academy of Sciences of the United States of America112, E1326–E1332.

[8]

Drummond, A.J., Rambaut, A., 2007. BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evolutionary Biology7, 214.

[9]

Fu, L.M., Niu, B.F., Zhu, Z.W., Wu, S.T., Li, W.Z., 2012. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics28, 3150–3152.

[10]

Guillou, L., Bachar, D., Audic, S., Bass, D., Berney, C., Bittner, L., Boutte, C., Burgaud, G., de Vargas, C., Decelle, J., del Campo, J., Dolan, J.R., Dunthorn, M., Edvardsen, B., Holzmann, M., Kooistra, W.H.C.F., Lara, E., Le Bescot, N., Logares, R., Mahé, F., Massana, R., Montresor, M., Morard, R., Not, F., Pawlowski, J., Probert, I., Sauvadet, A.L., Siano, R., Stoeck, T., Vaulot, D., Zimmermann, P., Christen, R., 2013. The Protist Ribosomal Reference database (PR2): a catalog of unicellular eukaryote Small Sub-Unit rRNA sequences with curated taxonomy. Nucleic Acids Research41, D597–D604.

[11]

Guisan, A., Zimmermann, N.E., 2000. Predictive habitat distribution models in ecology. Ecological Modelling135, 147–186.

[12]

Ho, S.Y.W., Duchêne, S., 2014. Molecular-clock methods for estimating evolutionary rates and timescales. Molecular Ecology23, 5947–5965.

[13]

Hubbell, S.P., Borda-de-Água, L., 2004. The unified neutral theory of biodiversity and biogeography: reply. Ecology85, 3175–3178.

[14]

Jeffrey, S.J., Carter, J.O., Moodie, K.B., Beswick, A.R., 2001. Using spatial interpolation to construct a comprehensive archive of Australian climate data. Environmental Modelling & Software16, 309–330.

[15]

Jiao, S., Xu, Y.Q., Zhang, J., Lu, Y.H., 2019. Environmental filtering drives distinct continental atlases of soil archaea between dryland and wetland agricultural ecosystems. Microbiome7, 15.

[16]

Kanehisa, M., Goto, S., 2000. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Research28, 27–30.

[17]

Langille, M.G.I., Zaneveld, J., Caporaso, J.G., McDonald, D., Knights, D., Reyes, J.A., Clemente, J.C., Burkepile, D.E., Thurber, R.L.V., Knight, R., Beiko, R.G., Huttenhower, C., 2013. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nature Biotechnology31, 814–821.

[18]

Legendre, P., Gallagher, E.D., 2001. Ecologically meaningful transformations for ordination of species data. Oecologia129, 271–280.

[19]

Li, P.F., Tedersoo, L., Crowther, T.W., Dumbrell, A.J., Dini-Andreote, F., Bahram, M., Kuang, L., Li, T., Wu, M., Jiang, Y.J., Luan, L., Saleem, M., de Vries, F.T., Li, Z.P., Wang, B.Z., Jiang, J.D., 2023b. Fossil-fuel-dependent scenarios could lead to a significant decline of global plant-beneficial bacteria abundance in soils by 2100. Nature Food4, 996–1006.

[20]

Li, P.F., Tedersoo, L., Crowther, T.W., Wang, B.Z., Shi, Y., Kuang, L., Li, T., Wu, M., Liu, M., Luan, L., Liu, J., Li, D.Z., Li, Y.X., Wang, S.H., Saleem, M., Dumbrell, A.J., Li, Z.P., Jiang, J.D., 2023a. Global diversity and biogeography of potential phytopathogenic fungi in a changing world. Nature Communications14, 6482.

[21]

Luan, L., Jiang, Y.J., Cheng, M.H., Dini-Andreote, F., Sui, Y.Y., Xu, Q.S., Geisen, S., Sun, B., 2020. Organism body size structures the soil microbial and nematode community assembly at a continental and global scale. Nature Communications11, 6406.

[22]

Martiny, J.B.H., Bohannan, B.J.M., Brown, J.H., Colwell, R.K., Fuhrman, J.A., Green, J.L., Horner-Devine, M.C., Kane, M., Krumins, J.A., Kuske, C.R., Morin, P.J., Naeem, S., Øvreås, L., Reysenbach, A.L., Smith, V.H., Staley, J.T., 2006. Microbial biogeography: putting microorganisms on the map. Nature Reviews Microbiology4, 102–112.

[23]

Nguyen, N.H., Song, Z.W., Bates, S.T., Branco, S., Tedersoo, L., Menke, J., Schilling, J.S., Kennedy, P.G., 2016. FUNGuild: an open annotation tool for parsing fungal community datasets by ecological guild. Fungal Ecology20, 241–248.

[24]

Phillips, S.J., Anderson, R.P., Schapire, R.E., 2006. Maximum entropy modeling of species geographic distributions. Ecological Modelling190, 231–259.

[25]

Prasad, A.M., Iverson, L.R., Liaw, A., 2006. Newer classification and regression tree techniques: bagging and random forests for ecological prediction. Ecosystems9, 181–199.

[26]

Quast, C., Pruesse, E., Yilmaz, P., Gerken, J., Schweer, T., Yarza, P., Peplies, J., Glöckner, F.O., 2013. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Research41, D590–D596.

[27]

Sanderson, M.J., 2003. r8s: inferring absolute rates of molecular evolution and divergence times in the absence of a molecular clock. Bioinformatics19, 301–302.

[28]

Shi, Y., Li, Y.T., Xiang, X.J., Sun, R.B., Yang, T., He, D., Zhang, K.P., Ni, Y.Y., Zhu, Y.G., Adams, J.M., Chu, H.Y., 2018. Spatial scale affects the relative role of stochasticity versus determinism in soil bacterial communities in wheat fields across the North China Plain. Microbiome6, 27.

[29]

Stadler, M., del Giorgio, P.A., 2022. Terrestrial connectivity, upstream aquatic history and seasonality shape bacterial community assembly within a large boreal aquatic network. The ISME Journal16, 937–947.

[30]

Stadler, T., 2011. Simulating trees with a fixed number of extant species. Systematic Biology60, 676–684.

[31]

Stehr, N., 2014. Climate policy: a societal sea change. Nature513, 312.

[32]

Steinegger, M., Söding, J., 2017. MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nature Biotechnology35, 1026–1028.

[33]

Vellend, M., Agrawal, A., 2010. Conceptual synthesis in community ecology. The Quarterly Review of Biology85, 183–206.

[34]

Volkov, I., Banavar, J.R., Hubbell, S.P., Maritan, A., 2003. Neutral theory and relative species abundance in ecology. Nature424, 1035–1037.

[35]

Wang, J.J., Pan, F.Y., Soininen, J., Heino, J., Shen, J., 2016. Nutrient enrichment modifies temperature-biodiversity relationships in large-scale field experiments. Nature Communications7, 13960.

[36]

Wang, J.J., Shen, J., Wu, Y.C., Tu, C., Soininen, J., Stegen, J.C., He, J.Z., Liu, X.Q., Zhang, L., Zhang, E.L., 2013. Phylogenetic beta diversity in bacterial assemblages across ecosystems: deterministic versus stochastic processes. The ISME Journal7, 1310–1321.

[37]

Wu, L.W., Ning, D.L., Zhang, B., Li, Y., Zhang, P., Shan, X.Y., Zhang, Q.T., Brown, M.R., Li, Z.X., Van Nostrand, J.D., Ling, F.Q., Xiao, N.J., Zhang, Y., Vierheilig, J., Wells, G.F., Yang, Y.F., Deng, Y., Tu, Q.C., Wang, A.J., Zhang, T., He, Z.L., Keller, J., Nielsen, P.H., Alvarez, P.J.J., Criddle, C.S., Wagner, M., Tiedje, J.M., He, Q., Curtis, T.P., Stahl, D.A., Alvarez-Cohen, L., Rittmann, B.E., Wen, X.H., Zhou, J.Z., 2019. Global diversity and biogeography of bacterial communities in wastewater treatment plants. Nature Microbiology4, 1183–1195.

[38]

Zhou, J.Z., Ning, D.L., 2017. Stochastic community assembly: does it matter in microbial ecology. Microbiology and Molecular Biology Reviews81, e00002-17.

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