Bulgarelli, D., Schlaeppi, K., Spaepen, S., Ver Loren van Themaat, E. & Schulze-Lefert, P. Structure and functions of the bacterial microbiota of plants. Annu. Rev. Plant Biol. 64, 807–838 (2013).
Google Scholar
Leach, J. E., Triplett, L. R., Argueso, C. T. & Trivedi, P. Communication in the phytobiome. Cell 169, 587–596 (2017).
Google Scholar
Vorholt, J. A., Vogel, C., Carlstrom, C. I. & Muller, D. B. Establishing causality: opportunities of synthetic communities for plant microbiome research. Cell Host Microbe 22, 142–155 (2017).
Google Scholar
Jiang, Y. et al. Plant cultivars imprint the rhizosphere bacterial community composition and association networks. Soil Biol. Biochem. 109, 145–155 (2017).
Google Scholar
Garbeva, P., van Elsas, J. D. & van Veen, J. A. Rhizosphere microbial community and its response to plant species and soil history. Plant Soil 302, 19–32 (2008).
Google Scholar
Li, Y. et al. Rhizobacterial communities of five co-occurring desert halophytes. PeerJ 6, e5508 (2018).
Google Scholar
Matthews, A., Pierce, S., Hipperson, H. & Raymond, B. Rhizobacterial community assembly patterns vary between crop species. Front. Microbiol. 10, 581 (2019).
Google Scholar
Perez-Jaramillo, J. E., Mendes, R. & Raaijmakers, J. M. Impact of plant domestication on rhizosphere microbiome assembly and functions. Plant Mol. Biol. 90, 635–644 (2016).
Google Scholar
Xu, J. et al. The structure and function of the global citrus rhizosphere microbiome. Nat. Commun. 9, 4894 (2018).
Google Scholar
Trivedi, P., Leach, J. E., Tringe, S. G., Sa, T. & Singh, B. K. Plant-microbiome interactions: from community assembly to plant health. Nat. Rev. Microbiol. 18, 607–621 (2020).
Google Scholar
Howard, M. M., Munoz, C. A., Kao-Kniffin, J. & Kessler, A. Soil microbiomes from fallow fields have species-specific effects on crop growth and pest resistance. Front. Plant Sci. 11, 1171 (2020).
Google Scholar
Yan, Y., Kuramae, E. E., de Hollander, M., Klinkhamer, P. G. & van Veen, J. A. Functional traits dominate the diversity-related selection of bacterial communities in the rhizosphere. ISME J. 11, 56–66 (2017).
Google Scholar
Bakker, P. A., Berendsen, R. L., Doornbos, R. F., Wintermans, P. C. & Pieterse, C. M. The rhizosphere revisited: root microbiomics. Front. Plant Sci. 4, 165 (2013).
Google Scholar
Lundberg, D. S. et al. Defining the core Arabidopsis thaliana root microbiome. Nature 488, 86–90 (2012).
Google Scholar
Busby, P. E. et al. Research priorities for harnessing plant microbiomes in sustainable agriculture. PLoS Biol. 15, e2001793 (2017).
Google Scholar
de Vries, F. T., Griffiths, R. I., Knight, C. G., Nicolitch, O. & Williams, A. Harnessing rhizosphere microbiomes for drought-resilient crop production. Science 368, 270–274 (2020).
Google Scholar
Hamonts, K. et al. Field study reveals core plant microbiota and relative importance of their drivers. Environ. Microbiol. 20, 124–140 (2018).
Google Scholar
Xu, Q. et al. Long-term chemical-only fertilization induces a diversity decline and deep selection on the soil bacteria. mSystems 5, e00337–20 (2020).
Google Scholar
Richter, A., Schöning, I., Kahl, T., Bauhus, J. & Ruess, L. Regional environmental conditions shape microbial community structure stronger than local forest management intensity. Ecol. Manag. 409, 250–259 (2018).
Bais, H. P., Weir, T. L., Perry, L. G., Gilroy, S. & Vivanco, J. M. The role of root exudates in rhizosphere interactions with plants and other organisms. Annu. Rev. Plant Biol. 57, 233–266 (2006).
Google Scholar
Wallenstein, M. D. Managing and manipulating the rhizosphere microbiome for plant health: a systems approach. Rhizosphere 3, 230–232 (2017).
Kuzyakov, Y. & Xu, X. Competition between roots and microorganisms for nitrogen: mechanisms and ecological relevance. N. Phytol. 198, 656–669 (2013).
Google Scholar
Roller, B. R., Stoddard, S. F. & Schmidt, T. M. Exploiting rRNA operon copy number to investigate bacterial reproductive strategies. Nat. Microbiol. 1, 16160 (2016).
Google Scholar
Wu, L. et al. Microbial functional trait of rRNA operon copy numbers increases with organic levels in anaerobic digesters. ISME J. 11, 2874–2878 (2017).
Google Scholar
Nuccio, E. E. et al. Niche differentiation is spatially and temporally regulated in the rhizosphere. ISME J. 14, 999–1014 (2020).
Google Scholar
Fan, K., Weisenhorn, P., Gilbert, J. A. & Chu, H. Wheat rhizosphere harbors a less complex and more stable microbial co-occurrence pattern than bulk soil. Soil Biol. Biochem. 125, 251–260 (2018).
Google Scholar
Fan, K. et al. Rhizosphere-associated bacterial network structure and spatial distribution differ significantly from bulk soil in wheat crop fields. Soil Biol. Biochem. 113, 275–284 (2017).
Google Scholar
Peiffer, J. A. et al. Diversity and heritability of the maize rhizosphere microbiome under field conditions. Proc. Natl Acad. Sci. USA 110, 6548–6553 (2013).
Google Scholar
Baudoin, E., Benizri, E. & Guckert, A. Impact of artificial root exudates on the bacterial community structure in bulk soil and maize rhizosphere. Soil Biol. Biochem. 35, 1183–1192 (2003).
Google Scholar
Kuzyakov, Y. & Razavi, B. S. Rhizosphere size and shape: temporal dynamics and spatial stationarity. Soil Biol. Biochem. 135, 343–360 (2019).
Google Scholar
Ren, Y. et al. Functional compensation dominates the assembly of plant rhizospheric bacterial community. Soil Biol. Biochem. 150, 107968 (2020).
Google Scholar
Chen, Y. et al. Organic amendments shift the phosphorus-correlated microbial co-occurrence pattern in the peanut rhizosphere network during long-term fertilization regimes. Appl. Soil Ecol. 124, 229–239 (2018).
Google Scholar
Atulba, S. L. et al. Evaluation of rice root oxidizing potential using digital image analysis. J. Korean Soc. Appl. Bi 58, 463–471 (2015).
Google Scholar
Schmidt, H., Eickhorst, T. & Tippkötter, R. Monitoring of root growth and redox conditions in paddy soil rhizotrons by redox electrodes and image analysis. Plant Soil 341, 221–232 (2011).
Google Scholar
Pausch, J., Zhu, B., Kuzyakov, Y. & Cheng, W. Plant inter-species effects on rhizosphere priming of soil organic matter decomposition. Soil Biol. Biochem. 57, 91–99 (2013).
Google Scholar
Finn, D., Kopittke, P. M., Dennis, P. G. & Dalal, R. C. Microbial energy and matter transformation in agricultural soils. Soil Biol. Biochem. 111, 176–192 (2017).
Google Scholar
Jones, R. T. et al. A comprehensive survey of soil acidobacterial diversity using pyrosequencing and clone library analyses. ISME J. 3, 442–453 (2009).
Google Scholar
Zhao, S. et al. Biogeographical distribution of bacterial communities in saline agricultural soil. Geoderma 361, 114095 (2020).
Google Scholar
Eiler, A., Heinrich, F. & Bertilsson, S. Coherent dynamics and association networks among lake bacterioplankton taxa. ISME J. 6, 330–342 (2012).
Google Scholar
Zhou, J. et al. Generation of arbitrary two-point correlated directed networks with given modularity. Phys. Lett. A 374, 3129–3135 (2010).
Google Scholar
Herron, P. M., Gage, D. J., Arango Pinedo, C., Haider, Z. K. & Cardon, Z. G. Better to light a candle than curse the darkness: illuminating spatial localization and temporal dynamics of rapid microbial growth in the rhizosphere. Front. Plant Sci. 4, 323 (2013).
Google Scholar
Blagodatskaya, E., Blagodatsky, S., Anderson, T. H. & Kuzyakov, Y. Microbial growth and carbon use efficiency in the rhizosphere and root-free soil. PLoS ONE 9, e93282 (2014).
Google Scholar
Berendsen, R. L., Pieterse, C. M. & Bakker, P. A. The rhizosphere microbiome and plant health. Trends Plant Sci. 17, 478–486 (2012).
Google Scholar
Mendes, L. W., Kuramae, E. E., Navarrete, A. A., van Veen, J. A. & Tsai, S. M. Taxonomical and functional microbial community selection in soybean rhizosphere. ISME J. 8, 1577–1587 (2014).
Google Scholar
Hinsinger, P. Bioavailability of soil inorganic P in the rhizosphere as affected by root-induced chemical changes: a review. Plant Soil 237, 173–195 (2001).
Google Scholar
Kuzyakov, Y. & Blagodatskaya, E. Microbial hotspots and hot moments in soil: Concept & review. Soil Biol. Biochem. 83, 184–199 (2015).
Google Scholar
Loeppmann, S., Blagodatskaya, E., Pausch, J. & Kuzyakov, Y. Substrate quality affects kinetics and catalytic efficiency of exo-enzymes in rhizosphere and detritusphere. Soil Biol. Biochem. 92, 111–118 (2016).
Google Scholar
Ma, X. et al. Spatial patterns of enzyme activities in the rhizosphere: Effects of root hairs and root radius. Soil Biol. Biochem. 118, 69–78 (2018).
Google Scholar
Kroener, E., Zarebanadkouki, M., Kaestner, A. & Carminati, A. Nonequilibrium water dynamics in the rhizosphere: How mucilage affects water flow in soils. Water Resour. Res. 50, 6479–6495 (2014).
Google Scholar
Carminati, A. Rhizosphere wettability decreases with root age: a problem or a strategy to increase water uptake of young roots? Front. Plant Sci. 4, 298 (2013).
Google Scholar
Holz, M., Zarebanadkouki, M., Kaestner, A., Kuzyakov, Y. & Carminati, A. Rhizodeposition under drought is controlled by root growth rate and rhizosphere water content. Plant Soil 423, 429–442 (2018).
Google Scholar
Tripathi, B. M. et al. Trends in taxonomic and functional composition of soil microbiome along a precipitation gradient in Israel. Microb. Ecol. 74, 168–176 (2017).
Google Scholar
Harms, A., Brodersen, D. E., Mitarai, N. & Gerdes, K. Toxins, targets, and triggers: an overview of toxin-antitoxin biology. Mol. Cell 70, 768–784 (2018).
Google Scholar
Kearns, P. J. & Shade, A. Trait-based patterns of microbial dynamics in dormancy potential and heterotrophic strategy: case studies of resource-based and post-press succession. ISME J. 12, 2575–2581 (2018).
Google Scholar
Klappenbach, J. A., Dunbar, J. M. & Schmidt, T. M. rRNA operon copy number reflects ecological strategies of bacteria. Appl. Environ. Microb. 66, 1328–1333 (2000).
Google Scholar
Schoeps, R. et al. Land-use intensity rather than plant functional identity shapes bacterial and fungal rhizosphere communities. Front. Micro. 9, 2711 (2018).
Nemergut, D. R. et al. Decreases in average bacterial community rRNA operon copy number during succession. ISME J. 10, 1147–1156 (2016).
Google Scholar
Cui, J. et al. Carbon and nitrogen recycling from microbial necromass to cope with C:N stoichiometric imbalance by priming. Soil Biol. Biochem. 142, 107720 (2020).
Google Scholar
Blagodatskaya, E. V., Blagodatsky, S. A., Anderson, T. H. & Kuzyakov, Y. Priming effects in chernozem induced by glucose and N in relation to microbial growth strategies. Appl. Soil Ecol. 37, 95–105 (2007).
Lecomte, S. M. et al. Diversifying anaerobic respiration strategies to compete in the rhizosphere. Front. Environ. Sci. 6, 139 (2018).
Herz, K. et al. Drivers of intraspecific trait variation of grass and forb species in German meadows and pastures. J. Veg. Sci. 28, 705–716 (2017).
Ravenek, J. M. et al. Linking root traits and competitive success in grassland species. Plant Soil 407, 39–53 (2016).
Google Scholar
Larsen, J., Jaramillo-López, P., Nájera-Rincon, M. & González-Esquivel, C. Biotic interactions in the rhizosphere in relation to plant and soil nutrient dynamics. J. Soil Sci. Plant Nutr. 15, 449–463 (2015).
Raaijmakers, J. M., Paulitz, T. C., Steinberg, C., Alabouvette, C. & Moënne-Loccoz, Y. The rhizosphere: a playground and battlefield for soilborne pathogens and beneficial microorganisms. Plant Soil 321, 341–361 (2009).
Google Scholar
Ma, H.-K. et al. Steering root microbiomes of a commercial horticultural crop with plant-soil feedbacks. Appl. Soil Ecol. 150, 103468 (2020).
Hannula, S. E. et al. Persistence of plant-mediated microbial soil legacy effects in soil and inside roots. Nat. Commun 12, 5686 (2021).
Google Scholar
Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).
Google Scholar
Hill, T. C., Walsh, K. A., Harris, J. A. & Moffett, B. F. Using ecological diversity measures with bacterial communities. FEMS Microbiol. Ecol. 43, 1–11 (2003).
Google Scholar
Lima-Mendez, G. et al. Determinants of community structure in the global plankton interactome. Science 348, 6237 (2015).
Noble, W. S. How does multiple testing correction work? Nat. Biotechnol. 27, 1135–1137 (2009).
Google Scholar
Luo, F., Zhong, J., Yang, Y., Scheuermann, R. H. & Zhou, J. Application of random matrix theory to biological networks. Phys. Lett. A 357, 420–423 (2006).
Google Scholar
Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).
Google Scholar
Bastian, M., Heymann, S. & Jacomy, M. Gephi: an open source software for exploring and manipulating networks. ICWSM 8, 361–362 (2009).
Peng, G. S. & Wu, J. Optimal network topology for structural robustness based on natural connectivity. Phys. A 443, 212–220 (2016).
Google Scholar
Ruan, Y., Wang, T., Guo, S., Ling, N. & Shen, Q. Plant grafting shapes complexity and co-occurrence of rhizobacterial assemblages. Microb. Ecol. 80, 643–655 (2020).
Google Scholar
Newman, M. E. Modularity and community structure in networks. Proc. Natl Acad. Sci. USA 103, 8577–8582 (2006).
Google Scholar
Deng, Y. et al. Molecular ecological network analyses. BMC Bioinforma. 13, 113 (2012).
Guimerà, R. & Nunes Amaral, L. A. Functional cartography of complex metabolic networks. Nature 433, 895–900 (2005).
Google Scholar
Olesen, J. M., Bascompte, J., Dupont, Y. L. & Jordano, P. The modularity of pollination networks. Proc. Natl Acad. Sci. USA 104, 19891 (2007).
Google Scholar
Ling, N. et al. Insight into how organic amendments can shape the soil microbiome in long-term field experiments as revealed by network analysis. Soil Biol. Biochem. 99, 137–149 (2016).
Google Scholar
Louca, S., Parfrey Laura, W. & Doebeli, M. Decoupling function and taxonomy in the global ocean microbiome. Science 353, 1272–1277 (2016).
Google Scholar
Douglas, G. M. et al. PICRUSt2 for prediction of metagenome functions. Nat. Biotechnol. 38, 685–688 (2020).
Google Scholar
Lennon, J. T. & Jones, S. E. Microbial seed banks: the ecological and evolutionary implications of dormancy. Nat. Rev. Microbiol. 9, 119–130 (2011).
Google Scholar
Hedges, L. V., Gurevitch, J. & Curtis, P. S. The meta-analysis of response ratios in experimental ecology. Ecology 80, 1150–1156 (1999).
Rosenberg, M. S., Adams, D. C. & Gurevitch, J. MetaWin: Statistical software for meta-analysis. Version 2.0. Sinauer (2000).
Viechtbauer, W. Conducting meta-analyses in R with the metafor Package. J. Stat. Softw. 36, 1–48 (2010).
Egger, M., Smith, G. D., Schneider, M. & Minder, C. Bias in meta-analysis detected by a simple, graphical test. BMJ 315, 629 (1997).
Google Scholar
Calcagno, V. & de Mazancourt, C. glmulti: an R package for easy automated model selection with (generalized) linear models. J. Stat. Softw. 34, 1–29 (2010).
Gloor, G. B., Macklaim, J. M., Pawlowsky-Glahn, V. & Egozcue, J. J. Microbiome datasets are compositional: and this is not optional. Front. Microbiol. 8, 2224 (2017).
Google Scholar
Edgar, R. C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797 (2004).
Google Scholar
Letunic, I. & Bork, P. Interactive tree of life (iTOL) v3: an online tool for the display and annotation of phylogenetic and other trees. Nucleic Acids Res. 44, W242–W245 (2016).
Google Scholar
Source: Ecology - nature.com