Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Core microbiomes for sustainable agroecosystems

A Publisher Correction to this article was published on 14 August 2018

This article has been updated

Abstract

In an era of ecosystem degradation and climate change, maximizing microbial functions in agroecosystems has become a prerequisite for the future of global agriculture. However, managing species-rich communities of plant-associated microbiomes remains a major challenge. Here, we propose interdisciplinary research strategies to optimize microbiome functions in agroecosystems. Informatics now allows us to identify members and characteristics of ‘core microbiomes’, which may be deployed to organize otherwise uncontrollable dynamics of resident microbiomes. Integration of microfluidics, robotics and machine learning provides novel ways to capitalize on core microbiomes for increasing resource-efficiency and stress-resistance of agroecosystems.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Entangled webs of below-ground interactions.
Fig. 2: Managing native biomes using core microorganisms.
Fig. 3: Microbial network information for controlling microbiomes in agroecosystems.
Fig. 4: Preparing and deploying core microbiomes.
Fig. 5: Agroecosystem management.

Change history

  • 14 August 2018

    Owing to a technical error, this Perspective was originally published without its received and accepted dates; the dates “Received: 31 December 2017; Accepted: 23 March 2018” have now been included in all versions.

References

  1. 1.

    Berendsen, R. L., Pieterse, C. M. & Bakker, P. A. The rhizosphere microbiome and plant health. Trends Plant Sci. 17, 478–486 (2012).

    PubMed  Article  CAS  Google Scholar 

  2. 2.

    de Vries, F. & Wallenstein, M. Below-ground connections underlying above-ground food production: a framework for optimising ecological connections in the rhizosphere. J. Ecol. 105, 913–920 (2017).

    Article  Google Scholar 

  3. 3.

    Busby, P. E. et al. Research priorities for harnessing plant microbiomes in sustainable agriculture. PLOS Biol. 15, e2001793 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  4. 4.

    Elser, J. & Bennett, E. Phosphorus cycle: a broken biogeochemical cycle. Nature 478, 29–31 (2011).

    PubMed  Article  CAS  Google Scholar 

  5. 5.

    Callaway, E. Devastating wheat fungus appears in Asia for first time. Nature 532, 421–422 (2016).

    PubMed  Article  PubMed Central  Google Scholar 

  6. 6.

    Howden, S. M. et al. Adapting agriculture to climate change. Proc. Natl Acad. Sci. USA 104, 19691–19696 (2007).

    PubMed  Article  Google Scholar 

  7. 7.

    Dangl, J. L., Horvath, D. M. & Staskawicz, B. J. Pivoting the plant immune system from dissection to deployment. Science 341, 746–751 (2013).

    PubMed  Article  CAS  Google Scholar 

  8. 8.

    Robertson, G. P. & Vitousek, P. M. Nitrogen in agriculture: balancing the cost of an essential resource. Ann. Rev. Env. Res. 34, 97–125 (2009).

    Article  Google Scholar 

  9. 9.

    Tilman, D., Balzer, C., Hill, J. & Befort, B. L. Global food demand and the sustainable intensification of agriculture. Proc. Natl Acad. Sci. USA 108, 20260–20264 (2011).

    PubMed  Article  Google Scholar 

  10. 10.

    van der Heijden, M. G. et al. Mycorrhizal fungal diversity determines plant biodiversity, ecosystem variability and productivity. Nature 396, 69–72 (1998).

    Article  CAS  Google Scholar 

  11. 11.

    Bonfante, P. & Anca, I.-A. Plants, mycorrhizal fungi, and bacteria: a network of interactions. Ann. Rev. Microbiol. 63, 363–383 (2009).

    Article  CAS  Google Scholar 

  12. 12.

    Hiruma, K. et al. Root endophyte Colletotrichum tofieldiae confers plant fitness benefits that are phosphate status dependent. Cell 165, 464–474 (2016).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  13. 13.

    Mendes, R. et al. Deciphering the rhizosphere microbiome for disease-suppressive bacteria. Science 332, 1097–1100 (2011).

    PubMed  Article  CAS  Google Scholar 

  14. 14.

    Zavala-Gonzalez, E. A. et al. Arabidopsis thaliana root colonization by the nematophagous fungus Pochonia chlamydosporia is modulated by jasmonate signaling and leads to accelerated flowering and improved yield. New Phytol. 213, 351–364 (2017).

    PubMed  Article  CAS  Google Scholar 

  15. 15.

    Calvo, P., Nelson, L. & Kloepper, J. W. Agricultural uses of plant biostimulants. Plant Soil 383, 3–41 (2014).

    Article  CAS  Google Scholar 

  16. 16.

    Arnold, A. E. et al. Fungal endophytes limit pathogen damage in a tropical tree. Proc. Natl Acad. Sci. USA 100, 15649–15654 (2003).

    PubMed  Article  CAS  Google Scholar 

  17. 17.

    Vorholt, J. A., Vogel, C., Carlström, C. I. & Müller, D. B. Establishing causality: opportunities of synthetic communities for plant microbiome research. Cell Host Microbe 22, 142–155 (2017).

    PubMed  Article  CAS  Google Scholar 

  18. 18.

    Robertson, G. P. et al. Cellulosic biofuel contributions to a sustainable energy future: choices and outcomes. Science 356, eaal2324 (2017).

    PubMed  Article  CAS  Google Scholar 

  19. 19.

    Castrillo, G. et al. Root microbiota drive direct integration of phosphate stress and immunity. Nature 543, 513–518 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  20. 20.

    Jacott, C. N., Murray, J. D. & Ridout, C. J. Trade-offs in arbuscular mycorrhizal symbiosis: disease resistance, growth responses and perspectives for crop breeding. Agronomy 7, 75 (2017).

    Article  Google Scholar 

  21. 21.

    Oldroyd, G. E. Speak, friend, and enter: signalling systems that promote beneficial symbiotic associations in plants. Nat. Rev. Microbiol. 11, 252–263 (2013).

    PubMed  Article  CAS  Google Scholar 

  22. 22.

    King, A. The future of agriculture. Nature 544, 21–23 (2017).

    Article  Google Scholar 

  23. 23.

    Schlaeppi, K. & Bulgarelli, D. The plant microbiome at work. Mol. Plant-Microbe Int. 28, 212–217 (2015).

    Article  CAS  Google Scholar 

  24. 24.

    Girlanda, M. et al. Impact of biocontrol Pseudomonas fluorescens CHA0 and a genetically modified derivative on the diversity of culturable fungi in the cucumber rhizosphere. Appl. Env. Micriobiol. 67, 1851–1864 (2001).

    Article  CAS  Google Scholar 

  25. 25.

    Streeter, J. G. Failure of inoculant rhizobia to overcome the dominance of indigenous strains for nodule formation. Can. J. Microbiol. 40, 513–522 (1994).

    Article  Google Scholar 

  26. 26.

    Castro-Sowinski, S., Herschkovitz, Y., Okon, Y. & Jurkevitch, E. Effects of inoculation with plant growth-promoting rhizobacteria on resident rhizosphere microorganisms. FEMS Microbiol. Lett. 276, 1–11 (2007).

    PubMed  Article  CAS  Google Scholar 

  27. 27.

    Walsh, U. et al. Residual impact of the biocontrol inoculant Pseudomonas fluorescens F113 on the resident population of rhizobia nodulating a red clover rotation crop. Microb. Ecol. 45, 145–155 (2003).

    PubMed  Article  CAS  Google Scholar 

  28. 28.

    Lloyd-Price, J. et al. Strains, functions and dynamics in the expanded Human Microbiome Project. Nature 550, 61–66 (2017).

    PubMed  PubMed Central  CAS  Google Scholar 

  29. 29.

    Bashan, A. et al. Universality of human microbial dynamics. Nature 534, 259–262 (2016).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  30. 30.

    Lozupone, C. A., Stombaugh, J. I., Gordon, J. I., Jansson, J. K. & Knight, R. Diversity, stability and resilience of the human gut microbiota. Nature 489, 220–230 (2012).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  31. 31.

    Paramsothy, S. et al. Multidonor intensive faecal microbiota transplantation for active ulcerative colitis: a randomised placebo-controlled trial. Lancet 389, 1218–1228 (2017).

    PubMed  Article  Google Scholar 

  32. 32.

    Müller, D. B., Vogel, C., Bai, Y. & Vorholt, J. A. The plant microbiota: systems-level insights and perspectives. Ann., Rev. Genet. 50, 211–234 (2016).

    Article  CAS  Google Scholar 

  33. 33.

    Toju, H., Yamamoto, S., Tanabe, A. S., Hayakawa, T. & Ishii, H. S. Network modules and hubs in plant-root fungal biomes. J. R. Soc. Interface 13, 20151097 (2016).

    PubMed  PubMed Central  Article  Google Scholar 

  34. 34.

    Bulgarelli, D. et al. Structure and function of the bacterial root microbiota in wild and domesticated barley. Cell Host Microbe 17, 392–403 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  35. 35.

    Marasco, R., Rolli, E., Fusi, M., Michoud, G. & Daffonchio, D. Grapevine rootstocks shape underground bacterial microbiome and networking but not potential functionality. Microbiome 6, 3 (2018).

    PubMed  PubMed Central  Article  Google Scholar 

  36. 36.

    Edwards, J. et al. Structure, variation, and assembly of the root-associated microbiomes of rice. Proc. Natl Acad. Sci. USA 112, 911–920 (2015).

    Article  CAS  Google Scholar 

  37. 37.

    Hartman, K. et al. Cropping practices manipulate abundance patterns of root and soil microbiome members paving the way to smart farming. Microbiome 6, 14 (2018).

    PubMed  PubMed Central  Article  Google Scholar 

  38. 38.

    Arumugam, M. et al. Enterotypes of the human gut microbiome. Nature 473, 174–180 (2011).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  39. 39.

    Knights, D. et al. Rethinking “enterotypes”. Cell Host Microbe 16, 433–437 (2014).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  40. 40.

    Scheffer, M., Carpenter, S., Foley, J. A., Folke, C. & Walker, B. Catastrophic shifts in ecosystems. Nature 413, 591–596 (2001).

    PubMed  Article  CAS  Google Scholar 

  41. 41.

    Beisner, B. E., Haydon, D. T. & Cuddington, K. Alternative stable states in ecology. Front. Ecol. Env. 1, 376–382 (2003).

    Article  Google Scholar 

  42. 42.

    Kim, S.-W. et al. Robustness of gut microbiota of healthy adults in response to probiotic intervention revealed by high-throughput pyrosequencing. DNA Res. 20, 241–253 (2013).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  43. 43.

    Dethlefsen, L. & Relman, D. A. Incomplete recovery and individualized responses of the human distal gut microbiota to repeated antibiotic perturbation. Proc. Natl Acad. Sci. USA 108, 4554–4561 (2011).

    PubMed  Article  Google Scholar 

  44. 44.

    Ng, K. M. et al. Microbiota-liberated host sugars facilitate post-antibiotic expansion of enteric pathogens. Nature 502, 96–99 (2013).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  45. 45.

    Hacquard, S. et al. Microbiota and host nutrition across plant and animal kingdoms. Cell Host Microbe 17, 603–616 (2015).

    PubMed  Article  CAS  Google Scholar 

  46. 46.

    Werner, G. D. & Kiers, E. T. Order of arrival structures arbuscular mycorrhizal colonization of plants. New Phytol. 205, 1515–1524 (2015).

    PubMed  Article  CAS  Google Scholar 

  47. 47.

    Braun-Kiewnick, A., Jacobsen, B. J. & Sands, D. C. Biological control of Pseudomonas syringae pv. syringae, the causal agent of basal kernel blight of barley, by antagonistic Pantoea agglomerans. Phytopathology 90, 368–375 (2000).

    PubMed  Article  CAS  Google Scholar 

  48. 48.

    Fukami, T. Historical contingency in community assembly: integrating niches, species pools, and priority effects. Ann. Rev. Ecol. Evol. Syst. 46, 1–23 (2015).

    Article  Google Scholar 

  49. 49.

    Wei, Z. et al. Trophic network architecture of root-associated bacterial communities determines pathogen invasion and plant health. Nat. Commun. 6, 8413 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  50. 50.

    Weller, D. M. Biological control of soilborne plant pathogens in the rhizosphere with bacteria. Ann. Rev. Phytopathol. 26, 379–407 (1988).

    Article  Google Scholar 

  51. 51.

    Morris, C. E. & Monier, J.-M. The ecological significance of biofilm formation by plant-associated bacteria. Ann. Rev. Phytopathol. 41, 429–453 (2003).

    Article  CAS  Google Scholar 

  52. 52.

    Pieterse, C. M. et al. Induced systemic resistance by beneficial microbes. Ann. Rev. Phytopathol. 52, 347–375 (2014).

    Article  CAS  Google Scholar 

  53. 53.

    Wrather, J. A. & Koenning, S. R. Effects of diseases on soybean yields in the United States 1996 to 2007. Plant Health Prog. https://doi.org/10.1094/PHP-2009-0401-01-RS (2009).

  54. 54.

    Freilich, S. et al. Competitive and cooperative metabolic interactions in bacterial communities. Nat. Commun. 2, 589 (2011).

    PubMed  Article  CAS  Google Scholar 

  55. 55.

    Masciarelli, O., Llanes, A. & Luna, V. A new PGPR co-inoculated with Bradyrhizobium japonicum enhances soybean nodulation. Microbiol. Res. 169, 609–615 (2014).

    PubMed  Article  CAS  Google Scholar 

  56. 56.

    Cassan, F. et al. Azospirillum brasilense Az39 and Bradyrhizobium japonicum E109, inoculated singly or in combination, promote seed germination and early seedling growth in corn (Zea mays L.) and soybean (Glycine max L.). Eur. J. Soil Biol. 45, 28–35 (2009).

    Article  CAS  Google Scholar 

  57. 57.

    Paredes, S. H. et al. Design of synthetic bacterial communities for predictable plant phenotypes. PLOS Biol. 16, e2003962 (2018).

    Article  Google Scholar 

  58. 58.

    Lundberg, D. S. et al. Defining the core Arabidopsis thaliana root microbiome. Nature 488, 86–90 (2012).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  59. 59.

    Barea, J.-M., Pozo, M. J., Azcon, R. & Azcon-Aguilar, C. Microbial co-operation in the rhizosphere. J. Exp. Bot. 56, 1761–1778 (2005).

    PubMed  Article  CAS  Google Scholar 

  60. 60.

    Agler, M. T. et al. Microbial hub taxa link host and abiotic factors to plant microbiome variation. PLOS Biol. 14, e1002352 (2016).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  61. 61.

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

    PubMed  Article  CAS  Google Scholar 

  62. 62.

    Coyte, K. Z., Schluter, J. & Foster, K. R. The ecology of the microbiome: networks, competition, and stability. Science 350, 663–666 (2015).

    PubMed  Article  CAS  Google Scholar 

  63. 63.

    Newman, M. E. J. Networks: an Introduction (Oxford University Press, New York, 2010).

  64. 64.

    Toju, H. et al. Species-rich networks and eco-evolutionary synthesis at the metacommunity level. Nat. Ecol. Evol. 1, 0024 (2017).

    Article  Google Scholar 

  65. 65.

    Bai, Y. et al. Functional overlap of the Arabidopsis leaf and root microbiota. Nature 528, 364–369 (2015).

    PubMed  Article  CAS  Google Scholar 

  66. 66.

    Layeghifard, M., Hwang, D. M. & Guttman, D. S. Disentangling interactions in the microbiome: a network perspective. Trends Microbiol. 25, 217–228 (2017).

    PubMed  Article  CAS  Google Scholar 

  67. 67.

    Hartman, K., van der Heijden, M. G., Roussely-Provent, V., Walser, J.-C. & Schlaeppi, K. Deciphering composition and function of the root microbiome of a legume plant. Microbiome 5, 2 (2017).

    PubMed  PubMed Central  Article  Google Scholar 

  68. 68.

    Bianciotto, V. et al. An obligately endosymbiotic mycorrhizal fungus itself harbors obligately intracellular bacteria. Appl. Env. Micriobiol. 62, 3005–3010 (1996).

    CAS  Google Scholar 

  69. 69.

    Behie, S. W. et al. Carbon translocation from a plant to an insect-pathogenic endophytic fungus. Nat. Commun. 8, 14245 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  70. 70.

    Langille, M. G. et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat. Biotech. 31, 814–821 (2013).

    Article  CAS  Google Scholar 

  71. 71.

    Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  72. 72.

    Delgado-Baquerizo, M. et al. A global atlas of the dominant bacteria found in soil. Science 359, 320–325 (2018).

    PubMed  Article  CAS  Google Scholar 

  73. 73.

    Deyle, E. R., May, R. M., Munch, S. B. & Sugihara, G. Tracking and forecasting ecosystem interactions in real time. P. Roy. Soc. Lond. B Bio. 283, 20152258 (2016).

    Article  Google Scholar 

  74. 74.

    Sugihara, G. et al. Detecting causality in complex ecosystems. Science 338, 496–500 (2012).

    PubMed  Article  CAS  Google Scholar 

  75. 75.

    Ushio, M. et al. Fluctuating interaction network and time-varying stability of a natural fish community. Nature 554, 360–363 (2018).

    PubMed  Article  CAS  Google Scholar 

  76. 76.

    Suzuki, K., Yoshida, K., Nakanishi, Y. & Fukuda, S. An equation-free method reveals the ecological interaction networks within complex microbial ecosystems. Methods Ecol. Evol. 8, 1774–1785 (2017).

    Article  Google Scholar 

  77. 77.

    Schreiber, T. Measuring information transfer. Phys. Rev. Lett. 85, 461–464 (2000).

    PubMed  Article  CAS  Google Scholar 

  78. 78.

    Chang, C.-W., Ushio, M. & Hsieh, C.-H. Empirical dynamic modeling for beginners. Ecol. Res. 32, 785–796 (2017).

    Article  Google Scholar 

  79. 79.

    Vandeputte, D. et al. Quantitative microbiome profiling links gut community variation to microbial load. Nature 551, 507–511 (2017).

    PubMed  CAS  Google Scholar 

  80. 80.

    Smets, W. et al. A method for simultaneous measurement of soil bacterial abundances and community composition via 16S rRNA gene sequencing. Soil Biol. Biochem. 96, 145–151 (2016).

    Article  CAS  Google Scholar 

  81. 81.

    Nguyen, N. H. et al. FUNGuild: an open annotation tool for parsing fungal community datasets by ecological guild. Fungal Ecol. 20, 241–248 (2016).

    Article  Google Scholar 

  82. 82.

    van der Heijden, M. G., de Bruin, S., Luckerhoff, L., van Logtestijn, R. S. & Schlaeppi, K. A widespread plant-fungal-bacterial symbiosis promotes plant biodiversity, plant nutrition and seedling recruitment. ISME J. 10, 389–399 (2016).

    PubMed  Article  CAS  Google Scholar 

  83. 83.

    Sheth, R. U., Cabral, V., Chen, S. P. & Wang, H. H. Manipulating bacterial communities by in situ microbiome engineering. Trends Genet. 32, 189–200 (2016).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  84. 84.

    Mee, M. T., Collins, J. J., Church, G. M. & Wang, H. H. Syntrophic exchange in synthetic microbial communities. Proc. Natl Acad. Sci. USA 111, 2149–2156 (2014).

    Article  CAS  Google Scholar 

  85. 85.

    Fondi, M. & Liò, P. Multi-omics and metabolic modelling pipelines: challenges and tools for systems microbiology. Microb. Res. 171, 52–64 (2015).

    Article  CAS  Google Scholar 

  86. 86.

    Peay, K. G. et al. Convergence and contrast in the community structure of Bacteria, Fungi and Archaea along a tropical elevation–climate gradient. FEMS Microbiol. Ecol. 93, 5 (2017).

    Article  CAS  Google Scholar 

  87. 87.

    Narisawa, K., Hambleton, S. & Currah, R. S. Heteroconium chaetospira, a dark septate root endophyte allied to the Herpotrichiellaceae (Chaetothyriales) obtained from some forest soil samples in Canada using bait plants. Mycoscience 48, 274–281 (2007).

    Article  Google Scholar 

  88. 88.

    Usuki, F. & Narisawa, K. A mutualistic symbiosis between a dark septate endophytic fungus, Heteroconium chaetospira, and a nonmycorrhizal plant, Chinese cabbage. Mycologia 99, 175–184 (2007).

    PubMed  Article  CAS  Google Scholar 

  89. 89.

    Aleklett, K. et al. Build your own soil: exploring microfluidics to create microbial habitat structures. ISME J. 12, 312–319 (2018).

    PubMed  Article  Google Scholar 

  90. 90.

    Li, R., Lv, X., Zhang, X., Saeed, O. & Deng, Y. Microfluidics for cell-cell interactions: A review. Front. Chem. Sci. Eng. 10, 90–98 (2016).

    Article  Google Scholar 

  91. 91.

    Nichols, D. et al. Use of ichip for high-throughput in situ cultivation of “uncultivable” microbial species. Appl. Env. Micriobiol. 76, 2445–2450 (2010).

    Article  CAS  Google Scholar 

  92. 92.

    Ikeda, S. et al. Development of a bacterial cell enrichment method and its application to the community analysis in soybean stems. Microb. Ecol. 58, 703–714 (2009).

    PubMed  Article  CAS  Google Scholar 

  93. 93.

    Ikeda, S. et al. Community-and genome-based views of plant-associated bacteria: plant–bacterial interactions in soybean and rice. Plant Cell Physiol. 51, 1398–1410 (2010).

    PubMed  Article  CAS  Google Scholar 

  94. 94.

    Hosokawa, M. et al. Droplet-based microfluidics for high-throughput screening of a metagenomic library for isolation of microbial enzymes. Biosens. Bioelectr. 67, 379–385 (2015).

    Article  CAS  Google Scholar 

  95. 95.

    Song, Y., Yin, H. & Huang, W. E. Raman activated cell sorting. Curr. Opin. Chem. Biol. 33, 1–8 (2016).

    PubMed  Article  CAS  Google Scholar 

  96. 96.

    Grossmann, G. et al. The RootChip: an integrated microfluidic chip for plant science. Plant Cell 23, 4234–4240 (2011).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  97. 97.

    Massalha, H., Korenblum, E., Malitsky, S., Shapiro, O. H. SpringerAmpamp; Aharoni, A. Live imaging of root–bacteria interactions in a microfluidics setup. Proc. Natl Acad. Sci. USA 114, 4549–4554 (2017).

    PubMed  Article  CAS  Google Scholar 

  98. 98.

    Monteagudo-Mera, A. et al. High purity galacto-oligosaccharides enhance specific Bifidobacterium species and their metabolic activity in the mouse gut microbiome. Benef. Microb. 7, 247–264 (2016).

    Article  CAS  Google Scholar 

  99. 99.

    Tilman, D., Cassman, K. G., Matson, P. A., Naylor, R. & Polasky, S. Agricultural sustainability and intensive production practices. Nature 418, 671–677 (2002).

    PubMed  Article  CAS  Google Scholar 

  100. 100.

    Yachi, S. & Loreau, M. Biodiversity and ecosystem productivity in a fluctuating environment: the insurance hypothesis. Proc. Natl Acad. Sci. USA 96, 1463–1468 (1999).

    PubMed  Article  CAS  Google Scholar 

  101. 101.

    Maherali, H. & Klironomos, J. N. Influence of phylogeny on fungal community assembly and ecosystem functioning. Science 316, 1746–1748 (2007).

    PubMed  Article  CAS  Google Scholar 

  102. 102.

    Mundt, C. Use of multiline cultivars and cultivar mixtures for disease management. Ann. Rev. Phytopathol. 40, 381–410 (2002).

    Article  CAS  Google Scholar 

  103. 103.

    Suzuki, S. U. & Sasaki, A. How does the resistance threshold in spatially explicit epidemic dynamics depend on the basic reproductive ratio and spatial correlation of crop genotypes? J. Theor. Biol. 276, 117–125 (2011).

    PubMed  Article  Google Scholar 

  104. 104.

    Isbell, F. et al. Benefits of increasing plant diversity in sustainable agroecosystems. J. Ecol. 105, 871–879 (2017).

    Article  Google Scholar 

  105. 105.

    Prieto, I. et al. Complementary effects of species and genetic diversity on productivity and stability of sown grasslands. Nat. Plants 1, 15033 (2015).

    PubMed  Article  CAS  Google Scholar 

  106. 106.

    Pham, T. A. N. & Lawley, T. D. Emerging insights on intestinal dysbiosis during bacterial infections. Curr. Opin. Microbiol. 17, 67–74 (2014).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  107. 107.

    Shen, D., Wu, G. & Suk, H.-I. Deep learning in medical image analysis. Ann. Rev. Biomed. Eng. 19, 221–248 (2017).

    Article  CAS  Google Scholar 

  108. 108.

    Torkamani, A., Andersen, K. G., Steinhubl, S. R. & Topol, E. J. High-definition medicine. Cell 170, 828–843 (2017).

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  109. 109.

    Scheffer, M., Carpenter, S. R., Dakos, V. & van Nes, E. H. Generic indicators of ecological resilience: Inferring the chance of a critical transition. Ann. Rev. Ecol. Evol. Syst. 46, 145–167 (2015).

    Article  Google Scholar 

  110. 110.

    Schmidt, M. & Lipson, H. Distilling free-form natural laws from experimental data. Science 324, 81–85 (2009).

    PubMed  Article  CAS  Google Scholar 

  111. 111.

    Murphy, K. P. Machine Learning: a Probabilistic Perspective (MIT press, London, 2012).

  112. 112.

    Sugiura, R. et al. Field phenotyping system for the assessment of potato late blight resistance using RGB imagery from an unmanned aerial vehicle. Biosyst. Eng. 148, 1–10 (2016).

    Article  Google Scholar 

  113. 113.

    Araus, J. L. & Cairns, J. E. Field high-throughput phenotyping: the new crop breeding frontier. Trends Plant Sci. 19, 52–61 (2014).

    PubMed  Article  CAS  Google Scholar 

  114. 114.

    Quesada-González, D. & Merkoçi, A. Mobile phone-based biosensing: an emerging “diagnostic and communication” technology. Biosens. Bioelectr. 92, 549–562 (2017).

    Article  CAS  Google Scholar 

  115. 115.

    Pan, S. J. & Yang, Q. A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22, 1345–1359 (2010).

    Article  Google Scholar 

  116. 116.

    Brundrett, M. C. Coevolution of roots and mycorrhizas of land plants. New Phytol. 154, 275–304 (2002).

    Article  Google Scholar 

  117. 117.

    Foster, K. R., Schluter, J., Coyte, K. Z. & Rakoff-Nahoum, S. The evolution of the host microbiome as an ecosystem on a leash. Nature 548, 43–51 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  118. 118.

    Franzosa, E. A. et al. Relating the metatranscriptome and metagenome of the human gut. Proc. Natl Acad. Sci. USA 111, 2329–2338 (2014).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank Takashi Akagi and three anonymous reviewers for their insightful comments on the manuscript. This work was financially supported by JSPS KAKENHI Grant (26711026), JST PRESTO (JPMJPR16Q6), and the Funding Program for Next Generation World-Leading Researchers of Cabinet Office, the Government of Japan (GS014) to H.T, DOE Award DE-SC0016097 to KGP, and by a European Research Council Grant (335542) to E.T.K.

Author information

Affiliations

Authors

Contributions

H.T. designed the study and wrote the first draft. H.T. and E.T.K. edited the final version of the manuscript based on discussion with all the authors.

Corresponding author

Correspondence to Hirokazu Toju.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Toju, H., Peay, K.G., Yamamichi, M. et al. Core microbiomes for sustainable agroecosystems. Nature Plants 4, 247–257 (2018). https://doi.org/10.1038/s41477-018-0139-4

Download citation

Further reading

Search

Quick links