Continental scale structuring of forest and soil diversity via functional traits

An Author Correction to this article was published on 03 October 2019

This article has been updated

Abstract

Trait-based ecology claims to offer a mechanistic approach for explaining the drivers that structure biological diversity and predicting the responses of species, trophic interactions and ecosystems to environmental change. However, support for this claim is lacking across broad taxonomic groups. A framework for defining ecosystem processes in terms of the functional traits of their constituent taxa across large spatial scales is needed. Here, we provide a comprehensive assessment of the linkages between climate, plant traits and soil microbial traits at many sites spanning a broad latitudinal temperature gradient from tropical to subalpine forests. Our results show that temperature drives coordinated shifts in most plant and soil bacterial traits but these relationships are not observed for most fungal traits. Shifts in plant traits are mechanistically associated with soil bacterial functional traits related to carbon (C), nitrogen (N) and phosphorus (P) cycling, indicating that microbial processes are tightly linked to variation in plant traits that influence rates of ecosystem decomposition and nutrient cycling. Our results are consistent with hypotheses that diversity gradients reflect shifts in phenotypic optima signifying local temperature adaptation mediated by soil nutrient availability and metabolism. They underscore the importance of temperature in structuring the functional diversity of plants and soil microbes in forest ecosystems and how this is coupled to biogeochemical processes via functional traits.

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Fig. 1: Coordinated trade-offs in plant functional traits and their relation to microbial and ecosystem processes.
Fig. 2: Shifts in community-weighted leaf and microbial traits that influence soil nutrient availability across 30 forest plots at six sites spanning a broad latitudinal temperature gradient.
Fig. 3: Adaptive trait continuum for plants and microbes spanning a broad latitudinal temperature gradient.
Fig. 4: Relationships between microbial and plant functional traits that are relevant for ecosystem nutrient cycling.

Data availability

Raw sequencing data have been deposited in the NCBI Sequence Read Archive under accession code PRJNA308872. The OTU tables and microarray data presented are available at http://www.ou.edu/ieg/publications/datasets. Additional data files and r-scripts are available at https://osf.io/thjxs/. Community-weighted mean trait data are available as Supplementary Data 13.

Code availability

R-script used for data formatting and statistics are available on the Open Science Framework website https://osf.io/thjxs/. Code for the simulation is available as Supplementary Data 4.

Change history

  • 03 October 2019

    An amendment to this paper has been published and can be accessed via a link at the top of the paper.

References

  1. 1.

    Grigulis, K. et al. Relative contributions of plant traits and soil microbial properties to mountain grassland ecosystem services. J. Ecol. 101, 47–57 (2013).

    Article  Google Scholar 

  2. 2.

    Funk, J. L. et al. Revisiting the holy grail: using plant functional traits to understand ecological processes. Biol. Rev. Camb. Phil. Soc. 92, 1156–1173 (2017).

    Article  Google Scholar 

  3. 3.

    Wardle, D. A. et al. Ecological linkages between aboveground and belowground biota. Science 304, 1629–1633 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Bardgett, R. D. & van der Putten, W. H. Belowground biodiversity and ecosystem functioning. Nature 515, 505–511 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Dı́az, S. & Cabido, M. Vive la différence: plant functional diversity matters to ecosystem processes. Trends Ecol. Evol. 16, 646–655 (2001).

    Article  Google Scholar 

  6. 6.

    Enquist, B. J. et al. Scaling from traits to ecosystems. Adv. Ecol. Res. 52, 249–318 (2015).

    Article  Google Scholar 

  7. 7.

    Green, J. L., Bohannan, B. J. M. & Whitaker, R. J. Microbial biogeography: from taxonomy to traits. Science 320, 1039–1043 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Martiny, A. C., Treseder, K. & Pusch, G. Phylogenetic conservatism of functional traits in microorganisms. ISME J. 7, 830–838 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Bardgett, R. D., Mommer, L. & De Vries, F. T. Going underground: root traits as drivers of ecosystem processes. Trends Ecol. Evol. 29, 692–699 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Krause, S. et al. Trait-based approaches for understanding microbial biodiversity and ecosystem functioning. Front. Microbiol. 5, 251 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Martiny, J. B. H., Jones, S. E., Lennon, J. T. & Martiny, A. C. Microbiomes in light of traits: a phylogenetic perspective. Science 350, aac9323 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Díaz, S. & Cabido, M. Plant functional types and ecosystem function in relation to global change. J. Veg. Sci. 8, 463–474 (1997).

    Article  Google Scholar 

  13. 13.

    Kattge, J. et al. TRY–a global database of plant traits. Glob. Change Biol. 17, 2905–2935 (2011).

    Article  Google Scholar 

  14. 14.

    Kimball, S. et al. Can functional traits predict plant community response to global change? Ecosphere 7, e01602 (2016).

    Article  Google Scholar 

  15. 15.

    Enquist, B. J. et al. Assessing trait-based scaling theory in tropical forests spanning a broad temperature gradient. Glob. Ecol. Biogeogr. 26, 1357–1373 (2017).

    Article  Google Scholar 

  16. 16.

    Lennon, J. T., Aanderud, Z. T., Lehmkuhl, B. K. & Schoolmaster, D. R. Mapping the niche space of soil microorganisms using taxonomy and traits. Ecology 93, 1867–1879 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Edwards, K. F., Litchman, E. & Klausmeier, C. A. Functional traits explain phytoplankton community structure and seasonal dynamics in a marine ecosystem. Ecol. Lett. 16, 56–63 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Pellissier, L. et al. Plant species distributions along environmental gradients: do belowground interactions with fungi matter? Front. Plant Sci. 4, 500 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Phillips, R. P., Brzostek, E. & Midgley, M. G. The mycorrhizal-associated nutrient economy: a new framework for predicting carbon-nutrient couplings in temperate forests. New Phytol. 199, 41–51 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Bennett, J. A. & Klironomos, J. Climate, but not trait, effects on plant-soil feedback depend on mycorrhizal type in temperate forests. Ecosphere 9, e02132 (2018).

    Article  Google Scholar 

  21. 21.

    Barberán, A. et al. Relating belowground microbial composition to the taxonomic, phylogenetic, and functional trait distributions of trees in a tropical forest. Ecol. Lett. 18, 1397–1405 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Reich, P. B. The world-wide ‘fast–slow’ plant economics spectrum: a traits manifesto. J. Ecol. 102, 275–301 (2014).

    Article  Google Scholar 

  23. 23.

    Clarke, A. Principles of Thermal Ecology: Temperature, Energy and Life (Oxford Univ. Press, 2017).

  24. 24.

    Michaletz, S. T. et al. The energetic and carbon economic origins of leaf thermoregulation. Nat. Plants 2, 16129 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Kobe, R. K., Lepczyk, C. A. & Iyer, M. Resorption efficiency decreases with increasing green leaf nutrients in a global dataset. Ecology 86, 2780–2792 (2005).

    Article  Google Scholar 

  26. 26.

    Parton, W. et al. Global-scale similarities in nitrogen release patterns during long-term decomposition. Science 315, 361–364 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Garnier, E. et al. Plant functional markers capture ecosystem properties during secondary succession. Ecology 85, 2630–2637 (2004).

    Article  Google Scholar 

  28. 28.

    Reich, P. B. & Oleksyn, J. Global patterns of plant leaf N and P in relation to temperature and latitude. Proc. Natl Acad. Sci. USA 101, 11001–11006 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Han, W., Fang, J., Guo, D. & Zhang, Y. Leaf nitrogen and phosphorus stoichiometry across 753 terrestrial plant species in China. New Phytol. 168, 377–385 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. 30.

    He, M. et al. Leaf nitrogen and phosphorus of temperate desert plants in response to climate and soil nutrient availability. Sci. Rep. 4, 6932 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Chapin, F. S. 3rd Effects of plant traits on ecosystem and regional processes: a conceptual framework for predicting the consequences of global change. Ann. Bot. 91, 455–463 (2003).

    Article  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Cornwell, W. K. et al. Plant species traits are the predominant control on litter decomposition rates within biomes worldwide. Ecol. Lett. 11, 1065–1071 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Hobbie, S. E. Plant species effects on nutrient cycling: revisiting litter feedbacks. Trends Ecol. Evol. 30, 357–363 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Bakker, M. A., Carreño-Rocabado, G. & Poorter, L. Leaf economics traits predict litter decomposition of tropical plants and differ among land use types. Funct. Ecol. 25, 473–483 (2010).

    Article  Google Scholar 

  35. 35.

    Cheeke, T. E. et al. Dominant mycorrhizal association of trees alters carbon and nutrient cycling by selecting for microbial groups with distinct enzyme function. New Phytol. 214, 432–442 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Averill, C., Dietze, M. C. & Bhatnagar, J. M. Continental-scale nitrogen pollution is shifting forest mycorrhizal associations and soil carbon stocks. Glob. Chang. Biol. 24, 4544–4553 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Lambers, H., Poorter, H. & Van Vuuren, M. M. I. (eds) Inherent Variation in Plant Growth: Physiological Mechanisms and Ecological Consequences (Backhuys, 1998).

  38. 38.

    Read, D. J. & Perez-Moreno, J. Mycorrhizas and nutrient cycling in ecosystems—a journey towards relevance? New Phytol. 157, 475–492 (2003).

    Article  Google Scholar 

  39. 39.

    Kerkhoff, A. J., Enquist, B. J., Elser, J. J. & Fagan, W. F. Plant allometry, stoichiometry and the temperature-dependence of primary productivity: plant allometry, stoichiometry and productivity. Glob. Ecol. Biogeogr. 14, 585–598 (2005).

    Article  Google Scholar 

  40. 40.

    Walker, T. W. & Syers, J. K. The fate of phosphorus during pedogenesis. Geoderma 15, 1–19 (1976).

    Article  CAS  Google Scholar 

  41. 41.

    Vitousek, P. M. & Farrington, H. Nutrient limitation and soil development: experimental test of a biogeochemical theory. Biogeochemistry 37, 63–75 (1997).

    Article  CAS  Google Scholar 

  42. 42.

    Elser, J. J., Dobberfuhl, D. R., MacKay, N. A. & Schampel, J. H. Organism size, life history, and N:P stoichiometry. Bioscience 46, 674–684 (1996).

    Article  Google Scholar 

  43. 43.

    Elser, J. J. et al. Biological stoichiometry from genes to ecosystems. Ecol. Lett. 3, 540–550 (2000).

    Article  Google Scholar 

  44. 44.

    Elser, J. J. et al. Growth rate–stoichiometry couplings in diverse biota. Ecol. Lett. 6, 936–943 (2003).

    Article  Google Scholar 

  45. 45.

    Levins, R. Evolution in Changing Environments: Some Theoretical Explorations (Princeton Univ. Press, 1968).

  46. 46.

    Carnicer, J. et al. A unified framework for diversity gradients: the adaptive trait continuum. Glob. Ecol. Biogeogr. 22, 6–18 (2012).

    Article  Google Scholar 

  47. 47.

    Carnicer, J. et al. Global biodiversity, stoichiometry and ecosystem function responses to human-induced C–N–P imbalances. J. Plant Physiol. 172, 82–91 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. 48.

    Hartman, W. H. & Richardson, C. J. Differential nutrient limitation of soil microbial biomass and metabolic quotients (qCO2): is there a biological stoichiometry of soil microbes? PLoS ONE 8, e57127 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. 49.

    Grace, J. B. et al. Guidelines for a graph-theoretic implementation of structural equation modeling. Ecosphere 3, 73 (2012).

    Article  Google Scholar 

  50. 50.

    Lefcheck, J. S. piecewiseSEM : piecewise structural equation modelling in r for ecology. Methods Ecol. Evol. 7, 573–579 (2016).

    Article  Google Scholar 

  51. 51.

    Shipley, B. The AIC model selection method applied to path analytic models compared using a d-separation test. Ecology 94, 560–564 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Reich, P. B. et al. Generality of leaf trait relationships: a test across six biomes. Ecology 80, 1955–1969 (1999).

    Article  Google Scholar 

  53. 53.

    Craine, J. M. et al. Global patterns of foliar nitrogen isotopes and their relationships with climate, mycorrhizal fungi, foliar nutrient concentrations, and nitrogen availability. New Phytol. 183, 980–992 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. 54.

    Koerselman, W. & Meuleman, A. F. M. The vegetation N:P ratio: a new tool to detect the nature of nutrient limitation. J. Appl. Ecol. 33, 1441–1450 (1996).

    Article  Google Scholar 

  55. 55.

    Craine, J. M. et al. Convergence of soil nitrogen isotopes across global climate gradients. Sci. Rep. 5, 8280 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. 56.

    Chalot, M. & Brun, A. Physiology of organic nitrogen acquisition by ectomycorrhizal fungi and ectomycorrhizas. FEMS Microbiol. Rev. 22, 21–44 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. 57.

    Enquist, B. J. & Niklas, K. J. Invariant scaling relations across tree-dominated communities. Nature 410, 655–660 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. 58.

    Zhou, J. et al. Temperature mediates continental-scale diversity of microbes in forest soils. Nat. Commun. 7, 12083 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. 59.

    Weiser, M. D. et al. Toward a theory for diversity gradients: the abundance-adaptation hypothesis. Ecography 41, 255–264 (2018).

    Article  Google Scholar 

  60. 60.

    Westoby, M. A leaf–height–seed (LHS) plant ecology strategy scheme. Plant Soil 199, 213–227 (1998).

    Article  CAS  Google Scholar 

  61. 61.

    Wright, I. J. et al. The worldwide leaf economics spectrum. Nature 428, 821–827 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. 62.

    Van Der Heijden, M. G. A. & Scheublin, T. R. Functional traits in mycorrhizal ecology: their use for predicting the impact of arbuscular mycorrhizal fungal communities on plant growth and ecosystem functioning. New Phytol. 174, 244–250 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  63. 63.

    Legay, N. et al. Influence of plant traits, soil microbial properties, and abiotic parameters on nitrogen turnover of grassland ecosystems. Ecosphere 7, e01448 (2016).

    Article  Google Scholar 

  64. 64.

    Eviner, V. T., Chapin, F. S. 3rd & Vaughn, C. E. Seasonal variations in plant species effects on soil N and P dynamics. Ecology 87, 974–986 (2006).

    Article  PubMed  PubMed Central  Google Scholar 

  65. 65.

    Violle, C., Reich, P. B., Pacala, S. W., Enquist, B. J. & Kattge, J. The emergence and promise of functional biogeography. Proc. Natl Acad. Sci. USA 111, 13690–13696 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. 66.

    Hevia, V. et al. Trait-based approaches to analyze links between the drivers of change and ecosystem services: synthesizing existing evidence and future challenges. Ecol. Evol. 7, 831–844 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  67. 67.

    Zhou, J. et al. Reproducibility and quantitation of amplicon sequencing-based detection. ISME J. 5, 1303–1313 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. 68.

    Zhou, J. et al. Random sampling process leads to overestimation of β-iversity of microbial communities. MBio 4, e00324–13 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  69. 69.

    Zhou, J. et al. High-throughput metagenomic technologies for complex microbial community analysis: open and closed formats. MBio 6, e02288–14 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. 70.

    Lear, G. et al. Methods for the extraction, storage, amplification and sequencing of DNA from environmental samples. N. Z. J. Ecol. 42, 10–50A (2018).

    Google Scholar 

  71. 71.

    Pérez-Harguindeguy, N. et al. New handbook for standardised measurement of plant functional traits worldwide. Aust. J. Bot. 61, 167–234 (2013).

    Article  Google Scholar 

  72. 72.

    Reich, P. B., Walters, M. B. & Ellsworth, D. S. From tropics to tundra: global convergence in plant functioning. Proc. Natl Acad. Sci. USA 94, 13730–13734 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. 73.

    Poorter, H. & Lambers, H. Is interspecific variation in relative growth rate positively correlated with biomass allocation to the leaves? Am. Nat. 138, 1264–1268 (1991).

    Article  Google Scholar 

  74. 74.

    Hodgson, J. G. et al. Is leaf dry matter content a better predictor of soil fertility than specific leaf area? Ann. Bot. 108, 1337–1345 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. 75.

    Robinson, D. δ15N as an integrator of the nitrogen cycle. Trends Ecol. Evol. 16, 153–162 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. 76.

    Hobbie, E. A. & Colpaert, J. V. Nitrogen availability and colonization by mycorrhizal fungi correlate with nitrogen isotope patterns in plants. New Phytol. 157, 115–126 (2003).

    Article  CAS  Google Scholar 

  77. 77.

    Kerkhoff, A. J., Enquist, B. J., Elser, J. J. & Fagan, W. F. Plant allometry, stoichiometry and the temperature-dependence of primary productivity. Glob. Ecol. Biogeogr. 14, 585–598 (2005).

    Article  Google Scholar 

  78. 78.

    Gloaguen, J. C. & Touffet, J. C–N evolution in the leaves and during litter decomposition under Atlantic climate—the beech and some conifers. Ann. Des. Sci. For. 39, 219–230 (1982).

    Article  Google Scholar 

  79. 79.

    Enríquez, S., Duarte, C. M. & Sand-Jensen, K. Patterns in decomposition rates among photosynthetic organisms: the importance of detritus C:N:P content. Oecologia 94, 457–471 (1993).

    Article  PubMed  PubMed Central  Google Scholar 

  80. 80.

    Pérez-Harguindeguy, N. et al. Chemistry and toughness predict leaf litter decomposition rates over a wide spectrum of functional types and taxa in central Argentina. Plant Soil 218, 21–30 (2000).

    Article  Google Scholar 

  81. 81.

    Zhou, J., Bruns, M. A. & Tiedje, J. M. DNA recovery from soils of diverse composition. Appl. Environ. Microbiol. 62, 316–322 (1996).

    CAS  PubMed  PubMed Central  Google Scholar 

  82. 82.

    Magoč, T. & Salzberg, S. L. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27, 2957–2963 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. 83.

    Kong, Y. Btrim: a fast, lightweight adapter and quality trimming program for next-generation sequencing technologies. Genomics 98, 152–153 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. 84.

    Edgar, R. C., Haas, B. J., Clemente, J. C., Quince, C. & Knight, R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27, 2194–2200 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. 85.

    Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. 86.

    DeSantis, T. Z. et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl. Environ. Microbiol. 72, 5069–5072 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. 87.

    Caporaso, J. G. et al. PyNAST: a flexible tool for aligning sequences to a template alignment. Bioinformatics 26, 266–267 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. 88.

    Edgar, R. C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. 89.

    Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2—approximately maximum-likelihood trees for large alignments. PLoS ONE 5, e9490 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. 90.

    Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. 91.

    McMurdie, P. J. & Holmes, S. Waste not, want not: why rarefying microbiome data is inadmissible. PLoS Comput. Biol. 10, e1003531 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. 92.

    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).

    Article  PubMed  PubMed Central  Google Scholar 

  93. 93.

    Tu, Q. et al. GeoChip 4: a functional gene-array-based high-throughput environmental technology for microbial community analysis. Mol. Ecol. Resour. 14, 914–928 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  94. 94.

    Wang, C. et al. Aridity threshold in controlling ecosystem nitrogen cycling in arid and semi-arid grasslands. Nat. Commun. 5, 4799 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. 95.

    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 

  96. 96.

    Wang, B. & Qiu, Y.-L. Phylogenetic distribution and evolution of mycorrhizas in land plants. Mycorrhiza 16, 299–363 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. 97.

    He, Z. et al. GeoChip 3.0 as a high-throughput tool for analyzing microbial community composition, structure and functional activity. ISME J. 4, 1167–1179 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. 98.

    He, Z. et al. GeoChip: a comprehensive microarray for investigating biogeochemical, ecological and environmental processes. ISME J. 1, 67–77 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. 99.

    Wen, C. et al. Evaluation of the reproducibility of amplicon sequencing with Illumina MiSeq platform. PLoS ONE 12, e0176716 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. 100.

    Groemping, U. Relative importance for linear regression in R: the package relaimpo. J. Stat. Softw. 17, 1–27 (2006).

    Article  Google Scholar 

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Acknowledgements

We thank the members of the LTER Network and the Smithsonian Tropical Research Institute—Barro Colorado Island who participated in data collection. We thank C. Sides, T. Birt, L. Sloat, A. Henderson, J. Messier and B. Blonder for field assistance with vegetation sampling. We thank J. Wright for assistance with sampling at Barro Colorado Island. We also thank E. Brodie for helpful comments on earlier drafts. Funding for this study was provided by the US National Science Foundation MacroSystems Biology program NSF EF-1065844.

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All authors contributed to the intellectual development of this study. V.B., B.J.E., M.K., S.T.M. and M.D.W. collected the field experimental data. Y.D., Z.H., J.Z., D.N., L.S., Q.T., J.V. and J.W. collected the sequencing and GeoChip data, and performed statistical analyses and data integration for microbial communities. D.N. developed and ran the simulation comparing rarefied versus non-rarefied sequence data. V.B., B.J.E. and S.T.M. performed statistical analyses and drafted the manuscript with help from Z.H., J.Z., M.K., R.W. and M.D.W.

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Correspondence to Vanessa Buzzard.

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Supplementary Information

Supplementary Figs. 1–7, Box 1, Tables 1–6 and references.

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Supplementary Data 1

All abundance weighted plant functional trait data for each community (plot). Site codes definitions are AND, HJ Andrews Experimental forest; BCI, Barro Colorado Island; CWT, Coweeta LTER; HFR, Harvard Forest; LUQ, Luquillo LTER; NWT, Niwot Ridge LTER with each plot labelled 1-5 at each site. Abund represents the abundance of woody plants observed within each plot. All traits are measured locally.

Supplementary Data 2

All abundance weighted fungal functional trait data for each community (plot). Site codes definitions are AND, HJ Andrews Experimental forest; BCI, Barro Colorado Island; CWT, Coweeta LTER; HFR, Harvard Forest; LUQ, Luquillo LTER; NWT, Niwot Ridge LTER with each plot labelled 1-5 at each site. Abund represents the abundance of fungi based on ITS observed within each plot. All traits are measured locally and definitions are available for GeoChip data in Supplementary Table 1.

Supplementary Data 3

All abundance weighted bacterial functional trait data for each community (plot). Site codes definitions are AND, HJ Andrews Experimental forest; BCI, Barro Colorado Island; CWT, Coweeta LTER; HFR, Harvard Forest; LUQ, Luquillo LTER; NWT, Niwot Ridge LTER with each plot labelled 1-5 at each site. Abund represents the abundance of bacteria based on 16S observed within each plot. All traits are measured locally and definitions are available for GeoChip data in Supplementary Table 1.

Supplementary Data 4

Script and data used for simulation to test use of rarified sequence data.

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Buzzard, V., Michaletz, S.T., Deng, Y. et al. Continental scale structuring of forest and soil diversity via functional traits. Nat Ecol Evol 3, 1298–1308 (2019). https://doi.org/10.1038/s41559-019-0954-7

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