Schlesinger, W. H. & Bernhardt, E. S. Biogeochemistry: an Analysis of Global Change (Elsevier/Academic Press, 2012).
Fernandez, C. W., Langley, J. A., Chapman, S., McCormack, M. L. & Koide, R. T. The decomposition of ectomycorrhizal fungal necromass. Soil Biol. Biochem. 93, 38–49 (2016).
Google Scholar
Glassman, S. I. et al. Decomposition responses to climate depend on microbial community composition. Proc. Natl Acad. Sci. USA 115, 11994–11999 (2018).
Google Scholar
Mushinski, R. M. et al. Microbial mechanisms and ecosystem flux estimation for aerobic NOy emissions from deciduous forest soils. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.1814632116 (2019).
Prosser, J. I. Dispersing misconceptions and identifying opportunities for the use of ‘omics’ in soil microbial ecology. Nat. Rev. Microbiol. 13, 439–446 (2015).
Google Scholar
Delgado-Baquerizo, M. et al. A global atlas of the dominant bacteria found in soil. Science 359, 320–325 (2018).
Google Scholar
Tedersoo, L. et al. Global diversity and geography of soil fungi. Science 346, 1256688 (2014).
Google Scholar
Bahram, M. et al. Structure and function of the global topsoil microbiome. Nature 560, 233–237 (2018).
Google Scholar
Drews, G. The roots of microbiology and the influence of Ferdinand Cohn on microbiology of the 19th century. FEMS Microbiol. Rev. 24, 225–249 (2000).
Google Scholar
Chase, J. M. Spatial scale resolves the niche versus neutral theory debate. J. Veg. Sci. 25, 319–322 (2014).
Google Scholar
Ricklefs, R. E. & Renner, S. S. Global correlations in tropical tree species richness and abundance reject neutrality. Science 335, 464–467 (2012).
Google Scholar
Cavender-Bares, J., Keen, A. & Miles, B. Phylogenetic structure of Floridian plant communities depends on taxonomic and spatial scale. Ecology 87, S109–S122 (2006).
Google Scholar
Cavender-Bares, J., Kozak, K. H., Fine, P. V. A. & Kembel, S. W. The merging of community ecology and phylogenetic biology. Ecol. Lett. 12, 693–715 (2009).
Google Scholar
Ladau, J. & Eloe-Fadrosh, E. A. Spatial, temporal, and phylogenetic scales of microbial ecology. Trends Microbiol. 27, 662–669 (2019).
Google Scholar
Elena, S. F. & Lenski, R. E. Evolution experiments with microorganisms: the dynamics and genetic bases of adaptation. Nat. Rev. Genet. 4, 457–469 (2003).
Google Scholar
Diaz, S. & Cabido, M. Plant functional types and ecosystem function in relation to global change. J. Veg. Sci. 8, 463–474 (1997).
Google Scholar
Violle, C. et al. Let the concept of trait be functional! Oikos 116, 882–892 (2007).
Google Scholar
Fierer, N., Bradford, M. A. & Jackson, R. B. Toward an ecological classification of soil bacteria. Ecology 88, 1354–1364 (2007).
Google Scholar
Nguyen, N. H. et al. FUNGuild: an open annotation tool for parsing fungal community datasets by ecological guild. Fungal Ecol. 20, 241–248 (2016).
Google Scholar
Whittaker, R. H. Communities and Ecosystems (Macmillan, 1975).
Gibbons, S. M. Microbial community ecology: function over phylogeny. Nat. Ecol. Evol. 1, 0032 (2017).
Google Scholar
Locey, K. J. & Lennon, J. T. Scaling laws predict global microbial diversity. Proc. Natl Acad. Sci. USA 113, 5970–5975 (2016).
Google Scholar
Dietze, M. C. Ecological Forecasting (Princeton Univ. Press, 2017).
Losos, J. B. Phylogenetic niche conservatism, phylogenetic signal and the relationship between phylogenetic relatedness and ecological similarity among species. Ecol. Lett. 11, 995–1003 (2008).
Google Scholar
Ramirez, K. S. et al. Detecting macroecological patterns in bacterial communities across independent studies of global soils. Nat. Microbiol. 3, 189–196 (2018).
Google Scholar
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).
Google Scholar
Hubbell, S. P. The Unified Neutral Theory of Biodiversity and Biogeography (Princeton Univ. Press, 2001).
Leibold, M. A., Urban, M. C., De Meester, L., Klausmeier, C. A. & Vanoverbeke, J. Regional neutrality evolves through local adaptive niche evolution. Proc. Natl Acad. Sci. USA 116, 2612–2617 (2019).
Google Scholar
Dietze, M. & Lynch, H. Forecasting a bright future for ecology. Front. Ecol. Environ. 17, 3 (2019).
Google Scholar
Thompson, L. R. et al. A communal catalogue reveals Earth’s multiscale microbial diversity. Nature 551, 457–463 (2017).
Google Scholar
Todd-Brown, K. E. O. et al. Causes of variation in soil carbon simulations from CMIP5 Earth system models and comparison with observations. Biogeosciences 10, 1717–1736 (2013).
Google Scholar
Todd-Brown, K. E. O. et al. Changes in soil organic carbon storage predicted by Earth system models during the 21st century. Biogeosciences 10, 18969–19004 (2013).
Google Scholar
Lekberg, Y. et al. More bang for the buck? Can arbuscular mycorrhizal fungal communities be characterized adequately alongside other fungi using general fungal primers? New Phytol. 220, 971–976 (2018).
Google Scholar
Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).
Google Scholar
Running, S., Mu, Q. & Zhao, M. MOD17A3 MODIS/Terra Net Primary Production Yearly L4 Global 1km SIN Grid V055. NASA EOSDIS Land Processes DAAC (NASA, 2011); https://cmr.earthdata.nasa.gov/search/concepts/C198653829-LPDAAC_ECS.html
Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).
Google Scholar
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).
Google Scholar
Kõljalg, U. et al. Towards a unified paradigm for sequence-based identification of fungi. Mol. Ecol. 22, 5271–5277 (2013).
Google Scholar
Steidinger, B. S. et al. Climatic controls of decomposition drive the global biogeography of forest-tree symbioses. Nature 569, 404–408 (2019).
Google Scholar
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).
Google Scholar
Albright, M. B. N., Chase, A. B. & Martiny, J. B. H. Experimental evidence that stochasticity contributes to bacterial composition and functioning in a decomposer community. mBio 10, e00568-19 (2019).
Google Scholar
Berlemont, R. & Martiny, A. C. Phylogenetic distribution of potential cellulases in bacteria. Appl. Environ. Microbiol. 79, 1545–1554 (2013).
Google Scholar
Ho, A., Lonardo, D. P. D. & Bodelier, P. L. E. Revisiting life strategy concepts in environmental microbial ecology. Microbiol. Ecol. https://doi.org/10.1093/femsec/fix006 (2017).
Wang, L. & Wise, M. J. Glycogen with short average chain length enhances bacterial durability. Naturwissenschaften 98, 719–729 (2011).
Google Scholar
Soil Microbe Community Composition (DP1.10081.001) (National Ecological Observatory Network (NEON)); https://data.neonscience.org
Averill, C., Dietze, M. C. & Bhatnagar, J. M. Continental-scale nitrogen pollution is shifting forest mycorrhizal associations and soil carbon stocks. Glob. Change Biol. 24, 4544–4553 (2018).
Google Scholar
Pawlowsky-Glahn, V., Egozcue, J. J. & Tolosana-Delgado, R. Modelling and Analysis of Compositional Data (John Wiley & Sons, 2015).
Smithson, M. & Verkuilen, J. A better lemon squeezer? Maximum-likelihood regression with beta-distributed dependent variables. Psychol. Methods 11, 54–71 (2006).
Google Scholar
Cribari-Neto, F. & Zeileis, A. Beta regression in R. J. Stat. Softw. 34, 1–22 (2010).
Johnson, N. L., Kotz, S. & Balakrishnan, N. Discrete Multivariate Distributions (Wiley, 1997).
Plummer, M. JAGS: a program for analysis of Bayesian graphical models using Gibbs sampling. In Proc. 3rd International Workshop on Distributed Statistical Computing 1–8 (2003); http://www.ci.tuwien.ac.at/Conferences/DSC-2003/Drafts/Plummer.pdf
Denwood, M. J. runjags: an R package providing interface utilities, model templates, parallel computing methods and additional distributions for MCMC models in JAGS. J. Stat. Softw. 71, 1–25 (2016).
Google Scholar
Gelman, A. & Hill, J. Data Analysis Using Regression and Multilevel/Hierarchical Models (Cambridge Univ. Press, 2007).
R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019).
Moran, P. A. P. Notes on continuous stochastic phenomena. Biometrika 37, 17–23 (1950).
Google Scholar
Paradis, E., Claude, J. & Strimmer, K. APE: analyses of phylogenetics and evolution in R language. Bioinformatics 20, 289–290 (2004).
Google Scholar
Source: Ecology - nature.com