References

1. Moore, D. Developmental Biology of Higher Fungi: Symposium of the British Mycological Society Held at the University of Manchester April 1984 . (Cambridge University Press, 1985).
2. Hawksworth, D. L. The magnitude of fungal diversity: the 1· 5 million species estimate revisited. Mycol. Res. 105 , 1422–1432 (2001).
3. Lodge, D. J. et al. A survey of patterns of diversity in non-lichenized fungi. Mitteilungen der Eidgenössischen Forschungsanstalt für Wald, Schnee und Landschaft 70 , 157–173 (1995).
4. Hawksworth, D. L. & Rossman, A. Y. Where are all the undescribed fungi? Phytopathology 87 , 888–891 (1997).
5. Wardle, D. A. et al. Ecological linkages between aboveground and belowground biota. Science (80-. ). 304 , 1629–1633 (2004).
6. Fearnside, P. M. Amazon forest maintenance as a source of environmental services. An. Acad. Bras. Cienc. 80 , 101–114 (2008).
7. Ojea, E., Martin-Ortega, J. & Chiabai, A. Defining and classifying ecosystem services for economic valuation: the case of forest water services. Environ. Sci. Policy 19 , 1–15 (2012).
8. Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science (80-. ). 342 , 850–853 (2013).
9. Antonelli, A. et al. Amazonia is the primary source of Neotropical biodiversity. Proc. Natl. Acad. Sci. 115 , 6034–6039 (2018).
10. Ritter, C. D. et al. Locality or habitat? Exploring predictors of biodiversity in Amazonia. Ecography (Cop.).42 , (2019).
11. Ritter, C. D. et al. High-throughput metabarcoding reveals the effect of physicochemical soil properties on soil and litter biodiversity and community turnover across Amazonia. PeerJ2018 , (2018).
12. Tedersoo, L. et al. Global diversity and geography of soil fungi. Science 346 , 1052–3 (2014).
13. Dunthorn, M., Kauserud, H., Bass, D., Mayor, J. & Mahé, F. Yeasts dominate soil fungal communities in three lowland Neotropical rainforests. Environ. Microbiol. Rep. 9 , 668–675 (2017).
14. Vasco-Palacios, A. M., Bahram, M., Boekhout, T. & Tedersoo, L. Carbon content and pH as important drivers of fungal community structure in three Amazon forests. Plant Soil 1–21 (2019).
15. Schoch, C. L. et al. Nuclear ribosomal internal transcribed spacer (ITS) region as a universal DNA barcode marker for Fungi.Proc. Natl. Acad. Sci. 109 , 6241–6246 (2012).
16. Nilsson, R. H., Ryberg, M., Abarenkov, K., Sjökvist, E. & Kristiansson, E. The ITS region as a target for characterization of fungal communities using emerging sequencing technologies. FEMS Microbiol. Lett. 296 , 97–101 (2009).
17. Nilsson, R. H. et al. Mycobiome diversity: high-throughput sequencing and identification of fungi. Nat. Rev. Microbiol.17 , 95–109 (2019).
18. Tedersoo, L., Tooming-Klunderud, A. & Anslan, S. PacBio metabarcoding of Fungi and other eukaryotes: errors, biases and perspectives. New Phytol. 217 , 1370–1385 (2018).
19. Purahong, W., Mapook, A., Wu, Y. & Chen, C. Characterization of the Castanopsis carlesii Deadwood Mycobiome by Pacbio Sequencing of the Full-Length Fungal Nuclear Ribosomal Internal Transcribed Spacer ( ITS ). Front. Microbiol. 10 , 983 (2019).
20. Rhoads, A. & Au, K. F. PacBio sequencing and its applications.Genomics. Proteomics Bioinformatics 13 , 278–289 (2015).
21. Goodwin, S., McPherson, J. D. & McCombie, W. R. Coming of age: ten years of next-generation sequencing technologies. Nat. Rev. Genet. 17 , 333 (2016).
22. ter Steege, H. T. et al. A spatial model of tree alpha-diversity and tree density for the Amazon. Biodivers. Conserv. 12 , 2255–2277 (2003).
23. Hoorn, C. et al. Amazonia Through Time : Andean.Science (80-. ). 330 , 927–931 (2010).
24. Zizka, A., Steege, H. ter, Pessoa, M. do C. R. & Antonelli, A. Finding needles in the haystack: where to look for rare species in the American tropics. Ecography (Cop.). 41 , 321–330 (2018).
25. Ritter, C. D. et al. The pitfalls of biodiversity proxies: Differences in richness patterns of birds, trees and understudied diversity across Amazonia. Sci. Rep. 9 , 1–13 (2019).
26. Myster, R. W. The physical structure of forests in the Amazon Basin: a review. Bot. Rev. 82 , 407–427 (2016).
27. Vogel, T. M. et al. TerraGenome: a consortium for the sequencing of a soil metagenome. (2009).
28. Laurance, S. G. W. et al. Influence of soils and topography on Amazonian tree diversity: a landscape‐scale study. J. Veg. Sci. 21 , 96–106 (2010).
29. Higgins, M. A. et al. Geological control of floristic composition in Amazonian forests. J. Biogeogr. 38 , 2136–2149 (2011).
30. Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41 , D590–D596 (2012).
31. Benson, D. A. et al. GenBank. Nucleic Acids Res.46 , D41–D47 (2018).
32. Tedersoo, L. & Lindahl, B. Fungal identification biases in microbiome projects. Environ. Microbiol. Rep. 8 , 774–779 (2016).
33. Anslan, S., Bahram, M., Hiiesalu, I. & Tedersoo, L. PipeCraft: Flexible open‐source toolkit for bioinformatics analysis of custom high‐throughput amplicon sequencing data. Mol. Ecol. Resour.17 , e234–e240 (2017).
34. Rognes, T., Flouri, T., Nichols, B., Quince, C. & Mahé, F. VSEARCH: a versatile open source tool for metagenomics. PeerJ 4 , e2584 (2016).
35. Schloss, P. D. et al. Introducing mothur: Open-Source, Platform-Independent, Community-Supported Software for Describing and Comparing Microbial Communities. Appl. Environ. Microbiol.75 , 7537 LP – 7541 (2009).
36. Bengtsson-Palme, J. et al. Improved software detection and extraction of ITS1 and ITS2 from ribosomal ITS sequences of fungi and other eukaryotes for analysis of environmental sequencing data.Methods Ecol. Evol. 4 , 914–919 (2013).
37. Edgar, R. C. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat. Methods 10 , 996 (2013).
38. Frøslev, T. G. et al. Algorithm for post-clustering curation of DNA amplicon data yields reliable biodiversity estimates. Nat. Commun. 8 , 1188 (2017).
39. Camacho, C. et al. BLAST+: architecture and applications.BMC Bioinformatics 10 , 421 (2009).
40. Abarenkov, K. et al. The UNITE database for molecular identification of fungi – recent updates and future perspectives.New Phytol. 186 , 281–285 (2010).
41. Nilsson, R. H. et al. The UNITE database for molecular identification of fungi: handling dark taxa and parallel taxonomic classifications. Nucleic Acids Res. 47 , D259–D264 (2018).
42. Cochrane, G., Karsch-Mizrachi, I., Takagi, T. & Sequence Database Collaboration, I. N. The international nucleotide sequence database collaboration. Nucleic Acids Res. 44 , D48–D50 (2016).
43. R Core Team. The R development core team. R: A Language and Environment for Statistical Computing 1 , (2003).
44. Jost, L. Entropy and diversity. Oikos 113 , 363–375 (2006).
45. McMurdie, P. J. & Holmes, S. Waste not, want not: why rarefying microbiome data is inadmissible. PLoS Comput. Biol. 10 , e1003531 (2014).
46. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol.15 , 550 (2014).
47. Oksanen, J. et al. Vegan: community ecology package. R package version 1.17-4. http//cran. r-project. org>. Acesso em 23 , 2010 (2010).
48. Nielsen, U. N., Ayres, E., Wall, D. H. & Bardgett, R. D. Soil biodiversity and carbon cycling: a review and synthesis of studies examining diversity–function relationships. Eur. J. Soil Sci.62 , 105–116 (2011).
49. Lauber, C. L., Hamady, M., Knight, R. & Fierer, N. Pyrosequencing-based assessment of soil pH as a predictor of soil bacterial community structure at the continental scale. Appl. Environ. Microbiol. 75 , 5111–5120 (2009).
50. Rue, H. et al. INLA: functions which allow to perform a full Bayesian analysis of structured additive models using Integrated Nested Laplace Approximation. R Packag. version 0.0 (2009).
51. Goslee, S. C. & Urban, D. L. The ecodist package for dissimilarity-based analysis of ecological data. J. Stat. Softw.22 , 1–19 (2007).
52. De Caceres, M., Jansen, F. & De Caceres, M. M. Package ‘indicspecies’. Relatsh. between species groups sites. R Packag. version 1 , (2016).
53. Oliveros, J. C. VENNY. An interactive tool for comparing lists with Venn Diagrams. http//bioinfogp. cnb. csic. es/tools/venny/index. html (2007).
54. Wickham, H. tidyverse: Easily Install and Load “Tidyverse” Packages (Version R package version 1.1. 1). (2017).
55. Wickham, H. ggplot2: elegant graphics for data analysis . (Springer, 2016).
56. Braga-Neto, R., Luizão, R. C. C., Magnusson, W. E., Zuquim, G. & de Castilho, C. V. Leaf litter fungi in a Central Amazonian forest: the influence of rainfall, soil and topography on the distribution of fruiting bodies. Biodivers. Conserv. 17 , 2701–2712 (2008).
57. Barr, D. J. S. Chytridiomycota. in Systematics and Evolution93–112 (Springer, 2001).
58. Kosa, G. et al. High-throughput screening of Mucoromycota fungi for production of low-and high-value lipids. Biotechnol. Biofuels 11 , 66 (2018).
59. Swift, M. J. Basidiomycetes as components of forest ecosystems.Decomposer basidiomycetes their Biol. Ecol. (1982).
60. Liu, J. et al. Soil carbon content drives the biogeographical distribution of fungal communities in the black soil zone of northeast China. Soil Biol. Biochem. 83 , 29–39 (2015).
61. Wang, J. T. et al. Soil pH determines the alpha diversity but not beta diversity of soil fungal community along altitude in a typical Tibetan forest ecosystem. J. Soils Sediments 15 , 1224–1232 (2015).
62. Rousk, J. et al. Soil bacterial and fungal communities across a pH gradient in an arable soil. ISME J. 4 , 1340 (2010).
63. Glassman, S. I., Wang, I. J. & Bruns, T. D. Environmental filtering by pH and soil nutrients drives community assembly in fungi at fine spatial scales. Mol. Ecol. 26 , 6960–6973 (2017).
64. Pärtel, M., Bennett, J. A. & Zobel, M. Macroecology of biodiversity: disentangling local and regional effects. New Phytol. 211 , 404–410 (2016).
65. Borges, S. H. et al. Bird communities in Amazonian white‐sand vegetation patches: effects of landscape configuration and biogeographic context. Biotropica 48 , 121–131 (2016).
66. ter Steege, H. & Hammond, D. S. Character convergence, diversity, and disturbance in tropical rain forest in Guyana. Ecology82 , 3197–3212 (2001).
67. Haugaasen, T. & Peres, C. A. Floristic, edaphic and structural characteristics of flooded and unflooded forests in the lower Rio Purús region of central Amazonia, Brazil. Acta Amaz. 36 , 25–35 (2006).
68. Assis, R. L. et al. Patterns of tree diversity and composition in Amazonian floodplain paleo‐várzea forest. J. Veg. Sci. 26 , 312–322 (2015).
69. Damasco, G., Vicentini, A., Castilho, C. V, Pimentel, T. P. & Nascimento, H. E. M. Disentangling the role of edaphic variability, flooding regime and topography of A mazonian white‐sand vegetation.J. Veg. Sci. 24 , 384–394 (2013).
70. Adeney, J. M., Christensen, N. L., Vicentini, A. & Cohn‐Haft, M. White‐sand ecosystems in Amazonia. Biotropica 48 , 7–23 (2016).
71. Singer, R. & Araujo, I. de J. da S. Litter decomposition and ectomycorrhiza in Amazonian forests. 1. A comparison of litter decomposing and ectomycorrhizal basidiomycetes in latosol-terra-firme rain forest and white podzol campinarana. Acta Amaz 25–42 (1979).
72. Singer, R. & Aguiar, I. A. Litter decomposing and ectomycorrhizalBasidiomycetes in an igapó forest. Plant Syst. Evol. 153 , 107–117 (1986).
73. Singer, R., Araujo, I. & Ivory, M. H. The ectotrophically mycorrhizal fungi of the neotropical lowlands, especially central Amazonia.(Litter decomposition and ectomycorrhiza in Amazonian forests 2.). Beihefte zur Nov. hedwigia (1983).
74. Roy, M. et al. Diversity and distribution of ectomycorrhizal fungi from Amazonian lowland white‐sand forests in Brazil and French Guiana. Biotropica 48 , 90–100 (2016).
75. Vasco-Palacios, A. M., Hernandez, J., Peñuela-Mora, M. C., Franco-Molano, A. E. & Boekhout, T. Ectomycorrhizal fungi diversity in a white sand forest in western Amazonia. Fungal Ecol.31 , 9–18 (2018).
76. Tedersoo, L. & Nara, K. General latitudinal gradient of biodiversity is reversed in ectomycorrhizal fungi. New Phytol.185 , 351–354 (2010).
77. Tedersoo, L. et al. Towards global patterns in the diversity and community structure of ectomycorrhizal fungi. Mol. Ecol.21 , 4160–4170 (2012).
78. Janzen, D. H. Tropical blackwater rivers, animals, and mast fruiting by the Dipterocarpaceae. Biotropica 69–103 (1974).
79. Looney, B. P., Ryberg, M., Hampe, F., Sánchez‐García, M. & Matheny, P. B. Into and out of the tropics: global diversification patterns in a hyperdiverse clade of ectomycorrhizal fungi. Mol. Ecol.25 , 630–647 (2016).
80. Huang, D., Meier, R., Todd, P. A. & Chou, L. M. Slow mitochondrial COI sequence evolution at the base of the metazoan tree and its implications for DNA barcoding. J. Mol. Evol. 66 , 167–174 (2008).
81. Quail, M. A. et al. A tale of three next generation sequencing platforms: comparison of Ion Torrent, Pacific Biosciences and Illumina MiSeq sequencers. BMC Genomics 13 , 341 (2012).
82. Prodan, A. et al. Comparing bioinformatic pipelines for microbial 16S rRNA amplicon sequencing. PLoS One 15 , e0227434 (2020).
83. Pereira, E. J. de A. L., Ferreira, P. J. S., de Santana Ribeiro, L. C., Carvalho, T. S. & de Barros Pereira, H. B. Policy in Brazil (2016–2019) threaten conservation of the Amazon rainforest.Environ. Sci. Policy 100 , 8–12 (2019).
84. Dinerstein, E. et al. An ecoregion-based approach to protecting half the terrestrial realm. Bioscience 67 , 534–545 (2017).
85. Team, Q. D. QGIS geographic information system. Open Source Geospatial Found. Proj. Versão 2 , (2015).

TABLES:
Table 1. Soil effects on OTU Shannon diversity by marker. The table shows the coefficients of each predictor in four Bayesian general multivariate regression models using stochastic partial differential equations (SPDE) that explicitly consider spatial correlation, modelling OTU diversity dependent on soil properties for Amazonian fungi in litter and soil. Since the organic carbon content and pH are considered important variables for soil biota, we use them as independent variables. Bold indicates important predictor variables (credible intervals not crossing zero). The importance of soil properties differed between markers and were significant only for the soil diversity. Carbon content was important for 18S and ITS soil, and chemical PC1 was important for COI and ITS.