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.