Connecting the multiple dimensions of global soil fungal diversity

  1. Vladimir Mikryukov 1
  2. Olesya Dulya 1
  3. Alexander Zizka 2
  4. Mohammad Bahram 3
  5. Niloufar Hagh-Doust 1
  6. Sten Anslan 1
  7. Oleh Prylutskyi 4
  8. Manuel Delgado-Baquerizo 5
  9. Fernando T. Maestre 6
  10. R. Henrik Nilsson 7
  11. Jaan Pärn 1
  12. Maarja Öpik 1
  13. Mari Moora 1
  14. Martin Zobel 1
  15. Mikk Espenberg 1
  16. Ülo Mander 1
  17. Abdul Nasir Khalid 8
  18. Adriana Corrales 9
  19. Ahto Agan 10
  20. Aída M. Vasco-Palacios 11
  21. Alessandro Saitta 12
  22. Andrea C. Rinaldi 13
  23. Annemieke Verbeken 14
  24. Bobby P. Sulistyo 14
  25. Boris Tamgnoue 15
  26. Brendan Furneaux 16
  27. Camila Duarte Ritter 17
  28. Casper Nyamukondiwa 18
  29. Cathy Sharp 19
  30. César Marín 20
  31. Daniyal Gohar 21
  32. Darta Klavina 22
  33. Dipon Sharmah 23
  34. Dong Qin Dai 24
  35. Eduardo Nouhra 25
  36. Elisabeth Machteld Biersma 26
  37. Elisabeth Rähn 10
  38. Erin K. Cameron 27
  39. Eske De Crop 14
  40. Eveli Otsing 21
  41. Evgeny A. Davydov 28
  42. Felipe E. Albornoz 29
  43. Francis Q. Brearley 30
  44. Franz Buegger 31
  45. Geoffrey Zahn 32
  46. Gregory Bonito 33
  47. Inga Hiiesalu 1
  48. Isabel C. Barrio 34
  49. Jacob Heilmann-Clausen 35
  50. Jelena Ankuda 36
  51. Jiri Doležal 37
  52. John Y. Kupagme 21
  53. Jose G. Maciá-Vicente 38
  54. Joseph Djeugap Fovo 15
  55. József Geml 39
  56. Juha M. Alatalo 40
  57. Julieta Alvarez-Manjarrez 41
  58. Kadri Põldmaa 1
  59. Kadri Runnel 1
  60. Kalev Adamson 10
  61. Kari Anne Bråthen 42
  62. Karin Pritsch 31
  63. Kassim I. Tchan 43
  64. Kęstutis Armolaitis 44
  65. Kevin D. Hyde 45
  66. Kevin K. Newsham 46
  67. Kristel Panksep 47
  68. Adebola A. Lateef 48
  69. Linda Hansson 49
  70. Louis J. Lamit 50
  71. Malka Saba 51
  72. Maria Tuomi 42
  73. Marieka Gryzenhout 52
  74. Marijn Bauters 53
  75. Meike Piepenbring 54
  76. Nalin Wijayawardene 55
  77. Nourou S. Yorou 43
  78. Olavi Kurina 56
  79. Peter E. Mortimer 57
  80. Peter Meidl 58
  81. Petr Kohout 59
  82. Rasmus Puusepp 21
  83. Rein Drenkhan 10
  84. Roberto Garibay-Orijel 41
  85. Roberto Godoy 60
  86. Saad Alkahtani 61
  87. Saleh Rahimlou 21
  88. Sergey V. Dudov 62
  89. Sergei Põlme 21
  90. Soumya Ghosh 52
  91. Sunil Mundra 63
  92. Talaat Ahmed 40
  93. Tarquin Netherway 3
  94. Terry W. Henkel 64
  95. Tomas Roslin 3
  96. Vincent Nteziryayo 65
  97. Vladimir E. Fedosov 62
  98. Vladimir G. Onipchenko 62
  99. W. A. Erandi Yasanthika 45
  100. Young Woon Lim 66
  101. Michael E. Van Nuland 67
  102. Nadejda Soudzilovskaia 68
  103. Alexandre Antonelli 69
  104. Urmas Kõljalg 1
  105. Kessy Abarenkov 70
  106. Leho Tedersoo 21
  107. Mostra tots els autors/es +
  1. 1 Institute of Ecology and Earth Sciences, University of Tartu, Tartu, Estonia
  2. 2 Department of Biology, Philipps-University, Marburg, Germany
  3. 3 Department of Ecology, Swedish University of Agricultural Sciences, Uppsala, Sweden
  4. 4 Department of Mycology and Plant Resistance, School of Biology, V.N. Karazin Kharkiv National University, Kharkiv, Ukraine
  5. 5 Laboratorio de Biodiversidad y Funcionamiento Ecosistemico. Instituto de Recursos Naturales y Agrobiología de Sevilla (IRNAS), CSIC, Sevilla, Spain
  6. 6 Instituto Multidisciplinar para el Estudio del Medio 'Ramón Margalef' and Departamento de Ecología, Universidad de Alicante; 03690, Alicante, Spain
  7. 7 Gothenburg Global Biodiversity Centre, University of Gothenburg, Gothenburg, Sweden
  8. 8 Institute of Botany, University of the Punjab, Lahore, Pakistan
  9. 9 Centro de Investigaciones en Microbiología y Biotecnología-UR (CIMBIUR), Universidad del Rosario, Bogotá, Colombia
  10. 10 Institute of Forestry and Engineering, Estonian University of Life Sciences, Tartu, Estonia
  11. 11 Grupo de BioMicro y Microbiología Ambiental, Escuela de Microbiologia, Universidad de Antioquia UdeA, Medellin, Antioquia, Colombia
  12. 12 Department of Agricultural, Food and Forest Sciences, University of Palermo, Palermo, Italy.
  13. 13 Department of Biomedical Sciences, University of Cagliari, Cagliari, Italy
  14. 14 Department Biology, Ghent University, Ghent, Belgium
  15. 15 Department of Crop Science, University of Dschang, Dschang, Cameroon
  16. 16 Department of Ecology and Genetics, Uppsala University, Uppsala, Sweden
  17. 17 Instituto Juruá, Manaus, AM, Brazil
  18. 18 Department of Biological Sciences and Biotechnology, Botswana International University of Science and Technology, Palapye, Botswana
  19. 19 Natural History Museum of Zimbabwe, Bulawayo, Zimbabwe
  20. 20 Centro de Investigación e Innovación para el Cambio Climático (CiiCC), Universidad SantoTomás, Av. Ramón Picarte 1130, Valdivia, Chile
  21. 21 Center of Mycology and Microbiology, University of Tartu, Tartu, Estonia
  22. 22 Latvian State Forest Research Insitute Silava, Salaspils, Latvia
  23. 23 Department of Botany, Jawaharlal Nehru Rajkeeya Mahavidyalaya, Pondicherry University, Port Blair, India
  24. 24 College of Biological Resource and Food Engineering, Qujing Normal University, Qujing,Yunnan, China
  25. 25 Instituto Multidisciplinario de Biología Vegetal (CONICET), Universidad Nacional de Córdoba, Cordoba, Argentina
  26. 26 Natural History Museum of Denmark, Copenhagen, Denmark
  27. 27 Department of Environmental Science, Saint Mary's University, Halifax, Nova Scotia, Canada
  28. 28 Altai State University, Barnaul, Russia
  29. 29 CSIRO Environment, Wembley, WA, Australia
  30. 30 Department of Natural Sciences, Manchester Metropolitan University, Manchester, UK
  31. 31 Helmholtz Zentrum München, Neuherberg, Germany
  32. 32 Utah Valley University, Orem UT, USA
  33. 33 Plant, Soil and Microbial Sciences, Michigan State University, East Lansing MI, USA
  34. 34 Faculty of Natural and Environmental Sciences, Agricultural University of Iceland, Hvanneyri, Iceland
  35. 35 Center for Macroecology, Evolution and Climate, University of Copenhagen, Copenhagen, Denmark
  36. 36 Vokė branch, Institute of Agriculture, Lithuanian Research Centre for Agriculture and Forestry (LAMMC). Vilnius, Lithuania.
  37. 37 Czech Academy of Sciences, Institute of Botany, Czech Republic, and Department of Botany, Faculty of Science, University of South Bohemia, České Budějovice, Czech Republic
  38. 38 Plant Ecology and Nature Conservation, Wageningen University & Research, Wageningen, The Netherlands
  39. 39 ELKH-EKKE Lendület Environmental Microbiome Research Group, Eszterházy Károly Catholic University, Eger, Hungary
  40. 40 Environmental Science Center, Qatar University, Doha, Qatar
  41. 41 Instituto de Biología, Universidad Nacional Autónoma de México, Ciudad de México, México
  42. 42 Department of Arctic and Marine Biology, The Arctic University of Norway, Tromsø, Norway
  43. 43 Research Unit Tropical Mycology and Plants-Soil Fungi Interactions, University of Parakou, Parakou, Benin
  44. 44 Department of Silviculture and Ecology, Institute of Forestry, Lithuanian Research Centre for Agriculture and Forestry (LAMMC), Girionys, Lithuania.
  45. 45 Center of Excellence in Fungal Research, Mae Fah Luang University, Chiang Rai, Thailand
  46. 46 NERC British Antarctic Survey, High Cross, Cambridge, UK
  47. 47 Chair of Hydrobiology and Fishery, Estonian University of Life Sciences, Tartu, Estonia
  48. 48 Department of Plant Biology, University of Ilorin, Ilorin, Nigeria
  49. 49 Gothenburg Centre for Sustainable Development, Gothenburg, Sweden
  50. 50 Department of Biology, Syracuse University, Syracuse, NY, USA
  51. 51 Department of Plant Sciences, Quaid-i-Azam University, Islamabad, Pakistan
  52. 52 Department of Genetics, University of the Free State, Bloemfontein, South Africa
  53. 53 Department of Environment, Ghent University, Ghent, Belgium
  54. 54 Mycology Working Group, Goethe University Frankfurt am Main, Frankfurt am Main, Germany
  55. 55 College of Biological Resource and Food Engineering, Qujing Normal University, Qujing, Yunnan, China
  56. 56 Institute of Agricultural and Environmental Sciences, Estonian University of Life Sciences, Tartu, Estonia
  57. 57 Center For Mountain Futures, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming, China
  58. 58 Freie Universität Berlin, Institut für Biologie, Berlin, Germany
  59. 59 Institute of Microbiology, Czech Academy of Sciences, Prague, Czech Republic
  60. 60 Instituto Ciencias Ambientales y Evolutivas, Universidad Austral de Chile, Valdivia, Chile
  61. 61 College of Science, King Saud University, Riyadh, Saudi Arabia
  62. 62 Department of Ecology and Plant Geography, Moscow Lomonosov State University, Moscow, Russia
  63. 63 Department of Biology, College of Science, United Arab Emirates University, Al Ain, Abu Dhabi, UAE
  64. 64 Department of Biological Sciences, California State Polytechnic University, Arcata CA, USA
  65. 65 Department of Food Science and Technology, University of Burundi, Bujumbura, Burundi
  66. 66 School of Biological Sciences and Institute of Microbiology, Seoul National University, Seoul, Korea
  67. 67 Society for the Protection of Underground Networks, Dover, DE, USA
  68. 68 Centre for Environmental Sciences, Hasselt University, Hasselt, Belgium
  69. 69 Royal Botanic Gardens, Kew, Richmond, United Kingdom
  70. 70 University of Tartu Natural History Museum, Tartu, Estonia
Mostra les afiliacions +

Editor: Zenodo

Any de publicació: 2023

Tipus: Dataset

CC BY 4.0

DOI: 10.5281/ZENODO.8013448 lock_openAccés obert editor

Resum

How the multiple facets of soil fungal diversity vary worldwide remains virtually unknown, hindering the management of this essential species-rich group. By sequencing high-resolution DNA markers in over 4000 topsoil samples from natural and human-altered ecosystems across all continents, we illustrate the distributions and drivers of different levels of taxonomic and phylogenetic diversity of fungi and their ecological groups. We show the impact of precipitation and temperature interactions on fungal local species richness (alpha diversity) across different climates. Our findings reveal how temperature drives fungal compositional turnover (beta diversity) and phylogenetic diversity, linking them with regional species richness (gamma diversity). Our work integrates fungi into the principles of global biodiversity distribution and presents detailed maps for biodiversity conservation and modeling of global ecological processes. ### Data overview These datasets contain comprehensive estimates of alpha, beta, and gamma diversity. The data are provided in two formats: TIFF (Tagged Image File Format) and GeoPackage formats, which are commonly used to store geospatially-referenced data. Alpha Diversity: `Alpha_S_*` files: These files contain estimates of alpha diversity (local species diversity) for each grid cell of a raster file. `Alpha_AOA_*` files: These files outline the 'Area of Applicability' for the alpha diversity estimates. `Alpha_Uncertainty_*` files: These files contain data related to the uncertainty of the alpha diversity predictions. Uncertainty here represents the range or degree of error associated with the diversity estimates.  `Alpha_Hotspots_and_ProtectedAreas` contains information on fungal diversity hotspots and their area under protection (based on IUCN classification). 'Hotspots' are areas with exceptionally high alpha diversity. Beta Diversity: `Beta_*` files: These files include results of beta diversity analyses: maps of global compositional dissimilarity among soil fungal communities and maps of compositional turnover rate. Other files: `EcM_and_AM_GlobalDistribution`: the global distribution of areas with high richness of ectomycorrhizal and arbuscular mycorrhizal fungi. `Ecoregions_Alpha,Beta,Gamma_Diversities`: estimates of alpha, beta, and gamma diversity at the level of ecoregion cf. Tedersoo et al., 2022 (DOI:10.1111/gcb.16398).   ### Data description Alpha diversity, which is a measure of local species richness (number of Operational Taxonomic Unit (OTU) representing distinct taxa, roughly corresponding to species level). Alpha diversity is represented by the residuals from a model adjusting for sequencing depth, with zero equating to the average OTU richness in the training data set. `Alpha_S_AllFungi_Consensus.tif`: This file provides consensus estimates for total fungal alpha diversity. Within the file, there are two types of consensus estimates:     AvgW - weighted consensus estimates for alpha diversity. The weighting takes into account both the area of applicability and the goodness-of-fit for the model used to generate the estimates.     Avg - non-weighted consensus estimates for alpha diversity. Unlike AvgW, these estimates give equal weight to all models regardless of their goodness-of-fit or area of applicability. `Alpha_AOA_*`: Files containing Area of Applicability information:     A raster value of '1' represents areas that are outside the Area of Applicability     A raster value of '2' denotes areas that are inside the Area of Applicability In the files containing prediction uncertainties (`Alpha_Uncertainty_*`), two types of data are presented to quantify the amount of uncertainty in model predictions, each represented by a different band: The SD band represents the standard deviation of predictions based on different folds of cross-validation. A larger standard deviation indicates greater variability in the predictions. The IQR band represents the interquartile range (the difference between the upper and lower quartiles) of predictions. The wider the IQR, the greater variability in the predictions. `Alpha_Hotspots_and_ProtectedAreas.tif`: This file provides information on regions of exceptionally high species richness, referred to as 'hotspots', along with information about protected areas. Hotspots are identified as the top 2.5% quantiles of the richest grid cells on the map in terms of OTU richness. IUCN_1_4 - terrestrial protected areas that fall into categories I-IV, as classified by the International Union for Conservation of Nature (IUCN). These categories typically represent areas with high levels of protection, often prohibiting extractive and destructive activities to preserve biodiversity. IUCN_all - all terrestrial protected areas as recorded in the World Database on Protected Areas (WDPA) database v.1.6. It includes a wider range of protected areas beyond the categories I-IV. All_Avg - Hotspots of total fungal alpha diversity, based on the consensus map GSM_All - Hotspots of total fungal alpha diversity, based on the GSMc dataset GSM_EcM - Hotspots of ectomycorrhizal alpha diversity GSM_AM - Hotspots of arbuscular mycorrhizal alpha diversity GSM_AgarNM - Hotspots of non-EcM Agaricomycetes alpha diversity GSM_Mold - Hotspots of mold alpha diversity GSM_Pathog - Hotspots of opportunistic human parasitic fungal alpha diversity GSM_OHP - Hotspots of putative pathogenic fungal alpha diversity GSM_Unicel - Hotspots of unicellular, non-yeast fungal alpha diversity GSM_Yeast - Hotspots of yeast alpha diversity GSMc_PD - Hotspots of phylogenetic alpha diversity GSM_PDst - Hotspots of phylogenetic dispersion `EcM_and_AM_GlobalDistribution.tif`: To illustrate the worldwide distribution of ectomycorrhizal (EcM) and arbuscular mycorrhizal (AM) fungi, we have categorized their richness into three distinct groups with low (1), medium (2), and high (3) alpha diversity. These categories have been encoded in the raster file using a bitcode system. Specifically, a value of '9' indicates that both EcM and AM fungal communities  have low alpha diversity, while a value of '27' signifies that both groups of fungi are OTU-rich To assist with interpretation, a color legend has been provided in a separate QML style file (`EcM_and_AM_GlobalDistribution.qml`). This should be automatically recognized by geographic information system software, such as QGIS, to aid in visual analysis. `Beta_Taxonomic_AllFungi.tif` and `Beta_Phylogenetic_AllFungi.tif`: These files quantify the degree of difference in OTU composition of fungal communities. The measurements are based on the Generalized Dissimilarity Modelling (GDM) framework, as described by Mokany et al., 2022 (DOI:10.1111/geb.13459). Each file provides a different perspective on beta diversity: taxonomic (which is the change in species composition between different locations), and phylogenetic (the change in phylogenetic lineage composition between different locations). Each of these raster files contains three bands, with each band representing a scaled axis from a Principal Component Analysis (PCA) of the GDM-transformed environmental predictors. `Beta_LocalTurnover.tif`: This file contains estimates of local turnover in fungal communities composition estimated as the median expected compositional dissimilarity (taxonomic or phylogenetic) between each location and its closest neighbors within a 150 km radius. In addition, interquartile range (IQR) of dissimilarities is also provided.   `Ecoregions_Alpha,Beta,Gamma_Diversities.gpkg`: Median alpha, beta, and gamma diversity estimates within ecoregions. Ecoregion - Ecoregion name (cf. Tedersoo et al., 2022, DOI:10.1111/gcb.16398) area - Ecoregion area, m2 Alpha_S_AllFungi_Consensus - Richness of all fungi (S'tot), consensus map Alpha_S_AllFungi_GSMc - Richness of all fungi (S'GSMc), based on GSMc dataset Alpha_S_EcM_GSMc - Richness of ectomycorrhizal fungi (S'ecm) Alpha_S_AM_GSMc - Richness of arbuscular mycorrhizal fungi (S'am) Alpha_S_NMA_GSMc - Richness of non-EcM Agaricomycetes (S'nma) Alpha_S_Mold_GSMc - Richness of molds (S'mold) Alpha_S_OHP_GSMc - Richness of opportunistic human parasitic fungi (S'ohp) Alpha_S_Path_GSMc - Richness of putative pathogenic fungi (S'path) Alpha_S_Ucel_GSMc - Richness of  unicellular, non-yeast fungi (S'ucel) Alpha_S_Yeast_GSMc - Richness of yeasts (S'yeast) Alpha_SESPD_GSMc - Phylogenetic dispersion of fungal communities (SESPD) Beta_Taxonomic_Median - Median taxonomic dissimilarity of fungal communities (Simpson's index) Beta_Taxonomic_IQR - Interquartile range of taxonomic dissimilarities of fungal communities Beta_Phylogenetic_Median - Median phylogenetic dissimilarity of fungal communities Beta_Phylogenetic_IQR - Interquartile range of phylogenetic dissimilarities of fungal communities Gamma_AllFungi - Gamma diversity (regional species richness) for all fungi (Gtot) Gamma_EcM - Gamma diversity of ectomycorrhizal fungi (Gecm) Gamma_AM - Gamma diversity of arbuscular mycorrhizal fungi (Gam) Gamma_NMA - Gamma diversity of non-EcM Agaricomycetes (Gnma) Gamma_Mold - Gamma diversity of molds (Gmold) Gamma_Path - Gamma diversity of opportunistic human parasitic fungi (Gohp) Gamma_OHP - Gamma diversity of putative pathogenic fungi (Gpath) Gamma_Ucel - Gamma diversity of  unicellular, non-yeast fungi (Gucel) Gamma_Yeast - Gamma diversity of yeasts (Gyeast)   ### Source code The code used for data analysis and visualization of the main results of the study are available at GitHub: https://github.com/Mycology-Microbiology-Center/Global_fungal_diversity