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Enregistrement W4393656053 · doi:10.5281/zenodo.8013447

Connecting the multiple dimensions of global soil fungal diversity

2023· dataset· en· W4393656053 sur OpenAlexaff
Vladimir Mikryukov, Olesya Dulya, Alexander Zizka, Mohammad Bahram, Niloufar Hagh‐Doust, Sten Anslan, Oleh Prylutskyi, Manuel Delgado‐Baquerizo, Fernando T. Maestre, R. Henrik Nilsson, Jaan Pärn, Maarja Öpik, Mari Moora, Martin Zobel, Mikk Espenberg, Ülo Mander, Abdul Nasir Khalid, Adriana Corrales, Ahto Agan, Aída M. Vasco‐Palacios, Alessandro Saitta, Andrea C. Rinaldi, Annemieke Verbeken, Bobby P. Sulistyo, Boris Tamgnoue, Brendan Furneaux, Camila Duarte Ritter, Casper Nyamukondiwa, Cathy Sharp, César Marín, Daniyal Gohar, Dārta Kļaviņa, Dipon Sharmah, Dong Dai, Eduardo Nouhra, Elisabeth M. Biersma, Elisabeth Rähn, Erin K. Cameron, Eske De Crop, Eveli Otsing, Evgeny A. Давыдов, Felipe E. Albornoz, Francis Q. Brearley, Franz Buegger, Geoffrey Zahn, Gregory Bonito, Inga Hiiesalu, Isabel C. Barrio, Jacob Heilmann‐Clausen, Jelena Ankuda, Jiří Doležal, John Y. Kupagme, Jose G. Maciá‐Vicente, Joseph Djeugap Fovo, József Geml, Juha M. Alatalo, Julieta Alvarez‐Manjarrez, Kadri Põldmaa, Kadri Runnel, Kalev Adamson, Kari Anne Bråthen, Karin Pritsch, Kassim I. Tchan, Kęstutis Armolaitis, Kevin D. Hyde, Kevin K. Newsham, Kristel Panksep, Adebola Azeez Lateef, Linda Hansson, Louis J. Lamit, Malka Saba, Maria Tuomi, Marieka Gryzenhout, Marijn Bauters, Meike Piepenbring, Nalin N. Wijayawardene, Nourou S. Yorou, Olavi Kurina, Peter E. Mortimer, Peter Meidl, Petr Kohout, Rasmus Puusepp, Rein Drenkhan, Roberto Garibay‐Orijel, Roberto Godoy, Saad Alkahtani, Saleh Rahimlou, Sergey V. Dudov, Sergei Põlme, Soumya Ghosh, Sunil Mundra, Talaat Ahmed, Tarquin Netherway, Terry W. Henkel, Tomas Roslin, Vincent Nteziryayo, Vladimir E. Fedosov, V. G. Onipchenko, W. A. Erandi Yasanthika, Young Woon Lim, Michael E. Van Nuland, Nadejda A. Soudzilovskaia, Alexandre Antonelli, Urmas Kõljalg, Kessy Abarenkov, Leho Tedersoo

Notice bibliographique

RevueSocio-Environmental Systems Modeling · 2023
Typedataset
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiquePlant Pathogens and Fungal Diseases
Établissements canadiensSaint Mary's University
Organismes subventionnairesnon disponible
Mots-clésDiversity (politics)Fungal DiversityGeographyEcologyBiologySociologyAnthropology

Résumé

récupéré en direct d'OpenAlex

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

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Jeu de données · Signal consensuel: aucune
Score de désaccord entre enseignants0,242
Score d'incertitude au seuil0,897

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,000
Communication savante0,0000,000
Science ouverte0,0000,001
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,018
Tête enseignante GPT0,223
Écart entre enseignants0,205 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreJeu de données

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2023
Routes d'admission1
Résumé présentoui

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