Global patterns in endemicity and vulnerability of soil fungi
Notice bibliographique
Résumé
This repository contains the data associated with the paper Tedersoo et al. (2022) <em>Global patterns in endemicity and vulnerability of soil fungi</em> // <strong>Global Change Biology</strong>. DOI:10.1111/gcb.16398 Fungi are highly diverse organisms and provide a wealth of ecosystem functions. However, distribution patterns and conservation needs of fungi have been very little explored compared to charismatic animals and plants. Here we assess endemicity patterns, global change vulnerability and conservation priority areas for functional groups of soil fungi based on six global surveys using a high-resolution, long-read metabarcoding approach. Endemicity of all fungi and most functional groups peaks in tropical habitats, including Amazonia, Yucatan, West-Central Africa, Sri Lanka and New Caledonia, with a negligible island effect compared with plants and animals. We also found that fungi are vulnerable mostly to drought, heat and land cover change, particularly in dry tropical regions with high human population density. Fungal conservation areas of highest priority include herbaceous wetlands, tropical forests and woodlands. We suggest that there should be more attention focused on the conservation of fungi, especially tropical root symbiotic arbuscular mycorrhizal and ectomycorrhizal fungi, unicellular early-diverging groups and macrofungi in general. Given the low overlap between endemicity of fungi and macroorganisms, but high matching in conservation needs, detailed analyses on distribution and conservation requirements are warranted for other microorganisms and soil organisms in general. This repository contains the following data associated with the publication: Supplementary tables S1 - S6 (`<strong>Tables_S1-S6.xlsx</strong>`): - Table S1. Definition of ecoregions and assignment of samples to ecoregions<br> - Table S2. GSMc dataset used for endemicity analyses<br> - Table S3. Dataset used for modeling endemicity values<br> - Table S4. Dataset used for calculating and mapping vulnerability scores<br> - Table S5. Dataset used for calculating and mapping conservation value<br> - Table S6. Additional funding sources by authors OTU distribution by samples and ecoregions (`<strong>Data_taxon_assignment_to ecoregions.xlsx</strong>`) Gridded maps: Conservation priorities for all fungi and fungal groups - ConservationPriority_AllFungi.tif<br> - ConservationPriority_AM.tif<br> - ConservationPriority_EcM.tif<br> - ConservationPriority_Moulds.tif<br> - ConservationPriority_NonEcMAgaricomycetes.tif<br> - ConservationPriority_OHPs.tif<br> - ConservationPriority_Pathogens.tif<br> - ConservationPriority_Unicellular.tif<br> - ConservationPriority_Yeasts.tif The average vulnerability of all fungi and fungal groups and the model uncertainty estimates - AverageVulnerability_AllFungi.tif<br> - AverageVulnerability_AM.tif<br> - AverageVulnerability_EcM.tif<br> - AverageVulnerability_Moulds.tif<br> - AverageVulnerability_NonEcMAgaricomycetes.tif<br> - AverageVulnerability_OHPs.tif<br> - AverageVulnerability_Pathogens.tif<br> - AverageVulnerabilityUncertainty_AllFungi.tif<br> - AverageVulnerabilityUncertainty_AM.tif<br> - AverageVulnerabilityUncertainty_EcM.tif<br> - AverageVulnerabilityUncertainty_Moulds.tif<br> - AverageVulnerabilityUncertainty_NonEcMAgaricomycetes.tif<br> - AverageVulnerabilityUncertainty_OHPs.tif<br> - AverageVulnerabilityUncertainty_Pathogens.tif<br> - AverageVulnerabilityUncertainty_Unicellular.tif<br> - AverageVulnerabilityUncertainty_Yeasts.tif<br> - AverageVulnerability_Unicellular.tif<br> - AverageVulnerability_Yeasts.tif The relative importance of predicted vulnerability of all fungi - RelativeImportanceOfVulnerability_AllFungi.tif Vulnerability to drought, heat, and land cover change for all fungi - Vulnerability_AllFungi_Heat-Drought-LandCoverChange.tif<br> - VulnerabilityUncertainty_AllFungi_Heat-Drought-LandCoverChange.tif Human footprint index based on the Land-Use Harmonisation (LUH2; Hurtt et al., 2020, doi:10.5194/gmd-13-5425-2020) - `<strong>LandCoverChange_1960-2015.tif</strong>` MD5 checksums for all files (`<strong>MD5.md5</strong>`) Fungal groups:<br> - <strong>AM</strong>, arbuscular mycorrhizal fungi (including all Glomeromycota but excluding all Endogonomycetes)<br> - <strong>EcM</strong>, ectomycorrhizal fungi (excluding dubious lineages)<br> - <strong>NonEcMAgaricomycetes</strong>, non-EcM Agaricomycetes (mostly saprotrophic fungi with usually macroscopic fruiting bodies)<br> - <strong>Moulds</strong> (including Mortierellales, Mucorales, Umbelopsidales and Aspergillaceae and Trichocomaceae of Eurotiales and Trichoderma of Hypocreales)<br> - Putative <strong>pathogens</strong> (including plant, animal and fungal pathogens as primary or secondary lifestyles)<br> - <strong>OHPs</strong>, opportunistic human parasites (excluding Mortierellales)<br> - <strong>Yeasts</strong> (excluding dimorphic yeasts)<br> - <strong>Unicellular</strong>, other unicellular (non-yeast) fungi (including chytrids, aphids, rozellids and other early-diverging fungal lineages) Detailed processing steps can be found here:<br> https://github.com/Mycology-Microbiology-Center/Fungal_Endemicity_and_Vulnerability
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».