Global patterns in endemicity and vulnerability of soil fungi
Bibliographic record
Abstract
This repository contains the data associated with the paper Tedersoo et al. (2022) Global patterns in endemicity and vulnerability of soil fungi // Global Change Biology. 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 (`Tables_S1-S6.xlsx`): - Table S1. Definition of ecoregions and assignment of samples to ecoregions - Table S2. GSMc dataset used for endemicity analyses - Table S3. Dataset used for modeling endemicity values - Table S4. Dataset used for calculating and mapping vulnerability scores - Table S5. Dataset used for calculating and mapping conservation value - Table S6. Additional funding sources by authors OTU distribution by samples and ecoregions (`Data_taxon_assignment_to ecoregions.xlsx`) Gridded maps: Conservation priorities for all fungi and fungal groups - ConservationPriority_AllFungi.tif - ConservationPriority_AM.tif - ConservationPriority_EcM.tif - ConservationPriority_Moulds.tif - ConservationPriority_NonEcMAgaricomycetes.tif - ConservationPriority_OHPs.tif - ConservationPriority_Pathogens.tif - ConservationPriority_Unicellular.tif - ConservationPriority_Yeasts.tif The average vulnerability of all fungi and fungal groups and the model uncertainty estimates - AverageVulnerability_AllFungi.tif - AverageVulnerability_AM.tif - AverageVulnerability_EcM.tif - AverageVulnerability_Moulds.tif - AverageVulnerability_NonEcMAgaricomycetes.tif - AverageVulnerability_OHPs.tif - AverageVulnerability_Pathogens.tif - AverageVulnerabilityUncertainty_AllFungi.tif - AverageVulnerabilityUncertainty_AM.tif - AverageVulnerabilityUncertainty_EcM.tif - AverageVulnerabilityUncertainty_Moulds.tif - AverageVulnerabilityUncertainty_NonEcMAgaricomycetes.tif - AverageVulnerabilityUncertainty_OHPs.tif - AverageVulnerabilityUncertainty_Pathogens.tif - AverageVulnerabilityUncertainty_Unicellular.tif - AverageVulnerabilityUncertainty_Yeasts.tif - AverageVulnerability_Unicellular.tif - 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 - 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) - `LandCoverChange_1960-2015.tif` MD5 checksums for all files (`MD5.md5`) Fungal groups: - AM, arbuscular mycorrhizal fungi (including all Glomeromycota but excluding all Endogonomycetes) - EcM, ectomycorrhizal fungi (excluding dubious lineages) - NonEcMAgaricomycetes, non-EcM Agaricomycetes (mostly saprotrophic fungi with usually macroscopic fruiting bodies) - Moulds (including Mortierellales, Mucorales, Umbelopsidales and Aspergillaceae and Trichocomaceae of Eurotiales and Trichoderma of Hypocreales) - Putative pathogens (including plant, animal and fungal pathogens as primary or secondary lifestyles) - OHPs, opportunistic human parasites (excluding Mortierellales) - Yeasts (excluding dimorphic yeasts) - Unicellular, other unicellular (non-yeast) fungi (including chytrids, aphids, rozellids and other early-diverging fungal lineages) Detailed processing steps can be found here: https://github.com/Mycology-Microbiology-Center/Fungal_Endemicity_and_Vulnerability
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.029 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.253 | 0.111 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".