What Do the First 597 Global Fungal Red List Assessments Tell Us about the Threat Status of Fungi?
Bibliographic record
Abstract
Fungal species are not immune to the threats facing animals and plants and are thus also prone to extinction. Yet, until 2015, fungi were nearly absent on the IUCN Red List. Recent efforts to identify fungal species under threat have significantly increased the number of published fungal assessments. The 597 species of fungi published in the 2022-1 IUCN Red List update (21 July 2022) are the basis for the first global review of the extinction risk of fungi and the threats they face. Nearly 50% of the assessed species are threatened, with 10% NT and 9% DD. For regions with a larger number of assessments (i.e., Europe, North America, and South America), subanalyses are provided. Data for lichenized and nonlichenized fungi are also summarized separately. Habitat loss/degradation followed by climate change, invasive species, and pollution are the primary identified threats. Bias in the data is discussed along with knowledge gaps. Suggested actions to address these gaps are provided along with a discussion of the use of assessments to facilitate on-the-ground conservation efforts. A research agenda for conservation mycology to assist in the assessment process and implementation of effective species/habitat management is presented.
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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 | 0.022 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".