Singleton-based species names and fungal rarity: Does the number really matter?
Bibliographic record
Abstract
Fungi are among the least known organisms on earth, with an estimated number of species between 1.5 and 10 million. This number is expected to be refined, especially with increasing knowledge about microfungi in undersampled habitats and increasing amounts of data derived from environmental DNA sequencing. A significant proportion of newly generated sequences fail to match with already named species, and thus represent what has been referred to as fungal "dark taxa". Due to the challenges associated with observing, identifying, and preserving sporophores, many macro- and microfungal species are only known from a single collection, specimen, isolate, and/or sequence-a singleton. Mycologists are consequently used to working with "rare" sequences and specimens. However, rarity and singleton phenomena lack consideration and valorization in fungal studies. In particular, the practice of publishing new fungal species names based on a single specimen remains a cause of debate. Here, we provide some elements of reflection on this issue in the light of the specificities of the fungal kingdom and global change context. If multiple independent sources of data support the existence of a new taxon, we encourage mycologists to proceed with formal description, irrespective of the number of specimens at hand. Although the description of singleton-based species may not be considered best practice, it does represent responsible science in the light of closing the Linnean biodiversity shortfall.
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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 | 0.050 | 0.156 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.009 | 0.018 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".