Biological knowledge shortfalls impede conservation efforts in poorly studied taxa—A case study of Laboulbeniomycetes
Bibliographic record
Abstract
Abstract Most empirical research on biological shortfalls has focused on vertebrate taxa. This is important given many species in poorly studied groups such as invertebrates, plants, and fungi are predicted to possess high conservation risk. Here, we focus on Laboulbeniomycetes: a class of microfungi that are understudied. We examined four shortfalls: Linnean (knowledge gaps in species diversity), Wallacean (knowledge gaps in distributions), Latimerian (knowledge gaps in species persistence), and the newly introduced Scottian (knowledge gaps in species conservation assessments) shortfalls. The Linnean shortfall in Laboulbeniomycetes is hard to predict due to inconsistent species description rates. Analysis of distribution patterns indicates Laboulbeniomycetes are likely to experience an extremely high Wallacean shortfall, with many species having highly disjunct known distributions. Latimerian shortfall analysis shows over half (51%) of Laboulbeniomycetes have not been recorded in >50 years, while the group has a collective Scottian shortfall of 100%, given none of the 2454 described species have received an IUCN threat assessment. We suggest continued study of natural history collections, expanded citizen science programmes, and machine‐learning identification approaches as important tools for reducing knowledge shortfalls in both Laboulbeniomycetes and poorly studied taxa more generally.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".