Troubles with mycorrhizal mushroom identification where morphological differentiation lags behind barcode sequence divergence
Bibliographic record
Abstract
Abstract Species of Russula (Russulaceae), a large, cosmopolitan, ectomycorrhizal fungal genus are notoriously difficult to identify. To delimit species and to evaluate their morphology, we sequenced the ~400 bp ITS2 ribosomal DNA region from 713 Pacific Northwest Russula specimens from Benjamin Woo’s exceptional collection. As a topological constraint for analysis of the ITS2, we sequenced and inferred a phylogeny from the ITS, LSU, RPB2 and EF1-a regions from 50 European and North American specimens of major clades in Russula. We delimited 72 candidate species from Woo's collection’s ITS2 sequences using ABGD, GMYC, PTP, and mothur software. To guide application of names, we sequenced a ~200 bp portion of the ITS from 18 American type specimens. Of the 72 delimited species, 28 matched a type or a currently barcoded European species. Among the remaining 44 are poorly known or undescribed species. We tested the congruence of morphology with delimitations for 23 species represented by 10 or more specimens each. No morphological character alone was consistently diagnostic across all specimens of any of the 23 candidate species. Ordination of combined field characters followed by pairwise multivariate analysis of variance showed that centroids were significantly different in 221 of 253 species pair comparisons. Ordination also showed that specimens from the same species were widely dispersed, overlapping with specimens from other species. This explains why only 48.5% of specimens were correctly assigned to their species in a canonical variates analysis of combined field and spore characters. Based on sequence comparisons, we contribute to correcting the broad and confusing misapplications of European names that have long obscured patterns of Russula’s geographical distribution and diversification. Our evidence suggests that morphology in Russula diverges slowly, and that phenotypic plasticity, convergence, or retention of ancestral polymorphisms blur the distinctions among recently derived species.
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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 | 0.011 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.010 |
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".