Towards standardizing taxonomic ranks using divergence times – a case study for reconstruction of the Agaricus taxonomic system
Bibliographic record
Abstract
The recognition of taxonomic ranks in the Linnean classification system is largely arbitrary. Some authors have proposed the use of divergence time as a universally standardized criterion. Agaricus (Agaricaceae, Agaricales) is a mushroom genus that contains many species of high commercial value. Recent studies using ITS sequence data discovered 11 new phylogenetic lineages within the genus, however their taxonomic ranks were uncertain due to the lack of criteria to define them within traditional taxonomy. In this study, we analyzed ITS sequence data from 745 collections (nearly 600 being newly generated) including 86 from type specimens of previously recognized subgenera and sections. Many monophyletic groups were recognized, but most basal relationships were unresolved. One hundred and fourteen representatives of the identified ITS clades were selected in order to produce a multi-gene phylogeny based on combined LSU, tef - 1α , and rpb2 sequence data. Divergence times within the multi-gene phylogeny were estimated using BEAST v1.8. Based on phylogenetic relationships and with respect to morphology, we propose a revised taxonomic system for Agaricus that considers divergence time as a standardized criterion for establishing taxonomic ranks. We propose to segregate Agaricus into five subgenera and 20 sections. Subgenus Pseudochitonia is substantially emended; circumscription of the subgenera Agaricus and Flavoagaricus is restricted to taxa of sections Agaricus and Arvenses , respectively; and two new subgenera ( Minores and Spissicaules ) are introduced. Within Pseudochitonia , sections Bivelares , Brunneopicti , Chitonioides , Nigrobrunnescentes , Sanguinolenti and Xanthodermatei are maintained, but the latter two are reduced because we raise subsection Bohusia to sectional rank and a clade within section Xanthodermatei is formally introduced as section Hondenses ; and sections Rubricosi , Crassispori , Flocculenti , and Amoeni are introduced. Section Laeticolores is placed in the subgenus Minores and sections Rarolentes and Subrutilescentes are placed in the subgenus Spissicaules . Twenty-two new species belonging to various sections are described. This work exemplifies that ITS data, while useful at lower taxonomic levels (i.e., detection of species and species groups), are of limited value for inferring deeper phylogenetic relationships. Finally, we suggest that the establishment of a standardized taxonomic system based on divergence times could result in a more objective, and biologically more meaningful, taxonomic ranking of fungi.
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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 | 0.017 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".