Phylogenetic Relationships, Speciation, and Origin of Armillaria in the Northern Hemisphere: A Lesson Based on rRNA and Elongation Factor 1-Alpha
Bibliographic record
Abstract
Armillaria species have a global distribution and play various roles in the natural ecosystems, e.g., pathogens, decomposers, and mycorrhizal associates. However, their taxonomic boundaries, speciation processes, and origin are poorly understood. Here, we used a phylogenetic approach with 358 samplings from Europe, East Asia, and North America to delimit the species boundaries and to discern the evolutionary forces underpinning divergence and evolution. Three species delimitation methods indicated multiple unrecognized phylogenetic species, and biological species recognition did not reflect the natural evolutionary relationships within Armillaria; for instance, biological species of A. mellea and D. tabescens are divergent and cryptic species/lineages exist associated with their geographic distributions in Europe, North America, and East Asia. While the species-rich and divergent Gallica superclade might represent three phylogenetic species (PS I, PS II, and A. nabsnona) that undergo speciation. The PS II contained four lineages with cryptic diversity associated with the geographic distribution. The genus Armillaria likely originated from East Asia around 21.8 Mya in early Miocene when Boreotropical flora (56–33.9 Mya) and the Bering land bridge might have facilitated transcontinental dispersal of Armillaria species. The Gallica superclade arose at 9.1 Mya and the concurrent vicariance events of Bering Strait opening and the uplift of the northern Tibetan plateau might be important factors in driving the lineage divergence.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".