Mitochondrial genomic data are effective at placing mycoheterotrophic lineages in plant phylogeny
Bibliographic record
Abstract
Fully mycoheterotrophic plants can be difficult to place in plant phylogeny due to elevated substitution rates associated with photosynthesis loss. This potentially limits the effectiveness of downstream analyses of mycoheterotrophy that depend on accurate phylogenetic inference. Although mitochondrial genomic data sets are rarely used in plant phylogenetics, theory predicts that they should be resilient to long-branch artefacts, thanks to their generally slow evolution, coupled with limited rate elevation in heterotrophs. We examined the utility of mitochondrial genomes for resolving contentious higher-order placements of mycoheterotrophic lineages in two test cases: monocots (focusing on Dioscoreales) and Ericaceae. We find Thismiaceae to be distantly related to Burmanniaceae in the monocot order Dioscoreales, conflicting with current classification schemes based on few gene data sets. We confirm that the unusual Afrothismia is related to Taccaceae-Thismiaceae, with a corresponding independent loss of photosynthesis. In Ericaceae we recovered the first well supported relationships among its five major lineages: mycoheterotrophic Ericaceae are not monophyletic, as pyroloids are inferred to be sister to core Ericaceae, and monotropoids to arbutoids. Genes recovered from mitochondrial genomes collectively resolved previously ambiguous mycoheterotroph higher-order relationships. We propose that mitochondrial genomic data should be considered in standardised gene panels for inferring overall plant phylogeny.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".