Using Genes as Characters and a Parsimony Analysis to Explore the Phylogenetic Position of Turtles
Bibliographic record
Abstract
The phylogenetic position of turtles within the vertebrate tree of life remains controversial. Conflicting conclusions from different studies are likely a consequence of systematic error in the tree construction process, rather than random error from small amounts of data. Using genomic data, we evaluate the phylogenetic position of turtles with both conventional concatenated data analysis and a "genes as characters" approach. Two datasets were constructed, one with seven species (human, opossum, zebra finch, chicken, green anole, Chinese pond turtle, and western clawed frog) and 4584 orthologous genes, and the second with four additional species (soft-shelled turtle, Nile crocodile, royal python, and tuatara) but only 1638 genes. Our concatenated data analysis strongly supported turtle as the sister-group to archosaurs (the archosaur hypothesis), similar to several recent genomic data based studies using similar methods. When using genes as characters and gene trees as character-state trees with equal weighting for each gene, however, our parsimony analysis suggested that turtles are possibly sister-group to diapsids, archosaurs, or lepidosaurs. None of these resolutions were strongly supported by bootstraps. Furthermore, our incongruence analysis clearly demonstrated that there is a large amount of inconsistency among genes and most of the conflict relates to the placement of turtles. We conclude that the uncertain placement of turtles is a reflection of the true state of nature. Concatenated data analysis of large and heterogeneous datasets likely suffers from systematic error and over-estimates of confidence as a consequence of a large number of characters. Using genes as characters offers an alternative for phylogenomic analysis. It has potential to reduce systematic error, such as data heterogeneity and long-branch attraction, and it can also avoid problems associated with computation time and model selection. Finally, treating genes as characters provides a convenient method for examining gene and genome evolution.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".