Congruence, Consensus, and the Comparative Phylogeography of Codistributed Species in California
Bibliographic record
Abstract
Comparative phylogeography has emerged as a means of understanding the spatial patterns of genetic divergence of codistributed species. However, researchers are often frustrated because of the lack of appropriate statistical tests to assess concordancy of multiple phylogeographic trees. We develop a method for testing congruence across multiple species and synthesizing the data into a regional supertree. Nine phylogeographic data sets of species with different life histories and ecologies were statistically compared using maximum agreement subtrees (MAST) and showed a high degree of concordancy. A supertree combining the different phylogeographic trees was then computed using matrix representation with parsimony, and the groups defined by this supertree were tested against climatic data to investigate a potential mechanism driving divergence. Our data suggest that species and genetic lineages in California are shaped by climatic regimes. The supertree method in combination with MAST represents a new approach to test congruence hypotheses and detect common geographic signals in comparative phylogeography.
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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 | 0.002 |
| 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".