Leaf adaptations and species boundaries in North American <i>Cercis</i>: implications for the evolution of dry floras
Bibliographic record
Abstract
PREMISE OF THE STUDY: The North American Cercis clade spans dry to mesic climates and exhibits complex morphological variation. We tested various proposed species classifications of this group and whether aspects of leaf morphology, particularly the "drip-tip" in some regional populations, are adaptive and/or linked with phylogeny. METHODS: We made measurements on over 1100 herbarium specimens from throughout North America and analyzed the data with univariate and multivariate approaches. We analyzed phylogenetically DNA sequence data from nuclear ITS and three plastid regions from 40 samples, and estimated divergence times with a relaxed-clock Bayesian analysis. We used climate and geographic position data to predict the variation observed in leaf size and shape by using stepwise multiple linear regressions. KEY RESULTS: Morphometric analyses yielded a pattern of continuous and often clinal character variation across North America, without correlated gaps in character states. Conversely, phylogenetic and divergence time analyses yielded distinct clades from California, the interior west, and eastern North America separated by between ~12 and 16 million years. Multiple regressions yielded highly significant correlations between leaf apex shape and precipitation of the warmest quarter. CONCLUSIONS: Despite a pattern of continuous morphological character variation, the long period of geographic and presumably genetic isolation warrants the delimitation of three species. Predictive modeling supports the adaptive value of acuminate apices or "drip-tips" in mesic habitats. This suggests that Cercis leaves change more rapidly than inferred from parsimony reconstruction, which has implications for the evolution of the dry floras of North America and Eurasia.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".