Transitions Into Freezing Environments Linked With Shifts in Phylogenetic Integration Between Vitaceae Leaf Traits
Bibliographic record
Abstract
Understanding how the intrinsic ability of populations and species to meet shifting selective demands shapes evolutionary patterns over both short and long timescales is a major question in biology. One major axis of evolutionary flexibility can be measured by phenotypic integration and modularity. The strength, scale, and structure of integration may constrain or catalyze evolution in the face of new selective pressures. We analyze a dataset of seven leaf measurements across Vitaceae to examine how correlations in trait divergence are linked to transitions between freezing and nonfreezing habitats. We assess this by applying a custom algorithm to compare the timing of habitat shifts to changes in the structure of evolutionary trait correlation at discrete points along a phylogeny. We also explore these patterns in relation to lineage diversification rates to understand how and whether patterns in the evolvability of complex multivariate phenotypes are linked to higher-level macroevolutionary dynamics. We found that shifts in the structure, but not the overall strength, of phylogenetic integration of leaves precipitate colonization of freezing climates. Lineages that underwent associated shifts in leaf trait integration and subsequent movement into freezing habitats also displayed lower turnover and higher net diversification, suggesting a link among shifting vectors of selection, internal constraint, and lineage persistence in the face of changing environments.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".