Trait evolution rates shape continental patterns of species richness in North America's most diverse angiosperm genus (<i>Carex</i>, Cyperaceae)
Bibliographic record
Abstract
Abstract Ecological opportunity has been associated with increases in diversification rates across the tree of life. Under an ecological diversification model, the emergence of novel environments is hypothesized to promote morpho‐ and ecospace evolution. Whether this model holds at the clade level within the most species‐rich angiosperm genus found in North America (Carex, Cyperaceae) is yet to be tested. Recent works demonstrate a temporal coupling of climate cooling and widespread colonization ofCarexin North America, implicating ecological diversification. In addition, research has consistently found asymmetric patterns of lineage‐level diversification in the genus. Why does variation in clade sizes exist in the genus? Is ecological diversification involved? In this study, we tested whether rates of morphological and ecological trait evolution are correlated with clade‐level species richness inCarexof North America north of Mexico. We constructed a phylogeny of 477 species—an almost complete regional sample. We estimated rates of evolution of morphological traits, habitat, and climatic niche and assessed whether differences in rates of evolution correlate with species richness differences in replicate non‐nested sister clades. Our work demonstrates significant positive correlations between climatic niche rates, habitat and reproductive morphological evolution, and species richness. This coupling of trait and niche evolution and species richness in a diverse, continental clade sample strongly suggests that the ability of clades to explore niche and functional space has shaped disparities in richness and functional diversity across the North American flora region. Our findings highlight the importance of the evolutionary history of trait and niche evolution in shaping continental and regional floras.
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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 | 0.000 |
| 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) | 0.001 | 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".