Diversification and the evolution of dispersal ability in the tribe Brassiceae (Brassicaceae)
Bibliographic record
Abstract
BACKGROUND AND AIMS: Dispersal and establishment ability can influence evolutionary processes such as geographic isolation, adaptive divergence and extinction probability. Through these population-level dynamics, dispersal ability may also influence macro-evolutionary processes such as species distributions and diversification. This study examined patterns of evolution of dispersal-related fruit traits, and how the evolution of these traits is correlated with shifts in geographic range size, habitat and diversification rates in the tribe Brassiceae (Brassicaceae). METHODS: The phylogenetic analysis included 72 taxa sampled from across the Brassiceae and included both nuclear and chloroplast markers. Dispersal-related fruit characters were scored and climate information for each taxon was retrieved from a database. Correlations between fruit traits, seed characters, habitat, range and climate were determined, together with trait-dependent diversification rates. KEY RESULTS: It was found that the evolution of traits associated with limited dispersal evolved only in association with compensatory traits that increase dispersal ability. The evolution of increased dispersal ability occurred in multiple ways through the correlated evolution of different combinations of fruit traits. The evolution of traits that increase dispersal ability was in turn associated with larger seed size, increased geographic range size and higher diversification rates. CONCLUSIONS: This study provides evidence that the evolution of increased dispersal ability and larger seed size, which may increase establishment ability, can also influence macro-evolutionary processes, possibly by increasing the propensity for long-distance dispersal. In particular, it may increase speciation and consequent diversification rates by increasing the likelihood of geographic and thereby reproductive isolation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".