Plant reproductive systems and evolution during biological invasion
Bibliographic record
Abstract
Recent biological invasions provide opportunities to investigate microevolution during contemporary timescales. The tempo and scope of local adaptation will be determined by the intensity of natural selection and the amounts and kinds of genetic variation within populations. In flowering plants, genetic diversity is strongly affected by interactions between reproductive systems and stochastic forces associated with immigration history and range expansion. Here, we explore the significance of reproductive system diversity for contemporary evolution during plant invasion. We focus in particular on how reproductive modes influence the genetic consequences of long-distance colonization and determine the likelihood of adaptive responses during invasion. In many clonal invaders, strong founder effects and restrictions on sexual reproduction limit opportunities for local adaptation. In contrast, adaptive changes to life-history traits should be a general expectation in both outbreeding and inbreeding species. We provide evidence that evolutionary modifications to reproductive systems promote the colonizing ability of invading populations and that reproductive timing is an important target of selection during range expansion. Knowledge of the likelihood and speed at which local adaptation evolves in invasive plants will be particularly important for management practices when evolutionary changes enhance ecological opportunities and invasive spread.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".