Rapid Evolution of Invasive Weeds Under Climate Change: Present Evidence and Future Research Needs
Bibliographic record
Abstract
Although evolution has been often seen as a gradual process through a Darwinian lens, far more rapid evolutionary change has been observed in recent times. Recent examples documenting the potential speed of invasive plant evolution have included: latitudinal flowering clines, life history shifts, or abrupt changes in morphology. The timescales for such observations range from centuries down to <5 years. Invasive weeds provide good models for the rapid changes, partly because invasive weeds exhibit unique evolutionary mechanisms integral to their success. For example, purging of their genetic load may enable invasive plants to adapt more rapidly. Other genetic mechanisms include plasticity as an evolved trait, hybridization, polyploidy, epigenetics, and clonal division of labor. It is well-demonstrated that anthropogenic stressors such as habitat disturbance or herbicide use may work synergistically with climate change stressors in fostering rapid weed evolution. Changing temperatures, moisture regimes and extreme climate events operate universally, but invasive plant species are generally better equipped than native plants to adapt. Research on this potential for rapid evolution is critical to developing more proactive management approaches that anticipate new invasive plant ecotypes adapted to changing climatic conditions.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".