Trait Evolution in Invasive Species
Bibliographic record
Abstract
Abstract One of the most exciting recent developments in the field of invasion biology has been the growing realisation that evolution can determine invasive species' success. Here, we review research on contemporary evolution in invasive populations, with a focus on traits that have the potential to contribute to invasive spread. Evidence available so far indicates adaptive divergence in quantitative traits predominates, although the contribution of non‐adaptive processes should not be easily discounted. Further, contemporary evolution of invasive populations appears to be more frequently spurred by abiotic factors, rather than escape from natural enemies. Important progress remains to be made on the role of hybridisation in invasion success, or the conditions under which rapid evolution of phenotypic plasticity at key traits leads to invasions. Also, we do not yet have a firm grasp on how often expansion load limits invasive spread. While convincing examples of adaptation along geographic or climatic gradients are available, we highlight conditions under which such clines would arise irrespective of biotic or abiotic conditions. We propose potentially important future lines of investigation that can illuminate the mechanistic basis of invasion success while maximising the value of invasive species for understanding evolutionary processes.
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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".