Why Alien Invaders Succeed: Support for the Escape‐from‐Enemy Hypothesis
Bibliographic record
Abstract
Successful biological invaders often exhibit enhanced performance following introduction to a new region. The traditional explanation for this phenomenon is that natural enemies (e.g., competitors, pathogens, and predators) present in the native range are absent from the introduced range. The purpose of this study was to test the escape-from-enemy hypothesis using the perennial plant Silene latifolia as a model system. This European native was introduced to North America in the 1800s and subsequently spread to a large part of the continent. It is now considered a problematic weed of disturbed habitats and agricultural fields in the United States and Canada. Surveys of 86 populations in the United States and Europe revealed greater levels of attack by generalist enemies (aphids, snails, floral herbivores) in Europe compared with North America. Two specialists (seed predator, anther smut fungus) that had dramatic effects on plant fitness in Europe were either absent or in very low frequency in North America. Overall, plants were 17 times more likely to be damaged in Europe than in North America. Thus, S. latifolia's successful North American invasion can, at least in part, be explained by escape from specialist enemies and lower levels of damage following introduction.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".