The lionfish <i>Pterois</i> sp. invasion: Has the worst‐case scenario come to pass?
Bibliographic record
Abstract
This review revisits the traits thought to have contributed to the success of Indo-Pacific lionfish Pterois sp. as an invader in the western Atlantic Ocean and the worst-case scenario about their potential ecological effects in light of the more than 150 studies conducted in the past 5 years. Fast somatic growth, resistance to parasites, effective anti-predator defences and an ability to circumvent predator recognition mechanisms by prey have probably contributed to rapid population increases of lionfish in the invaded range. However, evidence that lionfish are strong competitors is still ambiguous, in part because demonstrating competition is challenging. Geographic spread has likely been facilitated by the remarkable capacity of lionfish for prolonged fasting in combination with other broad physiological tolerances. Lionfish have had a large detrimental effect on native reef-fish populations in the northern part of the invaded range, but similar effects have yet to be seen in the southern Caribbean. Most other envisaged direct and indirect consequences of lionfish predation and competition, even those that might have been expected to occur rapidly, such as shifts in benthic composition, have yet to be realized. Lionfish populations in some of the first areas invaded have started to decline, perhaps as a result of resource depletion or ongoing fishing and culling, so there is hope that these areas have already experienced the worst of the invasion. In closing, we place lionfish in a broader context and argue that it can serve as a new model to test some fundamental questions in invasion ecology.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".