Marine protected areas can increase the abundance of invasive lionfish ( <i>Pterois miles</i> )
Bibliographic record
Abstract
Abstract Marine protected areas (MPAs) can protect and restore marine biodiversity and fisheries, but there are concerns that they may also benefit invasive species. The spatial and temporal colonization of invasive lionfish ( Pterois miles ) in the eastern Mediterranean was compared across zones with varying fishing restrictions (no fishing, recreational and commercial fishing, and commercial fishing only), and stations where targeted removal events were conducted by volunteer SCUBA divers. Lionfish density in no fishing areas was nearly double that of areas with commercial fishing only, and over four times greater than in areas where both commercial and recreational fishing were allowed. Lionfish density increased with depth, possibly due to easier human exploitation in shallow waters (0–10 m) that are accessible to recreational spearfishers. Targeted removals by volunteer divers decreased lionfish densities by over 60%, while areas without removals had a 200%–400% increase. Along with management actions, natural and ecological processes might drive lionfish densities within MPAs, and the speed with which lionfish colonized fishery‐restricted zones, emphasized the need for a more sophisticated MPA management strategy that considers invasive species impacts and dynamics in an ecosystem‐based approach.
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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".