Predation by Invasive Indo-Pacific Lionfish on Atlantic Coral Reef Fishes: Patterns, Processes, and Consequences
Bibliographic record
Abstract
Invasive species cause significant ecological impacts, ranging from the homogenization and reduction of biodiversity to changes in ecosystem function.In marine systems, where predation is a key force shaping demographic processes, predatory invaders are predicted to have particularly severe effects.My thesis focuses on the patterns, processes and consequences of the recent invasion of Western Atlantic coral reef habitats by predatory Indo-Pacific lionfish (Pterois volitans and P. miles).I first investigate the rate and timing of predation by lionfish through in situ observations on invaded Bahamian coral reefs, and find that lionfish primarily consume prey during crepuscular periods and at higher rates than reported from their native range.Next, I examine trends in the biomass of 42 native fishes found in the stomach contents of lionfish on invaded reefs, and find declines of ~65% in just two years likely owing to lionfish predation.Through field observations of predation and stomach contents analyses, I identify morphological and behavioural drivers of prey selection by lionfish: small size, shallow body depth, demersal habit, and not cleaning all contribute vulnerability to predation.Using these insights, I model the effect of lionfish predation on the biomass of their fish prey, taken as the difference between rates of lionfish prey consumption and prey fish production on invaded reefs, the latter estimated from community size-spectra data using metabolic scaling relationships.My model accurately predicts the magnitude of prey depletion observed on Bahamian reefs, and reveals that lionfish are likely to continue depleting native fishes unless culled by 30-95%.Finally, I conduct a removal experiment on 24 natural patch reefs over 18 months to test the model's predictions, and find that the lionfish density reductions predicted by the model are sufficient arrest native fish biomass declines.My thesis reveals the important role that invasive lionfish now play in structuring Atlantic coral reef fish communities, and quantifies the threat of invasion for the persistence of fish diversity in the region.However, by demonstrating that lionfish removal can limit the severe ecological impacts of this invasive predator, my research offers strategic targets and much needed hope for local marine management action.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".