Low salinity negatively affects metabolic rate, food consumption, digestion and growth in invasive lionfish Pterois spp.
Bibliographic record
Abstract
The establishment of the piscivorous lionfishPteroisspp. in the Western Atlantic and wider Caribbean is a well-documented example of a successful marine invasion. Recently, lionfish have been shown to colonise a wide range of ecosystems and tolerate a wider range of salinities than previously thought. In the present study, lionfish were maintained in aquaria under differing salinity treatments (10, 20 and 37 psu) similar to those they might experience in an estuarine ecosystem. The effects of long-term hyposaline exposure on growth, metabolic rate, maximum food consumption and digestion were examined. Consistent with previous studies, lionfish were able to survive in hyposaline conditions for extended periods of time. However, lionfish in the most hyposaline treatment (10 psu) exhibited reduced growth under low food conditions, lower maximum metabolic rate, lower aerobic scope, lower maximum food consumption, took longer to digest a standardized meal size and occupied a greater percentage of their aerobic scope during digestion. Results suggest that (1) given the ability of lionfish to tolerate low salinity, updated range expansion models should incorporate salinity data to improve accuracy of predicted range expansion and (2) the invasion of lionfish into low salinity ecosystems, although a serious concern, will not likely lead to the same level of population increase observed for coral reef habitats due to the physiological costs associated with living in low salinities.
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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 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".