High predatory efficiency and abundance drive expected ecological impacts of a marine invasive fish
Bibliographic record
Abstract
The ecological impacts of invasive species are highly variable and mediated by many factors, including both habitat and population abundance. LionfishPterois volitansare an invasive marine species which have high reported detrimental effects on prey populations, but whose effects relative to native predators are currently unknown for the recently colonized eastern Gulf of Mexico. We used functional response (FR) methodology to assess the ecological impact of lionfish relative to 2 functionally similar native species (red grouperEpinephelus morioand graysby grouperCephalopholis cruentata) foraging in a heterogeneous environment. We then combined theper capitaimpact of each species with their field abundance to obtain a Relative Impact Potential (RIP). RIP assesses the broader ecological impact of invasive relative to native predators, the magnitude of which predicts community-level negative effects of invasive species. Lionfish FR and overall consumption rate was intermediate to that of red grouper (higher) and graysby grouper (lower). However, lionfish had the highest capture efficiency of all species, which was invariant of habitat. Much higher field abundance of lionfish resulted in high RIPs relative to both grouper species, demonstrating that the ecological impact of lionfish in this region will be driven mainly by high abundance and high predator efficiency rather thanper capitaeffect. Our comparative study is the first empirical assessment of lionfishper capitaimpact and RIP in this region and is one of few such studies to quantify the FR of a marine predator.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".