Colonization, diversity, and seasonality of fishes at pelagic fish aggregating devices
Bibliographic record
Abstract
The pelagic zone of the ocean can be a challenging environment in which to conduct research and as a result we lack the robust baseline abundance and diversity data, compared to what is available in more accessible coastal habitats, to be able to track changes or stressors to the biota in this environment. Many large-scale fisheries target pelagic fish, and much of the information available on these species is based on fisheries-dependent data that may be biased towards hotspots and commercially valuable fishes. Here, a long-term video and visual fish survey was conducted on two subsurface moored fish aggregating devices (FADs) in the pelagic waters of the central Bahamas to determine the feasibility of using moored pelagic FADs as tools for collecting fish abundance and diversity data. A wide range of species was documented, including large migratory fish that are the focus of commercial and recreational fisheries, and smaller often overlooked species on which little abundance or seasonality information exists. We found that FADs colonize quickly and reach a peak stable (albeit seasonally cyclical) abundance and diversity within the first several months after deployment. Species richness was higher in video surveys, but abundance was higher in visual surveys, except for sharks. Our results highlight the need to tailor survey methods to fit the context and study objective, and provide further evidence for the importance of fisheries-independent data in monitoring pelagic species.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".