Spatial and temporal variation in shark communities of the lower Florida Keys and evidence for historical population declines
Bibliographic record
Abstract
Sharks are top predators in many marine ecosystems. Despite recent concerns over declines in shark populations, studies of shark communities in coastal habitats are limited. We used drumlines and longlines to determine shark community composition and habitat affinities in the Florida Keys, USA. Community composition varied among habitats. Catch rates of smaller sharks were highest in protected shallow waters, while large sharks were more abundant in deep channels. Overall probabilities of catching large sharks on drumlines did not vary with water temperature, while catches of small sharks on longlines increased with increasing water temperature. Individual species differed in their responsiveness to variation in water temperatures and habitat. Bait type affected catch rates of some species, suggesting that fishing methods should be considered explicitly in studies describing shark communities or temporal trends in abundance. Catch rates of large-bodied sharks were higher in a remote and protected location compared with similar habitats near inhabited Keys. Also, historical accounts of a shark fishery in the study area during the 1920s suggest substantial declines in large shark abundance and shifts in community composition. By implication, ecosystem impacts of changes in the large shark community may be dramatic and likely occurred before adequate baselines were established.
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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 | 0.001 | 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 | 0.000 |
| 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".