Behavioural responses to fisheries capture among sharks caught using experimental fishery gear
Bibliographic record
Abstract
The response to capture is important in fisheries because it can reveal potential threats to species beyond fishing mortalities resulting from direct harvest. To date, the vast majority of studies assessing shark stress responses have used physiology or biotelemetry to look at sensitivity after capture, leaving a gap in our understanding of the behaviours of sharks during capture. We examined the behavioural responses of sharks to capture by attaching accelerometers to fishing gear and measuring the immediate and prolonged forces they exerted while on the line. We recorded acceleration vectors and derived the rate of intense fighting behaviours of 23 individual sharks comprising three species. Results suggest that blacktip sharks (Carcharhinus limbatus) exhibited intense bouts of fighting behaviour at the onset of hooking, while nurse (Ginglymostoma cirratum) and tiger sharks (Galeocerdo cuvier) displayed more subdued acceleration values during capture. We also obtained plasma lactate from a subset of individuals and detected a strong correlation with maximum acceleration. These results align with previously published values and suggest that shark movement during fisheries capture is an important factor during bycatch and catch-and-release interactions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".