Depredation rates and spatial overlap between Great Hammerheads and Tarpon in a recreational fishing hot spot
Bibliographic record
Abstract
Abstract Objective Shark depredation, the full or partial consumption of a hooked fish by a shark before it is landed, is an increasing source of human–wildlife conflict in recreational fisheries. Reports of shark depredation in the catch-and-release Tarpon (also known as Atlantic Tarpon) Megalops atlanticus fishery in the Florida Keys are increasing, specifically in Bahia Honda, a recreational fishing hot spot and a putative Tarpon prespawning aggregation site. Methods Using visual surveys of fishing in Bahia Honda, we quantified depredation rates and drivers of depredation. With acoustic telemetry, we simultaneously tracked 51 Tarpon and 14 Great Hammerheads (also known as Great Hammerhead Sharks) Sphyrna mokarran, the most common shark to depredate Tarpon, to quantify residency and spatial overlap in Bahia Honda. Result During the visual survey, 394 Tarpon were hooked. The combined observed shark depredation and immediate postrelease predation rate was 15.3% for Tarpon that were fought longer than 5 min. Survival analysis and decision trees showed that depredation risk was highest in the first 5–12 min of the fight and on the outgoing current. During the spawning season, Great Hammerheads shifted their space use in Bahia Honda to overlap with Tarpon core use areas. Great Hammerheads restricted their space use on the outgoing current when compared to the incoming current, which could drive increased shark–angler interactions. Conclusion Bahia Honda has clear ecological importance for both Tarpon and Great Hammerheads as a prespawning aggregation and feeding ground. The observed depredation mortality and postrelease predation mortality raise conservation concerns for the fishery. Efforts to educate anglers to improve best practices, including reducing fight times and ending a fight prematurely when sharks are present, will be essential to increase Tarpon survival and reduce shark–angler conflict.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".