Long‐term satellite tracking reveals region‐specific movements of a large pelagic predator, the shortfin mako shark, in the western North Atlantic Ocean
Bibliographic record
Abstract
Summary As upper level predators, sharks serve an important role in marine ecosystems, but are often at risk from fisheries. Successful management of these species will require detailed information about their movements and distributions. Using satellite telemetry, we investigated the long‐term horizontal movements and seasonal distributions of shortfin mako sharks Isurus oxyrinchus in the western North Atlantic Ocean. Twenty‐six sharks (14 USA, 12 Mexico) were tracked for durations of 78–527 days. Ten sharks were tracked for >1 year. Sharks displayed region‐specific movements, with little distributional overlap between the Gulf of Mexico and Caribbean Sea, and the western North Atlantic. Sharks tagged off the USA ranged over a larger area, including shelf and pelagic habitats. Their core distribution was largely over the continental shelf and fluctuated seasonally, ranging from South Carolina, USA, in the winter to Nova Scotia, Canada, in the autumn, and appeared to follow seasonal productivity peaks while favouring warmer waters. Sharks tagged off Mexico displayed more restricted movements, largely confined to shelf habitats, with core activity centred year‐round on the eastern Campeche Bank, Mexico. Sharks moved across the jurisdictional management boundaries of 17 nations, and the proportion of tracked sharks harvested (22%) was twice that obtained from previous fisheries‐dependent, conventional tagging studies. Sharks also displayed considerable variability in movements, with seven sharks tagged off the USA making long‐distance, highly directional southern excursions into unproductive subtropical/tropical waters before returning north. Policy implications . The large‐scale and region‐specific movements of shortfin mako sharks underscore the need for close cooperation amongst western North Atlantic nations and implementation of regionally and seasonally specific management strategies. The movement patterns also provide baseline information, which could be used in spatially explicit stock assessment models. Identification of high‐use areas by shortfin mako sharks provides focal areas for quantifying interactions with fisheries. The high harvest rate observed in our fisheries‐independent tracking study raises questions about the true rate of fisheries mortality experienced by shortfin mako sharks, calling for a cautionary interpretation of past stock assessments used to determine management policy for this highly migratory species of conservation concern.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".