Migratory patterns and seasonal habitat use of great hammerhead sharks (<i>Sphyrna mokarran</i>) in the southeastern <scp>United States</scp>
Bibliographic record
Abstract
The great hammerhead shark (Sphyrna mokarran) is a highly mobile shark species whose population in the United States declined dramatically through the early 2000s. Their spatial ecology is poorly understood, creating challenges for effective conservation of this enigmatic marine predator. Using acoustic telemetry and network analyses, we describe the movement patterns of 15 mature great hammerhead sharks (207-331.5 cm fork length) primarily within the waters of the southeastern United States from April 2019 to December 2022, including identifying movement corridors, core use areas and seasonal shifts in habitat use. Great hammerheads exhibited partial migration; some sharks undertook consistent, repeatable, round-trip migrations between the tagging site in the Florida Keys and northern points in the Gulf of Mexico and Atlantic coast. Conversely, others remained in the Florida Keys year-round. Network analysis coupled with community detection algorithms revealed that individual great hammerheads displayed oceanic basin affinity, favouring migration to either the Atlantic Ocean or Gulf of Mexico. Great hammerheads that remained in the Florida Keys shifted their habitat use seasonally from inshore channels (spring and summer) to offshore artificial reefs and the natural reef tract (summer through winter). These shifts may be driven by prey availability, as core use areas often corresponded to known locations of seasonal spawning aggregations for reef fish. These data fill an important knowledge gap for great hammerhead migratory patterns that can improve spatial management strategies for this historically overexploited species. We discuss the importance of drawing upon data from multiple tagging locations for highly mobile species management, particularly in the designation of essential fish habitat.
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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 | 0.000 |
| 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 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".