Spatio-Temporal Variability in White Shark (Carcharodon carcharias) Movement Ecology During Residency and Migration Phases in the Western North Atlantic
Bibliographic record
Abstract
Understanding how mobile, marine predators use three-dimensional space over time is central to inform management and conservation actions. Combining tracking technologies can yield powerful datasets over multiple spatio-temporal scales to provide critical information for these purposes. For the white shark ( Carcharodon carcharias ), detailed movement and migration information over ontogeny, including inter- and intra-annual variation in timing of movement phases, is largely unknown in the western North Atlantic (WNA), a relatively understudied area for this species. To address this need, we tracked 48 large juvenile to adult white sharks between 2012 and 2020, using a combination of satellite-linked and acoustic telemetry. Overall, WNA white sharks showed repeatable and predictable patterns in horizontal movements, although there was variation in these movements related to sex and size. While most sharks undertook an annual migratory cycle with the majority of time spent over the continental shelf, some individuals, particularly adult females, made extensive forays into the open ocean as far east as beyond the Mid-Atlantic Ridge. Moreover, increased off-shelf use occurred with body size even though migration and residency phases were conserved. Summer residency areas included coastal Massachusetts and portions of Atlantic Canada, with individuals showing fidelity to specific regions over multiple years. An autumn/winter migration occurred with sharks moving rapidly south to overwintering residency areas in the southeastern United States Atlantic and Gulf of Mexico, where they remained until the following spring/summer. While broad residency and migration periods were consistent, migratory timing varied among years and among individuals within years. White sharks monitored with pop-up satellite-linked archival tags made extensive use of the water column (0–872 m) and experienced a broad range of temperatures (−0.9 – 30.5°C), with evidence for differential vertical use based on migration and residency phases. Overall, results show dynamic inter- and intra-annual three-dimensional patterns of movements conserved within discrete phases. These results demonstrate the value of using multiple tag types to track long-term movements of large mobile species. Our findings expand knowledge of the movements and migration of the WNA white shark population and comprise critically important information to inform sound management strategies for the species.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".