Habitat geography around Hawaii’s oceanic islands influences tiger shark (Galeocerdo cuvier) spatial behaviour and shark bite risk at ocean recreation sites
Bibliographic record
Abstract
We compared tiger shark (Galeocerdo cuvier) spatial behaviour among 4 Hawaiian Islands to evaluate whether local patterns of movement could explain higher numbers of shark bites seen around Maui than other islands. Our sample consisted of 96 electronically-tagged (satellite and acoustic transmitters) tiger sharks, individually tracked for up to 6 years. Most individuals showed fidelity to a specific 'home' island, but also swam between islands and sometimes ranged far (up to 1,400 km) offshore. Movements were primarily oriented to insular shelf habitat (0-200 m depth) in coastal waters, and individual sharks utilized core-structured home ranges within this habitat. Core utilization areas of large tiger sharks were closer to high-use ocean recreation sites around Maui, than around Oahu. Tiger sharks routinely visited shallow ocean recreation sites around Maui and were detected on more days overall at ocean recreation sites around Maui (62-80%) than Oahu (<6%). Overall, our results suggest the extensive insular shelf surrounding Maui supports a fairly resident population of tiger sharks and also attracts visiting tiger sharks from elsewhere in Hawaii. Collectively these natural, habitat-driven spatial patterns may in-part explain why Maui has historically had more shark bites than other Hawaiian Islands.
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 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 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".