Cool runnings: behavioural plasticity and the realised thermal niche of basking sharks
Bibliographic record
Abstract
Abstract Long-distance migrations by marine vertebrates are often triggered by pronounced environmental cues. For the endangered basking shark (Cetorhinus maximus), seasonal changes in water temperature are frequently proposed as a cue for aggregation within (and dispersal from) coastal hotspots. The inference is that such movements reflect year-round occupancy within a given thermal ‘envelope’. However, the marked variance in timing, direction and depth of dispersal movements hint at a more nuanced explanation for basking sharks. Here, using data from pop-off archival transmitters deployed on individuals in Irish waters, we explored whether autumnal decreases in water temperature triggered departure from coastal habitats and how depth and location shaped the sharks’ realised thermal environment over time. Temperature was not an apparent driver of dispersal from coastal seas, and variance in daily temperature ranges reflected occupancy of different habitats; coastal mixed/stratified and offshore subtropical/tropical waters. Furthermore, individuals that moved offshore and into more southern latitudes off Africa, exhibited a distinct daily cycle of deep dives (00:00–12:00, 200 m–700 m; 12:00–00:00, 0–300 m), experiencing a more extreme range of temperatures (6.8–27.4 °C), including cooler minimum temperatures, than those remaining in European coastal habitat (9.2–17.6 °C). Collectively, these findings challenge the supposition that temperature serves as a universal driver of seasonal dispersal from coastal seas and prompts further studies of deep-water forays in offshore areas.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".