Highly variable basking shark (Cetorhinus maximus) diving behavior in the lower Bay of Fundy, Canada
Bibliographic record
Abstract
Diving behavior in basking sharks, the largest obligate ram filter feeding planktivore, is highly dependent on their location. In the Bay of Fundy, where basking sharks congregate in the boreal summer and autumn, the sharks’ copepod prey are located deep in the water column, below 100 m, in dense but scattered patches. We used time-depth recorders to examine how the vertical movements of basking sharks adapt to such a prey field and captured 4,138 hours of diving behavior from 42 sharks in the boreal summer from 2008 to 2020. Using finite mixture models, we split the time series into surface and subsurface movement blocks and used dynamic time-warping to cluster subsurface movements into seven modes based on their shapes and lengths, with mostly V-shaped subsurface movements (85%) and a minority that were U-shaped (14%). Across sharks, five overall strategies of vertical movement behavior were identified. The strategies split broadly by the ratio of V-shaped movements to U-shaped movements in a deployment and whether the majority of subsurface movements were above or below 100 m. A majority of basking sharks (64%) were reverse diel vertical migrators but none altered their time-allocation across tidal periods. During more thermal stratification, sharks dove deeper, longer, and less frequently while during less thermal stratification sharks dove shallower, shorter, and more frequently. Overall, we show that basking sharks exhibit considerable inter- and intra-individual variability in their diving behavior, and therefore presumably also in foraging modes. Some of this variability relates to time of year and tidal phase, unsurprising in this highly tidally-driven system; however, the majority of the variability remains unexplained without more information on the distribution, composition, and abundance of the copepod prey field. The technique presented is extendable to other species and, unlike many dive classification techniques, requires few subjective delineations of diving behavior.
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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 | 0.001 | 0.002 |
| 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 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".