Oceanic Diel Vertical Movement Patterns of Blue Sharks Vary With Water Temperature and Productivity to Change Vulnerability to Fishing
Bibliographic record
Abstract
In the pelagic environment diel vertical movements (DVM) are widespread across taxa, from zooplankton ascending from day-time depths into surface layers at night to avoid visual predators, to apex predators following prey movements to maximise foraging opportunities. The drivers of DVM in large predators such as pelagic sharks have only recently begun to be investigated in detail with the advent of sophisticated archival tags and high-resolution oceanographic datasets. In this study, we satellite tagged adult [>180 cm fork length, (FL)] blue sharks ( Prionace glauca ) in the North Atlantic Ocean to examine behavioural changes in response to the encountered environment, and therefore, to determine potential risks of capture using pelagic longline fisheries data. Although blue sharks recurrently use surface waters, cyclic diel behaviours were observed, with >95% of night-time spent above 250 m depth and variable day-time depth use. Hence, three different diel behaviours were identified during the tracking period: (i) regular normal DVM (nDVM) (dawn descent – dusk ascent, with over 90% of nighttime spent above 250 m, and between 5 and 50% of the day below this threshold); (ii) surface-oriented behaviour (occupation of surface waters both day and night), and (iii) deep depth-oriented nDVM [dawn descent – dusk ascent, with the majority (>50%) of daytime spent at depth]. Importantly, diel behaviours generally occurred in different ocean regions with nDVM frequently observed in high latitudes, associated with cold, highly productive waters (e.g., North Atlantic Current/Labrador Current convergence zone, West African upwelling area), while depth-oriented nDVM was observed in warm, oligotrophic areas. Thus, day-time occupation of shallow waters significantly increased with lower water temperature at depth (100 m), and with increasing concentration (and decreasing depth) of the chlorophyll a maximum. During nights of full moon blue sharks spent significantly more time in the depth range of longline hooks, while fishing effort and catches were also higher. We demonstrate that increased occupancy of surface layers driven by highly productive, cold waters and greater lunar illumination lead to higher capture risk. Understanding habitat-specific vulnerability to fishing in a commercially important pelagic shark species is essential for improving management and conservation measures.
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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 | 0.001 | 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 | 0.002 |
| 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".