Sustained disruption of narwhal habitat use and behavior in the presence of Arctic killer whales
Bibliographic record
Abstract
Although predators influence behavior of prey, analyses of electronic tracking data in marine environments rarely consider how predators affect the behavior of tracked animals. We collected an unprecedented dataset by synchronously tracking predator (killer whales, [Formula: see text] = 1; representing a family group) and prey (narwhal, [Formula: see text] = 7) via satellite telemetry in Admiralty Inlet, a large fjord in the Eastern Canadian Arctic. Analyzing the movement data with a switching-state space model and a series of mixed effects models, we show that the presence of killer whales strongly alters the behavior and distribution of narwhal. When killer whales were present (within about 100 km), narwhal moved closer to shore, where they were presumably less vulnerable. Under predation threat, narwhal movement patterns were more likely to be transiting, whereas in the absence of threat, more likely resident. Effects extended beyond discrete predatory events and persisted steadily for 10 d, the duration that killer whales remained in Admiralty Inlet. Our findings have two key consequences. First, given current reductions in sea ice and increases in Arctic killer whale sightings, killer whales have the potential to reshape Arctic marine mammal distributions and behavior. Second and of more general importance, predators have the potential to strongly affect movement behavior of tracked marine animals. Understanding predator effects may be as or more important than relating movement behavior to resource distribution or bottom-up drivers traditionally included in analyses of marine animal tracking data.
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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 | 0.002 |
| 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 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".