Foraging behaviour and ecology of transient killer whales within a deep submarine canyon system
Bibliographic record
Abstract
Transient killer whales have been documented hunting marine mammals across a variety of habitats. However, relatively little has been reported about their predatory behaviours near deep submarine canyons and oceanic environments. We used a long-term database of sightings and encounters with these predators in and around the Monterey Submarine Canyon, California to describe foraging behaviour, diet, seasonal occurrence, and habitat use patterns. Transient killer whales belonging to the outer coast subpopulation were observed within the study area 261 times from 2006-2021. Occurrences, behaviours, and group sizes all varied seasonally, with more encounters occurring in the spring as grey whales migrated northward from their breeding and calving lagoons in Mexico (March-May). Groups of killer whales foraged exclusively in open water, with individuals within the groups following the contours of the submarine canyon as they searched for prey. Focal follows revealed that killer whales spent 51% of their time searching for prey (26% of their time along the shelf-break and upper slope of the canyon, and 25% in open water). The remainder of their time was spent pursuing prey (10%), feeding (23%), travelling (9%), socializing (6%), and resting (1%). Prey species during 87 observed predation events included California sea lions, grey whale calves, northern elephant seals, minke whales, common dolphins, Pacific white-sided dolphins, Dall's porpoise, harbour porpoise, harbour seals, and sea birds. The calculated kill rates (based on 270 hours of observing 50 predation events) were 0.26 California sea lions per killer whale over 24 hours, 0.11 grey whale calves, and 0.15 for all remaining prey species combined. These behavioural observations provide insights into predator-prey interactions among apex predators over submarine canyons and deep pelagic environments.
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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 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".