Killer whale ( <i>Orcinus orca</i> ) predation in a multi‐prey system
Bibliographic record
Abstract
Abstract Predation can regulate prey numbers but predator behaviour in multiple‐prey systems can complicate understanding of control mechanisms. We investigate killer whale ( Orcinus orca ) predation in an ocean system where multiple marine mammal prey coexist. Using stochastic models with Monte‐Carlo simulations, we test the most likely outcome of predator selection and compare scenarios where killer whales: (1) focus predation on larger prey which presumably offer more energy per effort, (2) generalize by feeding on prey as encountered during searches, or (3) follow a mixed foraging strategy based on a combination of encounter rate and prey size selection. We test alternative relationships within the Hudson Bay geographic region, where evidence suggests killer whales seasonally concentrate feeding activities on the large‐bodied bowhead whale ( Balaena mysticetus ). However, model results indicate that killer whales do not show strong prey specialization and instead alternatively feed on narwhal ( Monodon monoceros ) and beluga ( Delphinapterus leucas ) whales early and late in the ice‐free season. Evidence does support the conjecture that during the peak of the open water season, killer whale predation can differ regionally and feeding techniques can focus on bowhead whale prey. The mixed foraging strategy used by killer whales includes seasonal predator specialization and has management and conservation significance since killer whale predation may not be constrained by a regulatory functional response.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".