Surface behaviors correlate with prey abundance and vessels in an endangered killer whale (<i>Orcinus orca</i>) population
Bibliographic record
Abstract
Abstract Southern Resident killer whales (SRKWs) (Orcinus orca) are an endangered population in the United States and Canada, partly due to declines of their primary prey species, Chinook salmon. Prey availability influences various aspects of SRKW behavior, including distribution patterns and social structure. Yet, it is unclear to what extent a limited prey source influences the frequency of surface‐active behaviors (SABs), behaviors with important ecological implications. Here, we used long‐term datasets (1996–2019) to examine the relationships between the abundance of Chinook salmon, vessel presence, and the frequency with which SRKWs perform SABs. Salmon abundance was a significant predictor of SAB frequency, with fewer SABs performed in times of lower salmon abundance. SRKWs displayed more SABs when more whale watching vessels were present, and the whales spent a greater amount of time in the study area, performing more milling as opposed to traveling behavior, when vessel numbers were higher. Lastly, we found pod‐specific differences, such that K pod displayed significantly fewer SABs than either J or L pods. The observed relationships between SRKW behavior and both salmon abundance and vessel presence have implications for social network cohesion and foraging success. Our study adds to a growing body of literature highlighting factors affecting SRKW behavior as they experience increased threats from decreased prey availability, habitat loss, and anthropogenic disturbance, with implications for trans‐boundary management and conservation efforts.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".