Implications of wind and vessel noise on the sound fields experienced by southern resident killer whales Orcinus orca in the Salish Sea
Bibliographic record
Abstract
The soundscape of critical habitat for southern resident killer whale (SRKW) Orcinus orca in the Salish Sea, the waters around southern British Columbia, Canada, and northern Washington State, USA, is shaped by wind and wave noise as well as heavy commercial and recreational vessel traffic loads. First, we used recordings from 6 passive acoustic moorings to characterize the acoustic landscape experienced by SRKW in this region, focusing on the frequencies used for communication and echolocation. Mid-frequency wind noise was prevalent in winter sound fields, whereas higher-frequency noise levels associated with increased numbers of recreational vessels increased during summer. Commercial vessel presence was consistent, with acoustic inputs prevalent in the western part of the study area. The potential implications of these additions on SRKW acoustics use were then explored for the frequency band 1-40 kHz to represent communication calls and at 50 kHz to consider echolocation. The inputs of wind were extrapolated from modelled hourly wind speed measures and commercial shipping noise. The noise impact was expressed as a proportional reduction of communication and echolocation extent compared to maximum acoustic ranges at ‘minimum ambient’ levels, void of vessel and abiotic noise. The reductions calculated were substantial, with the presence and impact of vessel noise greater than wind-derived additions and the greatest impacts around shipping lanes. Impacts were found for SRKW foraging areas, with implications for group cohesion and feeding success. This interpretation of the influence of natural and vessel noise more clearly demonstrates the potential implications of altered soundscapes for SRKW.
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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 | 0.001 |
| 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".