The Efficacy of Management Measures to Reduce Vessel Noise in Critical Habitat of Southern Resident Killer Whales in the Salish Sea
Bibliographic record
Abstract
The inland waters around southern Vancouver Island and northern Washington State, known as the Salish Sea, host critical habitat for endangered southern resident killer whales (SRKW). This is, however, a highly traversed area, with approaches to industrial ports and coastal cities, international shipping lanes, ferry routes, and considerable recreational vessel traffic. Vessel noise is a key threat to SRKW prosperity, and so conservation measures directed to mitigate its effects have been explored annually since 2017. Here, we describe trials undertaken in 2020, which included spatially limited slowdown zones, exclusion areas as Interim whale Sanctuary Zones (ISZs), and a lateral displacement of tug transits to increase the distance between their route and SRKW foraging areas. To assess each of the measures we first considered the level of mariner participation using data from the Automated Identification Systems (AIS), mandatory for commercial vessels. Knowing this, the changes in soundscape were examined, focused on impacts on broadband (10 Hz to 100 kHz) ambient noise and the frequencies used by SRKW for communication (500 Hz to 15 kHz) and echolocation (15 to 100 kHz). A control period of two-months prior to trial initiation was used to quantify the changes. High levels (> 80%) of compliance were found for each measure, except ISZs, where observance was low. Median reduction in speeds ranged from 0.2–3.5 knots. Resulting sound reductions were most notable in the lower frequencies, although reductions were also recorded in SRKW pertinent ranges. Tug displacement also reduced ambient noise in these frequencies, despite making up a small portion of the overall traffic. The management trials were effective in reducing potential impacts singularly and in concert. Greater awareness and stakeholder engagement may increase compliance and, therefore, the efficacy of measures in the future.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".