Effects of vessel noise on beluga (<i>Delphinapterus leucas</i>) call type use: ultrasonic communication as an adaptation to noisy environments?
Bibliographic record
Abstract
Animal vocalizations can evolve structural features as long-term adaptations to noisy environments. Using such signals, cetaceans could mitigate masking from vessel noise. This study investigates whether beluga whales (Delphinapterus leucas) use ultrasonic high-frequency burst pulse (HFBP) calls to communicate in noisy conditions. We identified HFBP calls in three populations: St Lawrence Estuary, Eastern High Arctic-Baffin Bay, and Western Hudson Bay. Focusing on the industrialized St Lawrence, we investigated the effects of vessel noise on HFBP call rates compared to other call types. Ultrasonic calls, spanning a bandwidth of 36.4±6.5 to 144 kHz (Nyquist frequency), comprised 13% of the St Lawrence beluga repertoire (n=25,435). Noise events (n=21) were defined as periods when at least one vessel was visible within 2 km of the hydrophone while belugas were within 500 m. Sound pressure levels were measured before, during, and after exposure. Generalized linear mixed models revealed consistent HFBP call rates before, during, and after vessel noise exposure, while contact calls and other call types declined during exposure (n=4528). These findings suggest that ultrasonic signals that evolved in the Arctic - where ice-associated noise may have created a need for high-frequency communication - remain a viable communication channel in vessel noise, allowing belugas to exploit these signals to maintain communication. Understanding how belugas use signals in noisy environments can inform conservation strategies for noise-impacted marine mammals.
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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 | 0.000 | 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".