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Record W6997355047

Vocal communication, social structure, and responses to disturbance in Belugas (Delphinapterus leucas)

2025· dissertation· en· W6997355047 on OpenAlexaboutno aff

Bibliographic record

VenueScholarship at UWindsor (University of Windsor) · 2025
Typedissertation
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsnot available
Fundersnot available
KeywordsBelugaBeluga WhaleSocialityDisturbance (geology)Endangered speciesRange (aeronautics)Active listening
DOInot available

Abstract

fetched live from OpenAlex

Much can be learned by carefully listening to animals. This is particularly true for animals that produce complex vocalizations that vary between individuals or social groups. By examining the contexts in which these sounds are produced and variation in the acoustic features across locations, we can better understand how animals experience the world and how human activities impact their lives. Belugas, Delphinapterus leucas, one of the most loquacious cetaceans, produce complex contact calls that provide a key opportunity for such insights. The goal of my dissertation was to investigate the vocal and social behaviour of belugas, as well as the impacts of human activities on their behaviour. This dissertation includes five data chapters (Chapters 2-6). In Chapter 2, I reviewed the social structure of belugas and examine evidence of culture in beluga populations. Evidence indicates that beluga societies tend to be structured by atomistic, individual-based fission-fusion dynamics and that most beluga societies are sexually-segregated, with male groups often forming close, long-lasting associations. Growing evidence suggests that female beluga sociality may be best defined as “matrifocal” rather than “matrilineal”. I discussed the strong evidence for migratory culture in belugas and emphasized a need for further research on vocal culture. In Chapter 3, I examined the impacts of unoccupied aerial vehicles (i.e., drones) on endangered St. Lawrence belugas across a range of different conditions. I found that belugas were most likely to show evasive reactions during low-altitude drone flights, particularly below 23m. My findings indicated that evasive reactions are particularly likely during initial approaches to a group of whales, and during flights over larger groups. I reviewed drone studies of cetaceans to identify altitude thresholds linked to disturbance, and found that reactions to drones were most common at flight altitudes below 30 m. I formulated seven recommendations for researchers using drones to study cetaceans. In Chapter 4, I examined the contexts in which free-living St. Lawrence belugas produced complex contact calls. I showed that contact call production changed across different socio-behavioural contexts. My results showed that contact call production increased in large herds, in herds engaged in milling or multidirectional behaviour, and in herds with an intermediate level of dispersion. Notably, contact call production decreased as large vessel traffic increased, highlighting the impact of anthropogenic disturbance on beluga vocal behaviour. In Chapter 5, I used passive acoustic monitoring of distinctive contact calls to quantify the spatial structure of the St. Lawrence beluga population. Using a mark-recapture-based analysis of beluga complex contact calls over a five-year period, I identified two distinctive communities, one centered on the Saguenay Fjord (the Saguenay community), and one centered on the south shore of the Upper Estuary (the South Shore community). This finding suggests that the Saguenay-St-Lawrence Marine Park should be expanded to include the ranges of both communities to better protect the St. Lawrence beluga population. In Chapter 6, I examined vocal variation between the contact calls of the two communities and suggested that these differences represent community-specific vocal dialects, although further research is needed to support this conclusion. My findings showed that the signature elements of calls from the South Shore community tended to be more frequency-modulated, covered a narrower range of frequencies, and peaked at higher frequencies than those from the Saguenay community. However, Saguenay community calls exhibited greater energy in higher frequencies than calls from the South Shore community. Some of these differences may reflect adaptations to the acoustic environment of each community. Collectively, my research highlights the vocal, social, and cultural complexity of belugas and underlines the impacts of humans on their behaviour.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.019
GPT teacher head0.252
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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