Measuring the Factors Influencing Narwhal (Monodon monoceros) Presence in Eclipse Sound, Nunavut with Passive Acoustic Monitoring
Bibliographic record
Abstract
Marine animal behavioral responses to anthropogenic noise present concerns for resource managers in regions such as the Canadian Arctic, where communities also rely on marine wildlife for subsistence hunting. In these areas, the potential for displacement of animals due to noise disturbance has become an important factor in management and decision-making processes. Many factors play a role in the movements and behavior of marine animals, including the influence of environmental variables like light and temperature. To determine the impacts of an added anthropogenic factor, such as underwater noise from shipping, there is a need to improve understanding of natural behavior, relationships with habitat, and responses to stressors to inform conservation and resource management. Narwhals, (Monodon monoceros) may be more sensitive to disturbances than other cetacean species due to their strong site fidelity and close association with the sea ice. Understanding their relationships with habitat and responses to added stressors is necessary for effective management and conservation efforts. Sea ice conditions and the darkness of winter make it challenging to observe these species using visual approaches. Long-term Passive Acoustic Monitoring (PAM) offers an effective means to study narwhal behaviors and their responses to environmental changes locally. In this study, I utilized PAM to establish a baseline of narwhal behavior in the Eclipse Sound region of the Canadian Arctic between 2016-2021. My research was conducted in three phases: first, quantifying the seasonal acoustic presence of narwhals; second, analyzing ship traffic patterns; and third, investigating the preliminary relationships between narwhal acoustic presence and vessel traffic. By focusing on narwhal echolocation, I examined patterns of daily presence in relation to key environmental factors such as sea ice concentration, calendar year, and time of year. Narwhal echolocation clicks were detected in all years, mainly during transitional periods of sea ice melt and formation. The calendar year, day of year, and daily sea ice cover were significant factors in predicting narwhal presence. Shipping traffic patterns indicate minimal overlap between narwhal and ships in late spring and early summer, when animals are present at the floe edge in the area. Instances of ships in proximity to the recording location when narwhals were present were most common in late summer and early fall, just before and during sea ice formation. Preliminary investigation of narwhal echolocation presence with ships within a distance of 40 km suggests that acoustic detections of narwhal decrease substantially as ships approach. The study highlights the strongly seasonal migratory behavior of narwhals as they enter and exit an important summering area and sets a foundation for future studies on the impacts of anthropogenic noise on marine mammals in the Arctic.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".