Behavioural analysis of multi-year satellite telemetry data provides insight into narwhal (Monodon monoceros) winter prey selection in Baffin Bay
Bibliographic record
Abstract
Narwhals (Monodon monoceros) are deep-diving Arctic cetaceans that migrate seasonally between summering and wintering grounds. The Baffin Bay population overwinters in southern Baffin Bay and Davis Strait, where they are known to forage on high-energy benthic prey. Studying narwhal winter behaviour and prey preference has been challenged by their remote distribution and limited lifespan of satellite tags deployed in summer, restricting data on their habitat use and foraging strategies. Since prey consumption is thought to peak in the winter, understanding narwhal diet plasticity in a rapidly changing environment like Baffin Bay is critical. This study developed unique methods to examine four years of irregular satellite telemetry data from 22 narwhals tagged in their summering grounds. Locations and recorded diving data from the overwintering area were isolated, and a hidden Markov model was used to define three behaviours ("surface", "pelagic", and "deep-water" diving). We further examined the effects of five covariates on these behaviours to provide insight into the spatial patterns of narwhal winter prey preference. Narwhal behaviours were dominated by diving, with 37% of their time spent in pelagic waters and 40% in deep-water, while only 22% of their time was spent in surface related behaviours. Deep-water behaviours increased later in the day and into the winter season and occurred frequently in the center trough of Baffin Bay before (66°- 69°) and across Davis Strait (65° - 67°). In contrast, pelagic behaviours declined as the winter season progressed and occurred earlier in the day. Narwhals occupying the northern overwintering area exhibited more pelagic behaviours, despite it being deeper, suggesting different foraging strategies across their winter range. Our study identified behaviours suggestive of a variable winter diet and provided insight on the spatial nature of these behaviours across the winter season. The methods developed in this study present new opportunities for analysing lower resolution satellite tracking data. With advancements in bio-logging technology and remote field methods, the ability to successfully document changes in winter space use and fine-scale foraging behaviours may be possible for narwhal 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".