Decadal shifts in autumn migration timing by Pacific Arctic beluga whales are related to delayed annual sea ice formation
Bibliographic record
Abstract
Abstract Migrations are often influenced by seasonal environmental gradients that are increasingly being altered by climate change. The consequences of rapid changes in Arctic sea ice have the potential to affect migrations of a number of marine species whose timing is temporally matched to seasonal sea ice cover. This topic has not been investigated for Pacific Arctic beluga whales ( Delphinapterus leucas ) that follow matrilineally maintained autumn migrations in the waters around Alaska and Russia. For the sympatric Eastern Chukchi Sea (‘Chukchi’) and Eastern Beaufort Sea (‘Beaufort’) beluga populations, we examined changes in autumn migration timing as related to delayed regional sea ice freeze‐up since the 1990s, using two independent data sources (satellite telemetry data and passive acoustics) for both populations. We compared dates of migration between ‘early’ (1993–2002) and ‘late’ (2004–2012) tagging periods. During the late tagging period, Chukchi belugas had significantly delayed migrations (by 2 to >4 weeks, depending on location) from the Beaufort and Chukchi seas. Spatial analyses also revealed that departure from Beaufort Sea foraging regions by Chukchi whales was postponed in the late period. Chukchi beluga autumn migration timing occurred significantly later as regional sea ice freeze‐up timing became later in the Beaufort, Chukchi, and Bering seas. In contrast, Beaufort belugas did not shift migration timing between periods, nor was migration timing related to freeze‐up timing, other than for southward migration at the Bering Strait. Passive acoustic data from 2008 to 2014 provided independent and supplementary support for delayed migration from the Beaufort Sea (4 day yr −1 ) by Chukchi belugas. Here, we report the first phenological study examining beluga whale migrations within the context of their rapidly transforming Pacific Arctic ecosystem, suggesting flexible responses that may enable their persistence yet also complicate predictions of how belugas may fare in the future.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".