A review of aerial survey density estimates of bearded seals (Erignathus barbatus) in the Canadian Arctic highlights important knowledge gaps and research needs
Bibliographic record
Abstract
Abstract There has been significant sea ice loss in the Arctic as a result of climate change, with measurable impacts on available habitat for ice-obligate marine mammals. Bearded seals ( Erignathus barbatus ) have a circumpolar distribution and primarily inhabit coastal areas of shallower depths with seasonal pack ice cover. However, there is limited information and no formal estimates on population abundance and densities of bearded seals within Canada. Here, we review historic field reports and publications from aerial surveys conducted from 1974 to 2022 to compile the density and abundance estimates of bearded seals across Canadian waters. Aerial surveys for marine mammals have been flown in several areas across the Canadian Arctic, including the Beaufort Sea (1974–2006), Canadian High Arctic (1979–2022), Baffin Bay-Davis Strait (1979–2014), and the Hudson Complex (1994–2017). After reviewing all published data sources, we report that the density of bearded seals in Canada were highest (and highly variable) in the Beaufort Sea (0.01–8.68 seals*km −2 ), then in Baffin Bay-Davis Strait (0.004–8.3 seals*km −2 ), and lowest and less variable inter-annually in the Hudson Complex (0.02 to 0.12 seals*km −2 ) at the time of these surveys. We also determined that bearded seals are more often found in areas of patchy ice cover (50–75%) with shallow water depths ≤ 500 m. Further, this review identifies regions within the Canadian Arctic and sub-Arctic that require updated aerial survey information. Quantifying the abundance and density estimates of bearded seals in Canada is essential for monitoring population status over time to better understand how this species is responding to environmental variation from anthropogenic activity and climate change.
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 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 | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".