Polar bear predatory behaviour reveals seascape distribution of ringed seal lairs
Bibliographic record
Abstract
Abstract Ringed seal (Pusa hispida) breeding distribution has been extensively studied across near‐shore habitats, but has received limited attention at a seascape scale due to the difficulty in accessing offshore sea ice environments. Employing highly visible predation attempts by polar bears (Ursus maritimus) on ringed seals in subnivean lairs observed by helicopter, the spatial relationship between predatory behaviour and ringed seal breeding habitat was examined. Resource selection functions were used to determine the relative probability of predation attempts on ringed seals in lairs as a function of habitat during a period of low ringed seal natality (2004–2006). Ringed seal pup kill locations were compared between years of low (2003–2006) and high (2007–2011) natality to assess the effect of reproductive output on habitat use. During years of low natality, polar bear hunting attempts were more likely in near‐shore fast ice, and pup kills were observed predominately in fast ice (fast = 65 %, pack = 29 %, P = 0.002) at a median distance of 36 km from shore. In years of high natality, pup kills were observed farther from shore (median = 46 km, P = 0.03), and there was no difference in the proportion of observations in fast ice and pack ice (fast = 43 %, pack = 52 %, P = 0.29). These results suggest that the facultative use of adjacent offshore pack ice by breeding ringed seals may be influenced by natality. This study illustrates how documenting the behaviour of a predator can facilitate insight into the distribution of a cryptic prey.
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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) | 0.002 | 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".