Multi‐temporal factors influence predation for polar bears in a changing climate
Bibliographic record
Abstract
Predation is an ecological interaction influenced by abiotic and biotic factors acting on multiple temporal scales, yet multi‐temporal comparisons are rare in empirical studies. For polar bears Ursus maritimus , the physical configuration of the habitat and conditions in which seals are hunted may change on intra‐ and inter‐seasonal scales. Additionally, while the effects of climate change on polar bears have focused on linking reductions in sea ice to body condition and survival, the potential changes to on‐ice hunting conditions have not been examined. Employing observational counts of seals killed by polar bears between early‐April and late‐May 1985–2011 (n = 650), we modelled the likelihood of predation events in the Beaufort Sea, Canada at multi‐temporal scales. We used the top model to estimate the expected kill rate of seals in the springs of 1985–1986 and 2005–2006 and integrated the result with fasting rates derived from physiological markers in blood samples. A log‐likelihood ratio test suggested a multi‐temporal approach fit the seal kill data better than any single scale alone. Predation events were influenced by ringed seal Pusa hispida reproduction and haul‐out behaviour, regional sea ice concentration and the phase of climatic indices. The expected kill rate from the top predation model and the estimated mean biomass of seal kills were significant predictors of polar bear fasting rates. Results suggest that 50% less seal biomass was killed in 2005–2006 than in 1985–1986, which correlates with a significant increase in the frequency of polar bears in a fasting state. We propose that the documented changes in polar bear fasting rates between 1985–1986 and 2005–2006 are due to a complex set of abiotic and biotic factors including underlying prey dynamics, rather than a single‐scale environmental correlation.
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 | 0.000 | 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".