Monitoring phenology and behavior of polar bears at den emergence using cameras and satellite telemetry
Bibliographic record
Abstract
Abstract Maternal denning plays a vital role in the development and survival of highly altricial polar bear cubs by providing protection from external conditions. The denning period remains challenging to study and monitor because polar bear dens are often remote and difficult to access. Denning is typically inferred from satellite telemetry data, yet the accuracy of these measures in capturing important denning behaviors that are relevant to management and monitoring is unclear. We installed cameras at 13 den sites in Svalbard, Norway, over a 6‐year period, 9 of which yielded observations of behavior and phenology of polar bears at den emergence, and we compared these observations with denning behavior inferred from telemetry data (location, temperature, and activity levels) from satellite collars worn by denning bears. We next developed Bayesian generalized linear models to accurately predict denning behaviors (i.e., observations from cameras) from collar sensor data. From the camera data, mean date of observed den breakout was 9 March (SD = ±6.5 days, n = 7) and departure from the den site occurred 12.1 days later (±10.1 days, n = 7). Estimates of den breakout date based on joint analysis of collar temperature and activity data indicated breakout occurred on average 0.7 days later (±11.4 days, n = 7) and estimates based on collar temperature thresholds alone indicated breakout occurred 4.0 days later (±6.6 days, n = 7) compared to the camera data. Location data from collars suggested departure occurred on average 3.2 days later (±7.0 days, n = 7) than camera observations. We found that the probability a bear had broken out of the den could be accurately predicted from changes in collar temperature, activity, and ordinal date (e.g., a 1 SD decrease in collar temperature increased the probability of breakout by 18.5 percentage points). Post‐den emergence behavior was influenced by external environmental temperature, time of day, and the amount of time since den breakout; bears were more likely to emerge and stay outside longer given warmer temperatures and increasing time since den breakout. Our study highlights the importance of the post‐emergence period for cub acclimatization and development and provides new monitoring tools to study polar bear denning behavior, which is increasingly vulnerable to disruption in a rapidly changing Arctic.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".