Beat the heat: Movements of a cold‐adapted ungulate during a record‐breaking heat wave
Bibliographic record
Abstract
Abstract The frequency and severity of extreme weather events such as heat waves are increasing globally, revealing ecological responses that provide valuable insights toward the conservation of species in a changing climate. In this study, we utilized data from two populations of GPS‐collared female wood bison ( Bison bison athabascae ) in the boreal forest of northwestern Canada to investigate their movement behaviors in response to the 2021 Western North American Heat Wave. Using generalized additive mixed‐effect models and a model selection framework, we identified a behavioral temperature threshold for wood bison at 21°C. Above this threshold, movement rates decreased from ~100 m/h at 21°C to a low of ~25 m/h at 39°C (150% decrease; −9%/°C). Extreme heat also contributed to changes in diurnal movement patterns, reducing wood bison movement rates and shifting the timing of peak activity from midday to early morning. These findings highlight the behavioral adaptations of female wood bison and underscore the need to understand the behavioral and physiological responses of cold‐adapted mammals to extreme weather events. Subsequent effects of thermoregulatory behavior may impact individual fitness and population viability, particularly at high latitudes where cold‐adapted species are increasingly exposed to severe weather resulting from anthropogenic climate change.
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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 | 0.000 |
| 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) | 0.001 | 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".