Data from: Coping with strong variations in winter severity: plastic habitat selection of deer at high density
Bibliographic record
Abstract
Few empirical studies on large herbivores considered how behavioral plasticity could enhance their capacity to cope with rapid and extreme changes in weather conditions at several spatiotemporal scales. During winter, large herbivores living under low predation pressure mainly trade-off benefits of forage acquisition with the costs of exposure to harsh weather conditions. We assessed the changes in this trade-off for white-tailed deer adult females on Anticosti Island (Canada) at different scales during 2 contrasted winters (i.e., a harsher and a milder winter). We hypothesized that deer should adjust their foraging decisions to avoid cold wind-chill temperatures and high locomotion costs in deep snow as winter severity increased. We compared habitat selection at the home-range scale, habitat selection relative to thermal conditions within the home range, and selection for foraging sites relative to snow conditions along the foraging tracks between winters. Home-range selection of deer was similar between winters. Deer adjusted their within-home-range selection relative to thermal conditions: they selected thermal cover during cold-stress periods while their selection for open areas increased during the warmer periods. Deer showed high behavioral plasticity along their foraging tracks: they selected tracks with different forage resources between winters and traded-off the locomotion costs in deep snow cover with the benefits of forage availability as winter severity increased. We discuss how behavioral plasticity of deer in their thermoregulatory behavior and foraging site selection allows them to cope with varying winter conditions, in a system where their short-term behavioral adaptations were already strongly constrained by intraspecific competition.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.004 |
| 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".