Social facilitation of risky habitats in woodland caribou: responses to fire and roads
Bibliographic record
Abstract
Habitat change and subsequent trophic effects remain the dominant hypothesis for woodland caribou (Rangifer tarandus) population declines. Boreal forests are undergoing rapid changes, including expanding linear features and climate-induced increased wildfire activity. Within these landscapes caribou habitat selection and movement ought to maximize benefits while mitigating costs, e.g. predation risk, but spatial locations are likely not fixed in their cost-benefit ratio. For caribou, costs and benefits may vary seasonally and by social context. To test the effects of season and social context on caribou movement and selection of potentially risky habitats, we used a socially informed integrated step selection analysis (iSSA). We tested responses to two forms of risk in Terra Nova National Park, NL: roads and areas burned 2 - 75 years prior. Caribou avoided areas near roads irrespective of season. Burned area avoidance varied by season and burn age. Caribou avoided roads less and selected burns more in a social context. Social facilitation may permit the use of areas that were functionally unavailable to animals that were not in groups. As the most at-risk populations of woodland caribou exist at low density in highly disturbed landscapes, our results highlight the importance of considering sociality in maintaining access to resources.
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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 | 0.000 | 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".