Caribou encounters with wolves increase near roads and trails: a time‐to‐event approach
Bibliographic record
Abstract
Summary 1. Caribou and reindeer Rangifer tarandus are declining across North America and Scandinavia in part from wolf Canis lupus‐mediated apparent competition with more abundant ungulate prey species. While caribou generally persist in areas with low wolf density, wolf packs that overlap caribou ranges could trigger caribou declines. Moreover, anthropogenic linear features such as roads, trails and seismic lines are hypothesized to increase predation risk for caribou, yet few studies have examined the mechanistic effects of linear features or spatial overlap on wolf–caribou encounter rates and predation risk. 2. We used (a) time‐to‐event models of wolf–caribou encounters estimated from concurrent global positioning system (GPS) radio‐collar data from wolves and caribou and (b) wolf resource selection models of travel locations, to determine the potential influence of wolf–caribou spatial overlap, linear features, elevation and season on encounter rates. Analyses were based on data from 35 adult female caribou and 37 male and female wolves from 11 wolf packs from Banff and Jasper National Parks, Canada, from 2002 until 2010. 3. Wolf–caribou encounter rates increased with high wolf–caribou overlap, proximity to linear features and lower elevations. Wolves strongly selected low elevations, especially during winter and spring. Selection for linear features as travel routes increased with elevation. 4. Caribou risk of encounter was highest during the summer and autumn when wolves spent the most time at high elevations. Most wolf‐caused mortalities (n = 12) occurred during spring and summer. 5. Synthesis and applications. The presence of anthropogenic linear features and the amount of time wolves spend in caribou range could be equally as important as wolf density when prioritizing caribou recovery actions such as wolf or primary prey reductions or re‐introductions. The use of GPS locations and time‐to‐event modelling offers a powerful tool for evaluating factors affecting predation risk of threatened and endangered species.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".