Spatial relationships of sympatric wolves (Canis lupus) and coyotes (C. latrans) with woodland caribou (Rangifer tarandus caribou) during the calving season in a human-modified boreal landscape
Bibliographic record
Abstract
Context Woodland caribou (Rangifer tarandus caribou) populations have declined across most of North America. Wolf (Canis lupus) predation on adults is partially responsible for declines; however, caribou declines also can be attributed to low calf survival. Wolves and invading coyotes (C. latrans) may contribute to mortality of calves. Aim We assessed wolf and coyote food habits and population and individual level selection for caribou-preferred habitats (bogs and fens) during the caribou calving season (15 April to 30 June) in north-eastern Alberta, Canada, to determine what role these predators might play as a mortality factor for caribou calves. Methods We deployed global positioning system and very high-frequency (VHF) radio-collars on 32 wolves and nine coyotes in January 2006 – January 2008, and VHF collars on 42 adult female caribou individuals in 2003–08. We assessed wolf and coyote habitat selection using used-available resource-selection functions, and spatial overlap of wolves and coyotes with caribou using logistic regression to estimate coefficients for latent selection-difference functions. We collected and analysed scats to assess wolf and coyote food habits. Key results Wolves generally avoided caribou-preferred habitats, particularly bogs. Most coyotes selected caribou-preferred habitats (bogs and/or fens); however, relative to caribou, they were found closer to upland forests. Hair from adult and calf caribou was uncommon in wolf and coyote diet and caribou is likely to be an uncommon alternative prey for these predators. Conclusions We found that >25% of wolf packs and most coyotes selected caribou-preferred habitats during the calving season. Although caribou was not an important prey, limited secondary predation, by these predators and black bears (Ursus americanus), on adult and calf caribou is likely to be contributing to caribou population declines. Implications We caution that predation on caribou is likely to escalate as coyotes expand into this region and increasing human disturbance continues to create habitat for white-tailed deer (Odocoileus virginianus), which is an important prey for both wolves and coyotes.
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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 | 0.001 | 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".