Caribou avoiding wolves face increased predation by bears – <scp>C</scp>aught between <scp>S</scp>cylla and <scp>C</scp>harybdis
Bibliographic record
Abstract
Summary Prey may trade off resource acquisition with mortality risk by using various habitat selection strategies. Empirical assessments have shown that the functional and numerical responses of predators to human disturbances are variable, yet spatial changes in predation risk by two predators have seldom been studied for prey occurring in human‐modified landscapes. Using the boreal caribou Rangifer tarandus caribou – grey wolf Canis lupus – black bear Ursus americanus system in eastern Canada, we investigated whether responses of prey towards one predator could concomitantly increase risk of predation from another predator exhibiting a different foraging tactic. We investigated trade‐offs made by solitary caribou females and mothers accompanied by their calf during the period of highest calf vulnerability and compared the behaviour of mothers that would eventually lose their calf to predation to that of mothers whose calf survived until the following year. We modelled habitat selection using different metrics of forage based on field measurements and digital maps and developed empirical models of predation risk and prey behaviour using GPS data collected on both predators and prey. Mothers accompanied by their calf seemed to compromise foraging opportunities for safety, as opposed to solitary females who showed no particular avoidance of areas used by predators. Although caribou mothers adopted selection strategies that could have protected their offspring from wolves, females that eventually lost their calf to predation selected for vegetative associations that were favourable to bears. Synthesis and applications . We determined that mothers that most strongly avoided suitable wolf habitat were also those that most strongly selected suitable bear habitat, suggesting that by using antipredator strategies aimed at reducing predation risk from wolves, caribou exposed their offspring to increased predation risk from bears. This result is of paramount conservation value as bears were responsible for 94% of caribou calf kills in this system. In the short term, conservation efforts for boreal caribou may benefit from the management of bear populations by means of liberal hunting regulations or predator control. In the long term, however, these actions should be used in conjunction with the protection of potential calving areas away from cutblocks and roads.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".