Predicting multi-predator risk to elk ( Cervus canadensis ) using scats: Are migrant elk exposed to different predation risk?
Bibliographic record
Abstract
There is evidence that prey can perceive the risk of predation and alter their behaviour in response, resulting in changes in spatial distribution and potential fitness consequences. Previous approaches to mapping predation risk quantify predator space use to estimate potential predator-prey encounters, yet this approach does not account for successful predator attacks resulting in prey mortality. An exception is a prey kill-site, which reflects an encounter resulting in mortality, but obtaining these data can be expensive and requires time to accumulate adequate sample sizes. We illustrate an alternative approach using predator scat locations and their contents to quantify spatial predation risk for elk ( Cervus canadensis ) from multiple predators in Alberta, Canada. We combined predictions of scat-based resource selection functions for bears ( Ursus arctos/U. americanus ), cougars ( Puma concolor ), coyotes ( Canis latrans ), and wolves ( C. lupus ) based on scat-detection dog surveys with predictions for the probability that a predator-specific scat in a location contained elk. We evaluated our approach by comparing predictions to a predation risk model developed from elk kill sites and applied it to describing spatial patterns in predation risk that were consistent with changes in the distribution of elk over the past decade. We found a strong correlation between risk predicted by kill sites and risk predicted by our approach (r = 0.98, P < 0.001). There was a spatial pattern to predation risk, where elk that migrated east of their winter range were exposed to highest risk from cougars, non-migratory elk were exposed to high risk from wolves and bears, and risk to elk that migrated west of their winter range into protected areas was high only from bears. The patterns in predator risk were consistent with changes in the migratory tactics in this population. The scat-based approach we present permits broad-scale inferences on predation risk for prey.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".