Trade‐offs between predation risk and forage differ between migrant strategies in a migratory ungulate
Bibliographic record
Abstract
Trade-offs between predation risk and forage fundamentally drive resource selection by animals. Among migratory ungulates, trade-offs can occur at large spatial scales through migration, which allows an "escape" from predation, but trade-offs can also occur at finer spatial scales. Previous authors suggest that ungulates will avoid predation risk at the largest scale, although few studies have examined multi-scale trade-offs to test for the relative benefits of risk avoidance across scales. Building on previously developed spatial models of forage and wolf predation risk, we tested for trade-offs at the broad landscape scale and at a finer, within-home-range scale for migratory and non-migratory resident elk (Cervus elaphus) during summer in the Canadian Rockies in Banff National Park (BNP) and adjacent Alberta, Canada. Migration reduced exposure to wolf predation risk by 70% relative to residents at the landscape scale; at the fine scale, migrants used areas that were, on average, 6% higher in forage digestibility. In contrast, by forgoing migration, resident elk were exposed to higher predation risk, but they reduced predation risk at fine scales to only 15% higher than migrants by using areas close to human activity, which wolves avoided. Thus, residents paid for trying to avoid predation risk with lower forage quality. Residents may have been able to compensate, however, by using areas of abundant forage close to human activity where they may have been able to forage more selectively while avoiding predation risk. Human activity effectively decoupled the positive correlation between high forage quality and wolf predation, providing an effective alternate strategy for residents, similar to recent findings in other systems. Although ungulates appear capable of balancing risk and forage at different spatial scales, risk avoidance at large landscape scales may be more effective in the absence of human-caused refugia from predation.
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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 | 0.001 | 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".