Hunting exacerbates the response to human disturbance in large herbivores while migrating through a road network
Bibliographic record
Abstract
Abstract Migrations of large ungulates are globally threatened in environments affected by increasing human disturbance, rising large carnivore predation, deteriorating habitat quality, and changing climate. Animals migrating outside of protected areas can be exposed to greater human pressure, and this effect can be stronger when humans are perceived to be a predation risk, such as during hunting seasons. Using four consecutive years of satellite telemetry data ( n = 138 migration events), we compared habitat selection, movement, and behavior of a large partially migratory herbivore while migrating through a heterogeneous landscape in spring and fall. We tested the hypothesis that fall hunting exacerbates the response of a large herbivore exposed to human disturbance while migrating through a road network. All elk ( Cervus elaphus ) selected greater forest cover, reduced movement rates, and avoided roads during fall‐day than in any other season or time of day. Avoidance of roads was reduced during spring at night, for example, the time period of no hunting with fewest people on roads. Elk using stopovers in fall displayed different seasonal and diurnal behaviors between sexes in response to the disturbance. Females used steeper terrain during fall‐day and males did not use this strategy in fall. Male avoidance of roads was much stronger than females during fall‐day and males were less likely to cross a road during fall. Such responses are probably linked to higher hunting pressure on males vs. females. Finally, we found that elk spent more time feeding during spring migration compared to the fall migration and elk vigilance was >3 times higher in the fall hunting season. Our results provide insights into the effect of fear of humans on the ecology of both sexes of a migrating large herbivore when using stopovers. Such changes in behavior and stopover use might affect animal fitness by decreasing foraging, cause displacement from high‐quality habitats, or affect the permeability of migration route stopovers.
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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 | 0.000 |
| 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 | 0.000 | 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".