Anthropogenic effects on activity patterns of wildlife at crossing structures
Bibliographic record
Abstract
Despite the popular perception of protected areas as vestiges of remnant wilderness, the behavior and activity patterns of wildlife in these areas are still subject to many forms of anthropogenic disturbances, such as roads, recreation and resource extraction. In Banff National Park, Alberta, efforts to mitigate the effects of roads on wildlife using wildlife crossing structures (WC) have been successful for a number of large mammal species, when measured as a reduction in mortality from vehicle collisions or the restoration of population connectivity; however, a comprehensive view of mitigation success should also account for the restoration of animal behavior and activity patterns. We evaluated the general hypothesis that existing WC have mitigated the effects of anthropogenic disturbance on wildlife activity, and predicted that the activity patterns of wildlife at WC should be similar to those in areas characterized by little or no human disturbance. We collected data from a long‐term monitoring program of activity patterns, in an assemblage of large mammal species, using a network of camera traps throughout Banff National Park. We quantified the inter‐ and intra‐specific overlap of diel activity patterns at 39 engineered wildlife crossings, varying in design (e.g., over and underpasses) and age. We compared activity at WC to baseline conditions at 126 backcountry sites (BC). In general, activity patterns at WC were found to closely match those at BC. Of the eight species we examined, only elk and coyotes showed significantly different peaks in activity. Activity between WC depended on the time of day, the amount of human use and age of the crossing structure; however, the impact of these factors varied among carnivores and ungulates. Our work demonstrates that the impact of people on wildlife activity at WC is highly context dependent and that the restoration of native patterns of activity within protected areas will require management of human activity, even in relatively remote areas.
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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 | 0.001 |
| 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".