Integrating human trail use in montane landscapes reveals larger zones of human influence for wary carnivores
Bibliographic record
Abstract
Coexistence between humans and wildlife is necessary for many conservation goals but is difficult to achieve in landscapes with increasing human populations and species that are often wary of people and may also threaten human safety. In these contexts, coexistence may be enhanced by identifying geographic areas where animal movement is particularly important and changes to human use via trail design could support both wildlife conservation and human safety. We used camera trap data to monitor the spatial distribution of grizzly bears (Ursus arctos), gray wolves (Canis lupus), and humans within the central Canadian Rocky Mountains, where anthropogenic development and human activity have gradually encroached on limited wildlife habitat. We quantified spatial variation in human use, and then incorporated this output into models for the detection rates of bears and wolves. We interpolated metrics of human use throughout the study area using inverse distance weighted averages of human detection rates from cameras. This approach supported a novel estimate of the cumulative effects of human use at all nearby trails on animal space use. We used our models to estimate the zone of influence of human use on bears and wolves, determining the distance at which human-use on nearby trails no longer exhibited a measurable change in detection rates for each of grizzly bears and wolves. The negative effects of human use on wildlife declined steeply with distance such that 50% of the decrease in detection rates immediately adjacent to trails would be expected to occur at 267 m for grizzly bears and 576 m for wolves. Weak effects, 5% as strong as the effect adjacent to trails, extended up to 1.8 km and 6.1 km for grizzly bears and wolves, revealing the importance of cumulative measures of human use. Our work shows how the distribution of human activity over entire landscapes can alter wildlife detection rates. Our results identify target buffer distances for protected areas near trails, and the modelling framework could be used by land managers to predict how altering trail networks and modifying human activity could affect wary wildlife species and advance coexistence.07-Nov-2024 --
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".