Avoidance or Coexistence? The Spatiotemporal Patterns of Wild Mammals in a Human-dominated Landscape in the Western Himalaya
Bibliographic record
Abstract
Human–wildlife interfaces are increasing rapidly due to the disproportionate growth of human and wildlife populations in a spatial context. The Himalayan system, a global biodiversity hotspot, is subject to landscape modification from various anthropogenic activities. In this study, we offer insights into the human–wildlife interface, reflecting avoidance or coexistence, with implications for local and landscape management strategies. We investigated fine-scale space use and temporal activity patterns of mammalian wildlife in a human-dominated landscape, outside a protected area. The research methods involved robust digital camera trap sampling (n = 131) across the target area (116 km2) with a total human population of 153,585. We developed a new sampling strategy that accounted for spatial heterogeneity in the habitats and variations in mammalian community composition. Our results showed that, in spite of high usage and the presence of humans across the study area, 16 wild mammal species used the area with varying intensities, exploiting habitat and forage availability. Of the camera traps placed in the study area, 70.23% had overlapping captures for humans on foot and wild mammal species. Generalist species used natural, modified, and altered habitats, while herbivores remained in natural and modified areas. However, some mammals that used modified/altered areas avoided humans by modifying their temporal activity. In the context of management of large landscapes, including areas outside the protected area network, the results of this study highlight the significant plasticity exhibited by wild mammals in negotiating natural and human-modified habitats. This offers an opportunity to develop conservation management strategies focusing on these fine-scale patterns and human actions.
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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 | 0.001 | 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".