Leopards at large: population density, habitat utilisation and spatio-temporal overlap of leopards (Panthera pardus) with competitors and prey in the Rajaji Tiger Reserve, northern India
Bibliographic record
Abstract
Context In the vicinity of the western Rajaji Tiger Reserve, a significant rise in conflicts between humans and leopards has occurred. These conflicts have resulted in a substantial number of human deaths, with 60% attacks resulting in deaths over a 4-year period. Aims This study aims to examine any potential connection among the non-breeding tigers, leopards, prey, humans and the increased occurrence of conflicts. Methods To address this objective, we conducted an analysis to evaluate the density of leopards, as well as their spatial and temporal behaviour with respect to tigers, prey and humans through on-site habitat use, circular statistics, diel-overlap as well spatial niche breadth and spatial-niche overlap. Key results Our findings showed one of the highest leopard-density estimates (excluding cubs) (mean ± s.e.) of 23.7 ± 4.8 per 100 km2. When examining on-site habitat use by leopards, we found three variables that had a negative influence, namely, human presence, slope, and the presence of tigers. The presence of a large prey (sambar) had a positive influence. Leopards, exhibiting the highest spatial-niche breadth, demonstrated a small overlap with both tigers and humans, while displaying a significant diel overlap with sambar. The niche overlap was notably high with sambar, but very low with humans and tigers. Conclusions Although tigers locally created a zone of spatial and temporal avoidance, at the population level the leopards experienced a surge in numbers in response to the reduced tiger numbers, indicated by their high density. This could be a cause of high leopard movement into nearby human settlements. This unique scenario could have contributed to conflict. Implications Understanding the coexistence among different species and the impact of transient animals is crucial to develop effective management strategies to mitigate conflict. This approach would facilitate and ensure the long-term survival of diverse large carnivores in the tropical forests of southern Asia through sustained support for conservation from local communities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".