Coexistence of Sympatric Large Carnivores: Spatio‐Temporal Interactions Between Tigers and Leopards in Parsa National Park, Nepal
Bibliographic record
Abstract
ABSTRACT Understanding interspecific interactions between tigers ( Panthera tigris ) and leopards ( Panthera pardus ) is crucial for effective conservation planning. However, most studies have been conducted only in well‐known protected areas, leaving knowledge gaps in other parts of their overlapping range. This study investigates the spatial‐temporal interactions between sympatric carnivores (tigers and leopards) in Parsa National Park (PNP), Nepal. Camera trap data obtained from 157 sampling sites (2 × 2 km grid cells) were used to assess daily temporal activity patterns, single‐species occupancy, and density of tigers and leopards using spatially explicit capture‐recapture models (SECR) to further examine their coexistence mechanism in light of the recent recovery of tiger populations in PNP. In general, our findings indicate that both species co‐detected at 44 camera locations, demonstrating that they spatially share habitats inside the park. However, leopards avoid peak tiger activity periods, which is likely to reduce competitive interactions. The SECR model estimated a leopard density of 3.09 individuals per 100 km 2 whereas tiger density was 1.25 individuals per 100 km 2 within the study area. The model‐averaged occupancy probability of leopards in PNP was 0.45 (CI: 0.30, 0.64). The normalized difference vegetation index (NDVI) had a strong correlation with leopard occupancy, while the tiger relative abundance index (RAI) had minimal impact, reflecting the importance of high‐quality habitats in protected areas for conserving both species. Conservation initiatives targeting to strengthen the tiger recovery plans should incorporate thorough studies of interspecific interactions between sympatric large carnivores like tigers, leopards, and their prey base on a fine‐grain scale to ensure effective management strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".