Lessons for Transboundary Snow Leopard Conservation: Findings From a GPS Telemetry Study in Kangchenjunga Conservation Area, Nepal
Bibliographic record
Abstract
ABSTRACT Ensuring the long‐term persistence of snow leopards ( Panthera uncia ) in changing landscapes requires a deep understanding of their spatial ecology and movement behavior. To maintain viable metapopulations and ensure gene flow between populations, there is an urgent need to develop sound and effective conservation plans. This study presents findings from Nepal's first GPS telemetry study of snow leopards, shedding light on their home range size, habitat selection, and transboundary movements. GPS data were collected from four snow leopard individuals in the Kangchenjunga Conservation Area, eastern Nepal, over tracking periods ranging from 20 to 659 days, yielding a total of 4707 location points. We used three home range estimators for analysis: local convex hulls (LoCoH), fixed kernels (Kernel), and minimum convex polygons (MCP). Our results show that home range sizes were 6 to 97 times larger than previous estimates for Nepal, with LoCoH estimates of 310 and 102 km 2 (MCP = 730 and 211 km 2 ) for two adult females and 312 km 2 (MCP = 1032 km 2 ) for one adult male. Three snow leopards crossed international borders five to seven times, spending, on average, 10%–34% of their time in neighboring countries (China and India), with 28%–50% of their home ranges overlapping India. Our study demonstrates that snow leopards in Nepal have home ranges that are significantly larger than previously documented and frequently cross international borders. These extensive transboundary movements highlight the need for stronger coordination between Nepal, China, and India to ensure the long‐term conservation of snow leopards in this key region of their distributional range.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".