Finding the ghosts: Snow leopard density and distribution in the multi‐use region of Jammu and Kashmir, India
Bibliographic record
Abstract
Abstract Large carnivores occur in human‐dominated landscapes globally, albeit with varying consequences for the animals and people involved. This lies in stark contrast to the belief that Protected Areas are the only means to conserve large carnivores, and in particular big cats. We aimed to assess snow leopard density and distribution in non‐protected landscapes within Jammu and Kashmir, India. Using detection/non‐detection records from 193 camera traps, we developed an ensemble species distribution model to identify important areas for snow leopard occurrence across J&K. To estimate population size in a non‐protected, multi‐use landscape, we applied spatially explicit capture–recapture (SECR) models to data from 47 camera traps deployed across five valleys covering 989 km 2 in Paddar, Kishtwar Himalaya. Alongside, we conducted focus group discussions with local communities to understand land‐use practices. The distribution model predicted high snow leopard suitability across the eastern region of Jammu and Kashmir, that is, the Kishtwar Himalaya. We estimated 0.35 (0.11–1.06) snow leopards100 km −2 with a realized abundance of 6 (6–11) individuals in Paddar. Land uses were local and migratory livestock grazing, religious pilgrimages and medicinal plant and fodder collection. Snow leopard were rarely seen by people, but livestock owners and herders faced livestock losses to snow leopards. Practical implication . Our approach provides the first integrated assessment of snow leopard occurrence in J&K, and density and land use in Paddar, offering insights for conservation planning outside formally protected areas. This underscores the urgent need to set up community‐based conservation interventions as we confirm that the Kishtwar Himalaya, irrespective of protection status, needs to be managed as a snow leopard landscape. Our study adds to the evidence that snow leopards need landscape‐level conservation strategies, rather than relying solely on Protected Areas.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".