Establishing baseline estimates of blue sheep (Pseudois nayaur) abundance and density to sustain populations of the vulnerable snow leopard (Panthera uncia) in Western Bhutan
Bibliographic record
Abstract
Context Advances have been made in the development of reliable methods for estimating the abundance and density of large threatened mammalian predators, but there is little progress on developing population estimates for their principal prey. No standardised protocol for estimating prey populations exists, therefore different researchers use different methods. As such, there is little information on key prey species of the vulnerable snow leopard and this has hindered the preparation of effective snow leopard conservation plans. Aims This study aimed to establish an estimated seasonal baseline population abundance and density of blue sheep in the Lingzhi Park Range (LPR) of Bhutan’s Jigme Dorji National Park over winter (December to February) and summer (May to July). It also aimed to assess the number of snow leopard individuals that the current blue sheep population can sustain in the study area. Methods A refined double-observer survey method was used and involved walking transect lengths of 414 km in winter and 450 km in summer to estimate blue sheep abundance with the aid of 8 × 30 binoculars and 15 × 45 spotting scopes. Key results In total, 1762 (s.e. ± 199) blue sheep individuals were recorded in winter at a density of 8.51 individuals per km2 and 2097 (s.e. ± 172) individuals in summer at a density of 9.32 individuals per km2. Mean group size of blue sheep was 38.12 individuals (s.e. ± 6) in winter and 52.36 individuals (s.e. ± 4) in summer. LPR was estimated to sustain 11–17 snow leopards in winter and 15–21 in summer. Key conclusions LPR can be a hotspot for snow leopard conservation in western Bhutan and regionally in the eastern Himalayas, because the comparatively higher estimated blue sheep abundance and density supports possibly the highest density of snow leopards in Bhutan. The modified double-observer method used to assess blue sheep population estimates is inexpensive, robust and practical for the mountainous terrain of the Himalayas. Implications On the basis of this study, it is recommended that a refined double-observer method is adopted as a standard technique for estimating blue sheep populations in the snow leopard range countries of the Himalayas. Snow leopard conservation plans should, additionally, include efforts to minimise threats to blue sheep populations. This refined method is also highly applicable for future surveys of gregarious mammalian taxa, such as ungulates and primates, in difficult mountainous terrain elsewhere in the world.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".