Border fences reduce potential for transboundary migration of Marco Polo Sheep (Ovis ammon polii) in the Pamir Plateau
Bibliographic record
Abstract
Border fences have severely impeded the transboundary migration of a number of large mammals worldwide, with central Asia being one of the most impacted. The Marco Polo sheep (Ovis ammon polii), an iconic species of Pamir, is threatened in its transboundary movement by increasing border fencing among their five distributed countries, including Tajikistan, Kyrgyzstan, China, Afghanistan, and Pakistan. In this study, by building ensemble species distribution models, we found that eastern Tajikistan had the largest suitable Macro Polo sheep habitat (about 42 % of the total suitable habitat), followed by China (about 32 %). We used least-cost paths to identify 51 ecological corridors including 5 transboundary ecological corridors, which may be important to maintain connectivity in both domestic and transboundary regions. To assess the potential barrier effect of border fences, we assessed four scenarios (30, 40, 50 and 60°) corresponding to the upper limit of the slope for the construction of fences. In areas too steep for fencing, these could be used by wild sheep to cross barriers or borders and may represent migration or movement routes, defined as natural passages. In the most pessimistic Scenario 60, only 25 migratory passages along the border fences were identified, compared to 997 in the most optimistic scenario (Scenario 30), indicating a strong negative effect of intensive border fencing on the transboundary movement of Marco Polo sheep. The establishment of transnational conservation parks, and ensuring permeability is maintained in key areas, could have a positive impact on the connectivity and persistence of Marco Polo sheep populations, and provide important lessons for other large migratory mammals in transboundary regions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".