Siberian Ibex Capra sibirica Respond to Climate Change by Shifting to Higher Latitudes in Eastern Pamir
Bibliographic record
Abstract
Climate change has led to shifts in species distribution and become a crucial factor in the extinction of species. Increasing average temperatures, temperature extremes, and unpredictable weather events have all become a part of a perfect storm that is threatening ecosystems. Higher altitude habitats are disproportionately affected by climate change, and habitats for already threatened specialist species are shrinking. The Siberian ibex, Capra sibirica, is distributed across Central Asia and Southern Siberia and is the dominant ungulate in the Pamir plateau. To understand how climate change could affect the habitat of Siberian ibex in the Taxkorgan Nature Reserve (TNR), an ensemble species distribution model was built using 109 occurrence points from a four-year field survey. Fifteen environmental variables were used to simulate suitable habitat distribution under different climate change scenarios. Our results demonstrated that a stable, suitable habitat for Siberian ibex was mostly distributed in the northwest and northeast of the TNR. We found that climate change will further reduce the area of suitable habitat for this species. In the scenarios of RCP2.6 to 2070 and RCP8.5 to 2050, habitat loss would exceed 30%. In addition, suitable habitats for Siberian ibex will shift to higher latitudes under climate change. As a result, timely prediction of the distribution of endangered animals is conducive to the conservation of the biodiversity of mountain ecosystems, particularly in arid areas.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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 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".