Scale and selection of habitat and resources: Tibetan argali (Ovis ammon hodgsoni) in high-altitude rangelands
Bibliographic record
Abstract
Processes of habitat selection occur at multiple spatiotemporal scales, where large-scale selection is often determined by predation risk and landscape features, and finer scale selection by resource abundance and quality. To determine whether this hierarchy exists in relatively homogenous systems, we investigated patterns of habitat (landscape topography) and resource (feeding patch and plant group) selection by a medium-sized ungulate, the Tibetan argali ( Ovis ammon hodgsoni Blyth, 1840), in the high-altitude rangelands of the Indian Trans-Himalaya. We ran ecological niche factor analyses to explore habitat selection, bias-reduced logistic regression to analyze the selection of feeding patches, fuzzy correspondence analysis for vegetation categories, and microhistological analyses for the selection of plant groups. For springs and summers of 2005–2007, argali preferred an intermediate range of altitude, slope, and forage abundance. Selection of feeding patch was mainly determined by forage quality, not biomass, selecting graminoids and forbs, in particular. The avoidance of habitat with high forage abundance could indicate a trade-off between forage quality and quantity; a pattern consistent at the feeding-patch scale. Our results provide evidence that the hierarchical pattern of habitat selection probably also occurs in relatively homogeneous systems.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".