Resource selection in a high-altitude rangeland equid, the kiang (<i>Equus kiang</i>): influence of forage abundance and quality at multiple spatial scales
Bibliographic record
Abstract
Herbivores foraging in arid and seasonal environments often face choices between plant patches varying in abundance and nutritional quality at several spatial and temporal scales. Because of their noncompartmented digestive system, equids typically rely on abundant forage to meet their nutrient requirements. In forage-limited environments, therefore, scarcity of food resources represents a challenge for wild equids. We investigated hierarchical resource-selection patterns of kiangs (Equus kiang Moorcroft, 1841), a wild equid inhabiting the high-altitude steppes of the Tibetan Plateau, hypothesizing that vegetation abundance would be the main factor driving resource selection at a large scale and that plant quality would influence resource selection at finer scales. We investigated resource-selection patterns at three spatial levels (habitat, feeding site, and plant (vegetation groups, i.e., grasses, sedges, forbs, and shrubs)) during summer and fall. At the habitat level, kiangs selected both mesic and xeric habitats in summer and only xeric habitats (plains) during fall. At the feeding-site level, feeding sites had higher plant biomass and percentage of green foliage than random sites in the same habitats. At the plant level, grasses were selected over forbs and shrubs, and sedges were used in proportion to their availability during all seasons. Our results indicate that resource-selection patterns in kiangs vary across scales and that both forage abundance and quality play a role in resource selection. Plant quality appeared more important than hypothesized, possibly to increase daily nutrient intake in forage-limited and highly seasonal high-altitude rangelands.
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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 | 0.000 | 0.000 |
| 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".