Embedding trait‐based ecology within indigenous knowledge to advance sustainable management of Tibetan rangeland
Bibliographic record
Abstract
Abstract Questions Traditional pastoralists attribute high numbers of plateau pikas (Ochotona curzoniae) to impoverished soils that favor more and better forage for pika, suggesting a bottom‐up control of pika density. Conversely, government policies focus on excessive numbers of this small mammalian herbivore as the primary top‐down cause of degradation in Tibetan rangeland. Despite concerted campaigns to reduce pika abundance in recent decades, the sustainability of Tibetan rangelands remains uncertain. Location Alpine meadows on the Tibetan Plateau. Methods We proposed a conceptual model based on indigenous knowledge that predicted pika numbers from soil condition and plant traits. At three alpine meadow sites lightly grazed by livestock, we tested whether spatial variation in pika burrow density could be explained by changes in the functional composition of the plant community attributable to species turnover and intraspecific trait variation associated with changes in soil fertility. Results Due primarily to intraspecific trait variation, changes in the functional composition of the meadow community accounted for 56%–68% of the spatial variation in pika density, changes in the proportion of plant functional groups for 62%–74%, and changes in edaphic conditions for 71%–82%. Greater pika density was associated with a decline in soil phosphorus availability and a lower‐growing vegetation profile enriched in both the quantity and quality of forage preferred by pikas. Conclusions These results, which are in accord with indigenous knowledge, suggest that compensating soil phosphorus losses and maintaining a well‐calibrated grazing rotation can better manage pika populations and improve the sustainability of Tibetan rangelands. By combining trait‐based ecology and traditional knowledge, our study provides new insight into both understanding the dynamic complexity of grazing regimes and managing rangeland sustainability.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".