Elevation and aspect determine the differences in soil properties and plant species diversity on Himalayan mountain summits
Bibliographic record
Abstract
Abstract Although vegetation has been the focus of recent studies on mountain summits, little is known about smaller ‐ scale spatial patterns of soil physico ‐ chemical properties. Here, we report patterns and drivers of soil physico ‐ chemical properties and their role in shaping the plant diversity on mountain summits of the Himalaya. Using the globally standardized Multi ‐ Summit Approach, four summits along an elevation gradient from treeline to nival zone were selected in Kashmir Himalaya. Sampling of the summits was carried out to collect soil samples together with vegetation data and soil temperature. The results revealed that there is a significant effect of elevation and aspect on soil physico ‐ chemical properties and species richness on the summits. A significant correlation was observed between soil parameters and elevation, indicating that summits with distinct pool of species possess distinct soil properties. Moreover, soil temperature clearly determined the aspect ‐ wise distribution of soil parameters and species richness. The Canonical Correspondence Analysis revealed that elevation, soil temperature, pH, electrical conductivity and C:N ratio were more prominent in determining the plant diversity on summits. Our results demonstrate that mosaic of micro ‐ climatic conditions driven by elevation and aspect favor a suite of soil properties, which in turn determine the patterns of species diversity on the mountain summits. The present study, therefore, enhances understanding of the spatial patterns of variation in soil and plant diversity of mountain summits, which will help to monitor, model and predict how these ecologically unique ecosystems will respond to climate warming in the Himalaya, with implications for such environments elsewhere.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".