Elevation shapes tree composition, structure and diversity more than soil properties in the Annapurna Conservation Area, Nepal
Why this work is in the frame
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Bibliographic record
Abstract
Despite Nepal's steep elevational gradients and ecological diversity, forest stand dynamics along these gradients remain poorly understood. Therefore, this study aimed to analyze how elevation (1500–4000 m) influences forest properties in the Sikles region of the Annapurna Conservation Area, Nepal. We used generalized additive modeling (GAM) to capture nonlinear patterns in tree diversity and structural attributes across the elevation gradient, and structural equation modeling (SEM) to disentangle the direct and indirect relationships among soil, environmental, and tree variables. Transect-based sampling was conducted using 60 rectangular plots (20 × 25 m) across elevation gradients, spaced at 100 m intervals. Within each plot, we recorded dendrometric attributes (DBH > 5 cm, tree height, canopy cover), soil properties (pH, organic matter, total nitrogen, available phosphorus and potassium), and environmental variables (slope, aspect, elevation, precipitation, and temperature). Tree diversity and species composition declined with elevation. GAM revealed a significant nonlinear negative effect of elevation on diversity and structural attributes, with a plateau between 2000 and 3000 m and decline above 3000 m. Only organic matter and nitrogen showed weak elevation trends. SEM identified elevation, precipitation and soil nutrients as key drivers of species richness and tree structure. This study recommends habitat protection and sustainable forest management in mid-elevation forests (2000–3000 m) where diversity is highest, and restoration strategies above 3000 m such as assisted regeneration, enrichment planting, and introduction of climate-resilient native species to mitigate ongoing degradation and climate risks. These findings support elevation-based forest management in Nepal’s mountain forests and contribute to global climate-resilient conservation efforts.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it