Multiple Ecological Drivers Determining Vegetation Attributes across Scales in a Mountainous Dry Valley, Southwest China
Bibliographic record
Abstract
Identifying the ecological processes determining spatial variation in community composition and structure is a central issue in arid areas, especially in the face of climate change. This study aims to estimate how environmental and spatial processes jointly determine vegetation attributes across scales in the Min dry valley, one of the dry valleys in Hengduan Mountainous region suffering severe ecological degradation. A total of 48 plots along slope transects were investigated at three sites along the dry valley, with vegetation and environmental information gathered. Distance-based Moran’s eigenvector maps (dbMEM) was used to extract local spatial variables, while geographic coordinates were used as regional spatial variables. We used redundancy analysis (RDA) and variation partitioning to detect the relative importance of environmental and spatial processes in influencing community composition and vegetation structure (including biomass, coverage, height, density, α and β diversity), and to identify the most determinant environmental variables at different spatial scales. Results showed that both environmental and spatial processes accounted for significant and comparable variations in both vegetation composition and structure. Local spatial variables provided significant and comparable contribution as regional spatial variables to vegetation composition, while provided more contribution than regional spatial variables to vegetation structure. Topography had an overriding effect relative to soil on both vegetation composition and structure. Multi-scale analyses showed elevation was the most important variable (associated with soil moisture and nutrient) at the regional scale; while microtopography, especially slope aspect and shape, dominated at the local scale. We also demonstrated how vegetation composition and structure varied along environmental gradients. The study revealed the overriding role of topography in determining vegetation attributes in this mountainous dry valley, highlighting the advantage of multi-scale spatial analysis for better understanding spatial variation in vegetation pattern and with their important implications for biodiversity conservation and ecological management in the arid mountain areas.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".