Influence of edaphic factors on plant distribution and diversity in the arid area of Xinjiang, Northwest China
Bibliographic record
Abstract
The influence of edaphic factors on plant distribution is essential to community ecology and important for vegetation restoration and management in arid ecosystems. In this study, desert plant distribution and diversity as well as soil water, salinity, and nutrients were measured in the Ebinur Lake Wetland Nature Reserve (ELWNR) in arid Northwest China. We determined relationships between plant distribution and soil factors along a transect with increasing distance from the river and created optimal models using soil factors to explain variations in plant diversity. In general, soil factors decreased with increasing distance from the river. Soil volumetric water content (SVWC), electrical conductivity (EC), pH, nitrogen (N), phosphorus (P), and sulfur (S) were closely related to plant distribution. According to canonical correspondence analysis, plants along the transect were divided into three groups: saline–alkaline tolerant, drought tolerant, and high P-demanding. SVWC, pH, soil organic carbon (C), N, P, and S account for 93.8% of the variations in plant diversity in the ELWNR. Compared with other soil factors, S explained the largest percentage of variations in diversity when single soil variables were considered in generalized additive models (R2 = 38.9%). Our results suggest increases in soil pH, P, N, and S would improve plant diversity significantly. We found differences among species groups in preference for habitat, and N and S content had a significant positive effect of on plant diversity, providing a scientific reference for plant restoration and saline soil remediation in desert ecosystems.
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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 | 0.001 | 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 | 0.001 |
| 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".