Multivariate analysis of the effects of edaphic and topographical factors on plant distribution in the Yilong Lake Basin of Yun-Gui Plateau, China
Bibliographic record
Abstract
The relationships between environmental factors (soil and topography) and plant (shrub and herb) distribution on different hillside habitats in the Yilong Lake basin, southwest China, are examined. Surveys of 31 shrub sites and 31 herb sites, as well as seven edaphic and four topographic factors, on the northern and southern slopes around the Yilong Lake were performed monthly in October and November, 2004, and in November and December, 2005. Two-way indicator species analysis (TWINSPAN) was used to classify the plant communities, showing that the shrub community types on the northern and southern slopes were different, in contrast with the herb communities. Canonical correspondence analysis was conducted to clarify the relationships between vegetation and environmental factors in order to interpret the distribution of the communities and compare the relative importance among the environmental factors to the vegetation. The results showed that: (1) the combined effects of topography and soil explained 30.3% of the variability of shrub species distribution and 28.4% of the variability of herb species distribution; (2) the single effects of soil and topography explained 23.8 and 23.5% of shrub species distribution and 22.1 and 17.6% of herb species distribution, respectively; (3) slope aspect was the most important factor influencing shrub and herb distribution; and (4) specific site characteristics have important implications for effective vegetation management strategies and restoration of native species communities. Key words: Canonical correspondence analysis, ecology, soil, topography, vegetation distribution
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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 | 0.001 |
| 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 | 0.000 | 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".