Distribution of shrublands in relation to soil and climate in mid-subtropical China
Bibliographic record
Abstract
Understanding relationships between vegetation and environments is of importance for ecosystem restoration and management. However, information on how environments influence the floristic patterns of shrublands is lack, especially in the subtropical China. In this study, we explored how environments regulate species composition of shrublands at landscape scale in mid-subtropical China. We investigated species composition and measured the climate and soil environments for 207 shrubland plots in mid-subtropical China (24°39′–30°08′N, 108°47′–114°15′E). We applied a hierarchical cluster analysis and indicator species analysis based on the Bray–Curtis similarity index to identify the main shrubland types and employed principal coordinate analysis (PCoA) to explore the relationship between floristic composition and environment. We identified four shrubland types occurring in different environmental conditions. Montane shrubland, dominated by species suitable for cool climates (e.g. Rhododendron simsii ), were distributed in steep areas at comparatively high altitudes; foothill shrubland, dominated by mesophilous species (e.g. Loropetalum chinense ), were distributed in low mountains and hills; pioneer shrubland, dominated by fast grow and short-life cycles species (e.g. Rhus chinensis ), were distributed at low altitudes with dense population; and finally, limestone shrubland, dominated by calcicole plants (e.g. Coriaria nepalensis ), were distributed in the extensive karst areas. Communities occurring in high pH soils were completely separated from those in low pH soils according to the hierarchical cluster analysis. PCoA ordination associated the four types with distinct edaphic and climatic gradients. Soil pH explained 63.3% of variation in PCoA, followed by soil depth and soil bulk density.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".