Spatial and environmental determinants of plant species diversity in a temperate desert
Bibliographic record
Abstract
Deserts are one of the ecosystems most sensitive to global climate change. However, there are few studies examining how changing abiotic and biotic factors under climate change will affect plant species diversity in the temperate deserts of Asia. This study aimed to: (i) characterize species distributions and diversity patterns in an Asian temperate desert; and (ii) to quantify the effects of spatial and environment variables on plant species diversity. We surveyed 61 sites to examine the relationship between plant species diversity and several spatial/environmental variables in the Gurbantunggut Desert. Spatial and environmental variables were used to predict plant species diversity in separate multiple regression and ordination models. Variation in species responses to spatial and environmental conditions was partitioned by combining these variables in a redundancy analysis (RDA) and by creating multivariate regression trees (MRT). We found 92 plant species across the 61 sites. Elevation and geographic location were the dominant environmental factors underlying variation in site species richness. A RDA indicated that 93% of the variance in the species–environment relationships was explained by altitude, latitude, longitude, precipitation and slope position. Precipitation and topographic heterogeneity, through their effects on water availability, were more important than soil chemistry in determining the distribution of species. MRT analyses categorized communities into four groups based on latitude, soil pH and elevation, explaining 42.3% of the standardized species variance. Soil pH strongly influenced community composition within homogeneous geographic areas. Our findings suggest that precipitation and topographic heterogeneity, rather than edaphic heterogeneity, are more closely correlated to the number of species and their distributions in the temperate desert.
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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".