Floristic composition in relation to environmental gradients across <scp>K</scp>wa<scp>Z</scp>ulu‐<scp>N</scp>atal, <scp>S</scp>outh <scp>A</scp>frica
Bibliographic record
Abstract
Abstract Conservation planning in the face of global change is still in its infancy. A suggested approach is to incorporate environmental gradients into conservation planning as they reflect the ecological and evolutionary processes generating and maintaining diversity. Our study provides a framework to identify the dominant environmental gradients determining floristic composition and pattern. Nonmetric multidimensional scaling was used on 2155 sampling plots in savanna and grassland habitat located across the province of KwaZulu‐Natal, South Africa (94 697 km2), a floristically rich region having steep environmental gradients, to determine the dominant gradients. Hierarchical cluster analysis was used to group similar plots which were then used in a Classification and Regression Tree analysis to determine the environmental delimiters of the identified vegetation clusters. Temperature‐related variables were the strongest delimiters of floristic composition across the province, in particular mean annual temperature. Frost duration was the primary variable in the Classification and Regression Tree analysis with important implications for savanna/grassland dynamics. Soil properties (base, pH status) and moisture variables accounted for most of the variation for the second and third axes of floristic variation. Given that climatic and edaphic variables were well correlated with floristic composition, it is anticipated that a changing climate will have a marked influence on floristic composition. We predict warmer temperatures may facilitate the spread of frost sensitive savanna species into previously cooler, grassland areas. Species associated with specific soil types will not easily be able to move up the altitudinal gradient to cooler climes because geology is aligned in an approximately north‐south direction compared with increasing altitude from east‐west. Future conservation planning should take cognisance of these gradients which are surrogates for ecological and evolutionary processes promoting persistence.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".