Small-scale patterns of species richness and floristic composition in relation to microsite variation in herb-rich woodlands
Bibliographic record
Abstract
Non-riverine Eucalyptus camaldulensis (red gum) woodlands with herbaceous understoreys in southern Australia have been recognised as botanically significant due to their high small-scale species richness, but little is known about the factors that underpin these patterns. To examine the influence of local environmental variation (microsites) on small-scale vegetation patterns, we sampled vegetation under trees, away from trees, and in depressions and hummocks in three herb-rich woodlands. Trees influenced both the composition and richness of the ground-layer vegetation, with reductions in species richness found under trees in sites where incident light availability was reduced by >40%. Species composition and richness differed between microsites, indicating that spatial heterogeneity is an important factor affecting species distribution patterns. Patchiness in relation to abiotic factors creates environmentally and compositionally distinct patterns. Indicator species analysis found that all microsites could be distinguished by character species with some evidence for microsite limitation for only a few species, lending weak support for a niche-based model of community assembly for herb-rich woodlands. A more plausible explanation for extremely high small-scale species richness is the lack of dominant species in these low productivity woodlands.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".