Relationships between spatial environmental heterogeneity and plant species diversity on a limestone pavement
Bibliographic record
Abstract
No empirical studies have examined the relationship between diversity and spatial heterogeneity across unimodal species richness gradients. We determined the relationships between diversity and environmental factors for 144 0.18 m 2 plots in a limestone pavement alvar in southern Ontario, Canada, including within‐plot spatial heterogeneity in soil depth, microtopography and microsite composition. Species richness was unimodally related to mean soil depth and relative elevation. Microsite heterogeneity and soil depth heterogeneity were positively correlated with species richness, and the richness peaks of the unimodal gradients correspond to the maximally spatially heterogeneous plots. The best predictive models of species richness and evenness, however, showed that other factors, such as ramet density and flooding, are the major determinants of diversity in this system. The findings that soil depth heterogeneity had effects on diversity when the effects of mean soil depth were factored out, and that unimodal richness peaks were associated with high spatial heterogeneity in environmental factors represent significant contributions to our understanding of how spatial heterogeneity might contribute to diversity maintenance in plant communities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".