Relative importance of available energy, environmental heterogeneity, and seed availability for seedling emergence on a limestone pavement
Bibliographic record
Abstract
Environmental heterogeneity at fine spatial scales is expected to be especially important in determining community patterns at seed germination and establishment stages. I compared seedling and adult species richness patterns in relation to environmental gradients by adding seeds from 39 species across an elevation gradient, unimodally related to species richness on a limestone pavement. Environmental variables linked to habitat fertility (“available energy”), within-plot spatial or temporal variability (“environmental heterogeneity”), and spatial coordinates were evaluated as contributors to richness patterns using variance partitioning. Variables related to available energy explained most of the variance in species richness for adults and seedlings in sown plots; pure spatial variation, shared variation between energy and heterogeneity variables, and other shared fractions explained more of the variance in seedling richness in unsown plots. Seedling density and richness increased with sowing, but the relative increase differed along the elevation gradient; relative increase in richness was greatest at the lowest and highest elevations. Limited seed dispersal from parent plants may result in seedlings colonizing less favorable microsites in unsown plots, whereas sowing resulted in greater explained variance owing to environmental factors, and lower variance attributable to space alone, indicating that appropriate species are more able to reach suitable microsites. While many experimental studies have revealed associations between microsite characteristics and species-specific recruitment responses, seed limitations in natural communities can contribute to the spatial structure of seedling communities and mask environmental control of seedling species richness.
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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 | 0.000 | 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) | 0.001 | 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".