Forest Landscape Heterogeneity Increases Shrub Diversity at the Expense of Tree Seedling Diversity in Temperate Mixedwood Forests
Bibliographic record
Abstract
Partial disturbances enhance spatial heterogeneity through the diversification of forest structure, which contributes to niche partitioning and consequently to species diversity. However, this heterogeneity–diversity relationship may differ between groups of species, and is potentially modified by biotic interactions at the community level. We propose that shrub diversity will be greater in heterogeneous landscapes, while tree diversity will be lower in those same landscapes, due to the biotic interactions of shrub competition. We conducted field sampling in the balsam fir/yellow birch bioclimatic domain in western Québec, a forested ecosystem disturbed by natural and anthropogenic partial disturbances. We selected 12 forested landscapes (1 km2), four in each of three classes of landscape heterogeneity (heterogeneous, moderately heterogeneous, homogenous). Shrub and tree species regeneration abundance was measured in three different size classes of canopy gaps and the forest understory. Gap proportions were assessed in each landscape using aerial LiDAR data. Tree and shrub alpha-diversity significantly responded to landscape heterogeneity, shrubs being more diverse while tree seedlings were less diverse in heterogeneous landscapes. Heterogeneous landscapes showed highest species accumulation rates for shrubs in medium-sized gaps. For tree seedlings, species accumulation rates were highest in heterogeneous landscapes in the forest understory. Our study thus supports the heterogeneity–diversity relationship with shrubs having higher alpha and beta diversity in heterogeneous landscapes whereas local-scale tree diversity was higher in homogenous landscapes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".