Determinants of fine‐scale plant species richness in a deciduous forest of northeastern North America
Bibliographic record
Abstract
Abstract Question: What are the determinants of fine‐scale plant species richness (SR)? Location: Île‐aux‐Grues, Québec, Canada. Methods: Elevation, soil organic matter, soil pH, irradiance, tree basal area (BA) and plant SR (herbs, shrubs, and trees) were determined in 100 contiguous 25 m2 quadrats in a deciduous forest. Each variable was analysed for spatial autocorrelation using Moran's I. Path analysis was used to determine the effects of different variables on tree, shrub and herb SR in a hierarchical modelling approach. Results: Most of the variables, except tree BA, PPFD (photo‐synthetic photon flux density) and shrub SR, were positively autocorrelated at a scale of ca. 20 m or less. The path analyses explained ca. ll%‐40% of the variance in plant SR; however, the model for shrub SR was not significant. Tree SR was positively associated, but herb SR was negatively associated with tree BA. Tree SR had a positive influence on shrub SR, but herb SR remained unaffected by tree or shrub SR. Conclusion: The positive association of tree BA and tree SR suggests that the data from the study site correspond to the left (ascending) portion of the SR‐biomass relationship (un‐dersaturated SR). The negative effect of tree BA on herb SR is direct and not mediated through reduced PPFD. High tree BA might cause high resource use, induce high litter production and affect soil properties, all of which might significantly affect herb SR. Several factors not considered here might influence fine‐scale SR, such as interspecific interactions, fine‐scale disturbances and heterogeneity (both spatial and temporal) in resources and abiotic conditions. Within‐site variations of SR might be difficult to model with precision because of the relative importance of stochastic vs deterministic processes at this spatial scale.
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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 | 0.001 |
| 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".