Diversity of canopy and understorey spiders in north‐temperate hardwood forests
Bibliographic record
Abstract
Abstract 1 We characterized and compared diversity patterns of canopy and understorey spiders (Arachnida: Araneae) on sugar maple ( Acer saccharum Marsh.) and American beech ( Fagus grandifolia Ehrh.) in hardwood forests of southern Québec, Canada. 2 We sampled canopies of 45 sugar maple and 45 American beech trees and associated understorey saplings in mature protected forests near Montréal. Samples were obtained by beating the crown foliage at various heights and by beating saplings around each tree. 3 Eighty‐two species were identified from 13 669 individuals. Forty‐eight species and 3860 individuals and 72 species and 9809 individuals were collected from the canopy and the understorey, respectively. 4 Multivariate analyses (NMDS ordination and NPMANOVA) showed the composition of canopy and understorey assemblages differed significantly, and canopy assemblages differed between tree species. Rank‐abundance distribution models fitted to the canopy and understorey data indicated that different mechanisms structure the assemblages in both habitats. Three abundant spider species were significantly more common in the canopy; ten species were collected significantly more often in the understorey. 5 The forest canopy was shown to be an important reservoir for spider diversity in north‐temperate forests.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".