Understory species and functional diversity in a chronosequence of jack pine and red pine stands in the south-central boreal forest
Bibliographic record
Abstract
Dominant tree species and anthropogenic disturbances influence understory taxonomic diversity (TD) and species traits (functional diversity, FD) in managed forests. We compared understory TD and FD in managed jack pine (Pinus banksiana Lamb.) stands and red pine (Pinus resinosa Ait.) plantations. Understory herbs, grasses, shrubs, and nonvascular species were assessed in 1 m2 quadrats on two perpendicular line transects. Data were analyzed using generalised least squares regression, and multivariate fourth corner and RLQ analyses. Although there were few differences in understory species communities, red pine plantations appeared to impose weak environmental filters on TD in ways that were absent from the jack pine stands. Taxonomic diversity declined with age in red pine but not in jack pine, and may have been affected by the accumulation of litter cover in red pine stands. Although vegetation cover was lower in red pine than in jack pine, species richness on a percent cover basis was greater, and saturated at high cover levels in jack pine. Functional richness and evenness were unaffected by overstory species, but high litter cover favoured higher values of functional divergence. These results imply that there was a high level of functional redundancy among species for the suite of traits that we analyzed.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".