Legacy of forest composition and changes over the long-term on tree radial growth
Bibliographic record
Abstract
The forests of North America have undergone important changes since European settlement, particularly in terms of stand composition and associated changes in soil properties. While soil nutrients availability is known to influence forest productivity, the causes and consequences of its variation through time remain poorly understood. This study investigates the effects of long-term changes in forest composition and soil properties on the radial growth of sugar maple (Acer saccharum Marsh.) and balsam fir (Abies balsamea (L.) Mill.), two important species of northeastern North America’s forests. Using data from 130 plots measured in 1930 and in 2012–2014 and a mixed-effects modelling approach, we studied the links between radial growth, soil nutrients availability, current stand composition, and shifts in vegetation. The radial growth of balsam fir was found to vary with soil available nitrogen and present-day relative basal area of yellow birch in the stand, while that of sugar maple was found to be invariant to soil characteristics, but proportional to present-day spruce (Picea spp.) relative basal area. However, no direct effects of vegetation change on radial growth were detected. Our results suggest that prior stand composition had no influence on radial growth of both studied species, yet vegetation change could indirectly influence balsam fir growth through an improvement of litter quality with an increase in the abundance of yellow birch (Betula alleghaniensis Britton). Moreover, despite clear differences between the studied species, we conclude that maintaining a certain proportion of compositional diversity may enhance radial growth of both balsam fir and sugar maple.
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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".