A three decade assessment of climate‐associated changes in forest composition across the north‐eastern <scp>USA</scp>
Bibliographic record
Abstract
Summary Climate‐associated changes in forest composition have been widely reported, particularly where changes in abiotic conditions have resulted in high mortality of sensitive species and have disproportionately favoured certain species better adapted to these newer conditions. In the north‐eastern USA and south‐eastern Canada, few studies have examined climate‐related influences associated with forest composition, and none have considered broad‐scale changes over a long temporal (>25 years) period. We used US Forest Service Forest Inventory and Analysis data from 1983 to 2014 across four north‐eastern states (Maine, New Hampshire, New York and Vermont) to assess temporal and spatial changes in the occurrence and abundance of American beech Fagus grandifolia Ehrh, sugar maple Acer saccharum L., red maple Acer rubrum L. and birch Betula spp. saplings. We also tested the effects of biotic and abiotic factors on the distribution of the four studied deciduous species over the entire period examined. Occurrence and abundance of American beech have increased substantially over the past three decades, whereas the occurrence and abundance of three other deciduous species have decreased in all ecological provinces of the north‐eastern USA, except the Midwest Broadleaf ecological province. Consequently, a clear shift in species composition is currently underway in the beech‐maple‐birch (BMB) forests of the north‐eastern USA, with uncertain consequences for future ecosystem structure and function. In the studied region and over the entire period examined, the distribution of increased occurrence and abundance of beech relative to the three other deciduous species were associated with higher temperature and precipitation as well as higher conspecific basal area and dead tree basal area. Synthesis and applications. The change from beech‐maple‐birch forests to more beech‐dominated forests with beech encroachment to new forest areas across the north‐eastern USA may continue if higher intensity harvesting and disturbances (i.e. large‐scale canopy openings) do not occur. This would be a significant management concern as beech is associated with a widespread bark disease, is commercially less desirable, and can limit natural regeneration from other species. Our results emphasize the need for management strategies such as higher intensity harvesting methods, vegetation control and limiting browsing pressure to reduce beech dominance.
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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 | 0.001 |
| 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".