A beech bark disease induced change in tree species composition influences forest floor acid–base chemistry
Bibliographic record
Abstract
Beech bark disease (BBD) has demonstrable ecosystem consequences for eastern US forests stemming from American beech (Fagus grandifolia Ehrh.) mortality, often leading to increased dominance by its competitor, sugar maple (Acer saccharum Marsh.). We hypothesized that this BBD-induced shift in tree species composition leads to changes in soil acid–base chemistry, mediated through differences in leaf litter chemistry of the two species. Using a sequence of plots representing the progression of the disease in the Catskill Mountains, NY, USA, we examined the influence of tree species composition shift on soil chemistry. The BBD impact on tree species composition was confounded by variability in substrate (or nonexchangeable soil) calcium (Ca). While substrate Ca explained much of the variation in acid–base chemistry, increasing BBD was associated with increasing forest floor exchangeable Ca, sum of base cations, base saturation, cation-exchange capacity, and decreasing hydrogen. An apparent threshold effect of substrate Ca on sugar maple litter Ca concentration suggests that underlying soil Ca availability may contribute to the spatial extent and timeframe of BBD-induced shifts in species composition. The species compositional shift is a mechanism contributing to a vegetation effect on soil acid–base status and may partially counteract soil acidification in this acid deposition impacted region.
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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 | 0.000 | 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".