Abiotic and Biotic Factors Influencing Sugar Maple Health: Soils, Topography, Climate, and Defoliation
Bibliographic record
Abstract
Sugar maple ( Acer saccharum Marsh.), a keystone species of northern hardwood forests, is susceptible to decline, especially on sites low in the soil base cations calcium (Ca) and magnesium (Mg). A common stressor of sugar maple is forest tent caterpillar (FTC; Malacosoma disstria Hübner), an indigenous defoliator. The recent outbreak of FTC (2002–2007) affected 600,000 ha of forest in the northeastern United States and Canada. We assessed the condition of sugar maple trees in 47 North American Maple Project stands in Massachusetts (2006–2007) and Vermont and New York (2007–2008) just after the peak of the FTC outbreak. Mortality was highest in stands with the most crown dieback the previous year ( R 2 = 0.62, P < 0.001). In addition to drought, cold winter temperatures, and concave microrelief, mortality reflected an interaction of defoliation with soil base cation availability ( P = 0.02), with stands defoliated in 2005 that also had low Mg saturation in the A horizon being most likely to suffer high mortality. Sites with above‐average annual sugar maple mortality (>3 or 4%) occurred on soils with low concentrations of Ca (0.31–0.46 cmol c kg −1 in the upper B horizon), Mg (0.06–0.10 cmol c kg −1 ), and K (0.03–0.05 cmol c kg −1 ). This work extends the thresholds for these base cations determined by previous research on the Allegheny Plateau to a larger geographic area.
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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".