Increased tree water use with the development of a dense understory layer in a North American hardwood forest
Bibliographic record
Abstract
Abstract The formation of a single species, recalcitrant understory vegetation layer can limit tree regeneration and, in the long term, modify the composition of forests. Few studies have investigated how recalcitrant vegetation influences competition for water resources although the formation of a dense understory is likely to modify the forest water balance. In eastern North American hardwood forests, the development of a dense understory layer of American beech (Fagus grandifolia) has been observed in stands dominated by sugar maple (Acer saccharum), a phenomenon that shares many characteristics associated with recalcitrant vegetation. Given that water availability is generally negatively correlated with stand density, we hypothesized that the formation of a dense understory beech layer increased competition for water resources, thus leading to reduced water use by sugar maple trees in beech‐dominated stands. Using thermal dissipation sensors, we measured sap flux density (Fd) of two sugar maple trees at three beech‐dominated sites and three control sites. During the growing season, Fd of sugar maple trees was significantly larger at beech‐dominated sites compared to control sites, indicating a greater rate of water use by sugar maples in stands with a dense understory beech layer. We provide two hypotheses to explain our results at the tree scale: (i) reduced cover by forest floor vegetation could limit transpiration by this layer, thus allowing increased water availability to supply transpiration by overstory trees, or (ii) increased tree transpiration rate could be a mechanism to satisfy nutrient requirements in beech‐dominated stands often associated with lower soil fertility.
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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".