Short-term effect of thinning on red maple transpiration in a temperate mixed forest
Bibliographic record
Abstract
Under climate change, forests are expected to experience drier conditions that may increase tree mortality. Silvicultural treatments, such as thinning, have been proposed to reduce moisture competition and to improve forest resistance to drought events. Most studies have investigated the effectiveness of thinning under semi-arid conditions, while little information is available regarding temperate forest responses, together with the residual basal area (BA) that is required to reap the benefits of these treatments. This research aims to understand how the residual BA influences transpiration in mixed temperate forest stands that are dominated by red maple (Acer rubrum) in southeastern Canada. We monitored the sap flux density (Fd) with thermal dissipation-type sensors for 18 red maples spread across nine experimental plots that were thinned to obtain a gradient of residual BA (20, 12.5, 6 m2 ha-1). The study was conducted during the first growing season following treatment. Low residual BA plots (6 m2 ha-1) incurred drier atmospheric conditions as shown by a greater vapor pressure deficit (VPD) compared to high residual BA plots (20 m2 ha-1). At the tree scale, Fd increased with residual BA, with the most pronounced differences under dry atmospheric conditions: when daily VPD exceeded 1.1 kPa, mean Fd in high residual BA plots was respectively 20% and 75% greater than in medium (12.5 m2 ha-1) and low residual BA plots. At the stand level, we simulated total transpiration considering the stand as only made of red maples. The transpiration in medium and low residual BA plots amounted to 41% and 79% of transpiration simulated in the high residual BA plot. Overall, this work highlighted broad variation in response to residual BA treatments, emphasizing the need to better model forest water budgets, and partitioning overstory and understory evapotranspiration to make more adequate residual BA prescriptions in temperate forests.
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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) | 0.000 | 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".