Scaling from single-point sap velocity measurements to stand transpiration in a multispecies deciduous forest: uncertainty sources, stand structure effect, and future scenarios
Bibliographic record
Abstract
A major challenge in studies estimating stand water use in mixed-species forests is how to effectively scale data from individual trees to the stand. This is the case for forest ecosystems in the northeastern USA where differences in water use among species and across different size classes have not been extensively studied, despite their relevance for a wide range of ecosystem services. Our objectives were to assess the importance of different sources of variability on transpiration upscaling and explore the potential impacts of future shifts in species composition on the forest water budget. We measured sap velocity in five tree species (Fagus grandifolia Ehrh., Acer rubrum L., Acer saccharum Marsh., Betula alleghaniensis Britton, and Betula papyrifera Marsh.) in a mature stand and a young stand in New Hampshire, USA. Our results showed that the greatest potential source of error was radial variability and that tree size was more important than species in determining sap velocity. Total sapwood area was demonstrated to exert a strong controlling influence on transpiration, varying depending on tree size and species. We conclude that the effect of potential species shifts on transpiration will depend on the sap velocity, determined not only by radial variation and tree size, but also by the sapwood area distribution in the stand.
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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 | 0.004 | 0.013 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".