Site-level soil moisture controls water-use efficiency improvement and climate response in sugar maple: a dual dendroisotopic study
Bibliographic record
Abstract
Given the large contribution of forests to terrestrial carbon storage, there is a need to resolve the environmental and physiological drivers of tree-level response to rising atmospheric CO2. This study examines how site-level soil moisture influences growth and intrinsic water-use efficiency in sugar maple (Acer saccharum Marsh.). We construct tree-ring, δ18O, and Δ13C chronologies for trees across a soil moisture gradient in Ontario, Canada, and employ a structural equation modelling approach to ascertain their climatic, ontogenetic, and environmental drivers. Our results support previous evidence for the presence of strong developmental effects in tree-ring isotopic chronologies — in the range of −4.7‰ for Δ13C and +0.8‰ for δ18O — across the tree life span. Additionally, we show that the physiological response of sugar maple to increasing atmospheric CO2 depends on site-level soil moisture variability, with trees only in relatively wet plots exhibiting temporal increases in intrinsic water-use efficiency. These results suggest that trees in wet and mesic plots have experienced temporal increases in stomatal conductance and photosynthetic capacity, whereas trees in dry plots have experienced decreases in photosynthetic capacity. This study is the first to examine sugar maple physiology using a dendroisotopic approach and broadens our understanding of carbon–water interactions 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".