High responsiveness of wood anatomy to water availability and drought near the equatorial rear edge of Douglas-fir
Bibliographic record
Abstract
In trees, wood anatomy is a more precise and informative measure of hydraulic responses to drought than radial growth. Tree populations located near the equatorial-range edge of their distribution (rear edge) are used to monitor responses to changes in hydroclimate; however, few studies have assessed the wood anatomy of these populations. We measured tracheid lumen diameter (LD) and cell wall thickness (CWT) in a rear-edge Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) population. We also evaluated the formation of intra-annual density fluctuations (IADFs), characterized by earlywood (EW) tracheids with narrow lumens. We expected that EW LD would be particularly sensitive to hydroclimate variability. We found that EW LD was directly related to precipitation during the previous year’s late winter and the current year’s spring. Wet conditions were associated with wider EW tracheids and improved growth. These correlations peaked when considering cumulative precipitation from January to May. EW IADFs were formed in response to dry conditions during March and April, suggesting that dry spring conditions limit tracheid expansion. The described associations in a rear-edge Douglas-fir population confirm that wood anatomy is highly responsive to drought. We conclude that EW LD is a valuable proxy for hydroclimate reconstruction in Douglas-fir.
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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".