Variation of xylem vessel diameters across a climate gradient: insight from a reciprocal transplant experiment with a widespread boreal tree
Bibliographic record
Abstract
Summary Xylem vessel diameters represent an important plant hydraulic trait to ensure sufficient water supply from the roots to the leaves. The ability to adjust the hydraulic pathway to environmental cues is key in order to satisfy transpirational demands and maximize growth and survival. We evaluated the variability of vessel diameters in trembling aspen in a reciprocal transplant experiment. We tested six provenances from three ecological regions of North America planted at four test sites in western Canada. All test sites were established at the same time with the same provenances arranged in a randomized complete block design. Vessel diameter showed a strong interaction of population and test site suggesting a high degree of phenotypic plasticity in this trait. Gaussian kernel density estimates support plastic as well as genetic contributions in vessel diameter control trending from bimodal distributions at the most northern test site towards unimodal distributions at the warmest and mildest test site. Furthermore, we used test site‐specific climate data in form of a 2‐year, 5‐year and 10‐year average of 21 directly and derived climatic variables and found that average site‐specific vessel diameters correlated strongly with summer moisture availability. A previously found negative relationship with vessel diameter and tree height in central Alberta was also found at two other boreal test sites but reversed at a wetter and milder sub‐boreal test site. In summary, vessel diameters were highly plastic in response to different environments and varied with summer moisture availability. The variability of vessel diameter and tree height correlations suggests that vessel diameter alone cannot serve as a reliable proxy for long‐term growth performance beyond boreal environments. Instead, selecting aspen populations with a high degree of plasticity in this trait appears to be the safest option for assisted migration and seed transfer programmes under climate change.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".