Elevated growth temperatures alter hydraulic characteristics in trembling aspen (<i>Populus tremuloides</i>) seedlings: implications for tree drought tolerance
Bibliographic record
Abstract
Although climate change will alter both soil water availability and evaporative demand, our understanding of how future climate conditions will alter tree hydraulic architecture is limited. Here, we demonstrate that growth at elevated temperatures (ambient +5 °C) affects hydraulic traits in seedlings of the deciduous boreal tree species Populus tremuloides, with the strength of the effect varying with the plant organ studied. Temperature altered the partitioning of hydraulic resistance, with greater resistance attributed to stems and less to roots in warm-grown seedlings (P < 0.02), and a 46% (but marginally significant, P = 0.08) increase in whole plant conductance at elevated temperature. Vulnerability to cavitation was greater in leaves grown at high than at ambient temperatures, but vulnerability in stems was similar between treatments. A soil-plant-atmosphere (SPA) model suggests that these coordinated changes in hydraulic physiology would lead to more frequent drought stress and reduced water-use efficiency in aspen that develop at warmer temperatures. Tissue-specific trade-offs in hydraulic traits in response to high growth temperatures would be difficult to detect when relying solely on whole plant measurements, but may have large-scale ecological implications for plant water use, carbon cycling and, possibly, plant drought survival.
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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".